Abstract:
  Our study of FeSe$ _ {1-x}$Te$ _ {x}$ crystals with x $<$ 0.15 shows that the
phase separation in these compositions occurs into phases with a different
stoichiometry of iron. This phase separation may indicate structural
instability of the iron plane in the studied range of compositions. To explain
it, we discuss the bond polarity and the peculiarity of the direct $d$ exchange
in the iron plane in the framework of the basic phenomenological description
such as the Bethe-Slater curve. With this approach, when the distance between
iron atoms is close to the value at which the sign of the magnetic exchange for
some $d$ orbitals changes, structural and electronic instability can occur.
Anomalies in the crystal field near the point of charge neutrality can also be
a significant component of this instability. A similar instability of the iron
plane may also be an important factor for other series of iron-based
superconductors.


Abstract:
  We establish an explicit formula for the character of an irreducible
finite-dimensional representation of $\mathfrak{gl}(m|n)$. The formula is a
finite sum with integer coefficients in terms of a basis $\mathcal{E}_{\mu}$
(Euler characters) of the character ring. We prove a simple formula for the
behaviour of the ``superversion'' of $\mathcal{E}_{\mu}$ in the
$\mathfrak{gl}(m|n)$ and $\mathfrak{osp}(m|2n)$-case under the map $ds$ on the
supercharacter ring induced by the Duflo-Serganova cohomology functor $DS$. As
an application we get combinatorial formulas for superdimensions, dimensions
and $\mathfrak{g}_0$-decompositions for $\mathfrak{gl}(m|n)$ and
$\mathfrak{osp}(m|2n)$.


Abstract:
  A bounded container maintains a collection of elements that can be inserted
and extracted as long as the number of stored elements does not exceed the
predefined capacity. We consider the concurrent implementations of a bounded
container more or less memory-friendly depending on how much memory they use in
addition to storing the elements. This way, memory-optimal implementation
employs the minimal possible memory overhead and ensures that data and metadata
are stored most compactly, reducing cache misses and unnecessary memory
reclamation.
  In this paper, we show that non-blocking implementations of a large class of
element-independent bounded containers, including queues, stacks, and pools,
must incur linear in the number of concurrent processes memory overhead. In the
special cases when the ABA problem is excluded, e.g., by assuming that the
hardware supports LL/SC instructions or by storing distinct elements, we show
that the lower bound can be circumvented, by presenting lock-free bounded
queues with constant memory overhead. For the general case, we present a
CAS-based bounded queue implementation that asymptotically matches our lower
bound. We believe that these results open a new research avenue devoted to the
memory-optimality phenomenon.


Abstract:
  In this paper, we study the initial boundary value problem of the important
hyperbolic Kirchhoff equation
  $$u_{tt}-\left(a \int_\Omega |\nabla u|^2 \dif x +b\right)\Delta u = \lambda
u+ |u|^{p-1}u
  ,$$
  where $a$, $b>0$, $p>1$, $\lambda \in \mathbb{R}$ and the initial energy is
arbitrarily large. We prove several new theorems on the dynamics such as the
boundedness or finite time blow-up of solution under the different range of
$a$, $b$, $\lambda$ and the initial data for the following cases: (i) $1<p<3$,
(ii) $p=3$ and $a>1/\Lambda$, (iii) $p=3$, $a \leq 1/\Lambda$ and $\lam
<b\lam_1$, (iv) $p=3$, $a < 1/\Lambda$ and $\lam >b\lam_1$, (v) $p>3$ and
$\lam\leq b\lam_1$, (vi) $p>3$ and $\lam> b\lam_1$, where $\lam_1 =
\inf\left\{\|\nabla u\|^2_2 :~ u\in H^1_0(\Omega)\ {\rm and}\ \|u\|_2
=1\right\}$, and $\Lambda = \inf\left\{\|\nabla u\|^4_2 :~ u\in H^1_0(\Omega)\
{\rm and}\ \|u\|_4 =1\right\}$. Moreover, we prove the invariance of some
stable and unstable sets of the solution for suitable $a$, $b$ and $\lam$, and
give the sufficient conditions of initial data to generate a vacuum region of
the solution. Due to the nonlocal effect caused by the nonlocal
integro-differential term, we show many interesting differences between the
blow-up phenomenon of the problem for $a>0$ and $a=0$.


Abstract:
  Lensed quasars and supernovae can be used to study galaxies' gravitational
potential and measure cosmological parameters. The typical image separation of
objects lensed by galaxies is of the order of 0.5". Therefore, finding the ones
with small separations, and measuring their time-delays using ground-based
observations is challenging. We suggest a new method to identify lensed quasars
and simultaneously measure their time-delays, using seeing-limited synoptic
observations in which the lensed quasar images and the lensing galaxy are
unresolved. We show that using the light curve of the combined flux, and the
astrometric measurements of the center-of-light position of the lensed images,
the lensed nature of a quasar can be identified, and its time-delay can be
measured. We provide the analytic formalism to do so, taking into account the
measurement errors and the fact that the power spectra of quasar light curves
is red (i.e., the light curve is highly correlated). We demonstrate our method
on simulated data, while its implementation to real data will be presented in
future papers. Our simulations suggest that, under reasonable assumptions, the
new method can detect unresolved lensed quasars and measure their time delays,
even when the image separation is below 0.1", or the flux ratio between the
faintest and brightest images is as low as 0.03. Python and MATLAB
implementations are provided. In a companion paper, we present a method for
measuring the time delay using the combined flux observations. Although the
flux-only method is less powerful, it may be useful in cases in which the
astrometric information is not relevant (e.g., reverberation mapping).


Abstract:
  We propose a homotopy sampling procedure, loosely based on importance
sampling. Starting from a known probability distribution, the homotopy
procedure generates the unknown normalization of a target distribution. In the
context of stationary distributions that are associated with physical systems
the method is an alternative way to estimate an unknown microcanonical
ensemble. The process is iterative and also generates samples from the target
distribution. In practice, the homotopy procedure does not circumvent using
sample averages in the estimation of the normalization constant. The error in
the procedure depends on the errors incurred in sample averaging and the number
of stages used in the computational implementation of the process. However, we
show that it is possible to exchange the number of homotopy stages and the
total number of samples needed at each stage in order to enhance the
computational efficiency of the implemented algorithm. Estimates of the error
as a function of stages and sample averages are derived. These could guide
computational efficiency decisions on how the calculation would be mapped to a
given computer architecture. Consideration is given to how the procedure can be
adapted to Bayesian estimation problems, both stationary and non-stationary.
Emphasis is placed on the non-stationary problems, and in particular, on a
sequential estimation technique known as particle filtering. It is shown that a
modification of the particle filter framework to include the homotopy process
can improve the computational robustness of particle filters. The homotopy
process can ameliorate particle filter collapse, a common challenge to using
particle filters when the sample dimension is small compared with the state
space dimensions.


Abstract:
  In this paper, we consider an inverse conductivity problem on a bounded
domain $\Omega\subset\mathbb{R}^n$, $n\geq2$, also known as Electrical
Impedance Tomography (EIT), for the case where unknown impenetrable obstacles
are embedded into $\Omega$. We show that a piecewise-constant conductivity
function and embedded obstacles can be simultaneously recovered in terms of the
local Dirichlet-to-Neumann map defined on an arbitrary small open subset of the
boundary of the domain $\Omega$. The method depends on the well-posedness of a
coupled PDE-system constructed for the conductivity equations in the
$H^1$-space and some elementary a priori estimates for Harmonic functions.


Abstract:
  Deception detection is an important task that has been a hot research topic
due to its potential applications. It can be applied to many areas from
national security (e.g, airport security, jurisprudence, and law enforcement)
to real-life applications (e.g., business and computer vision). However, some
critical problems still exist and worth more investigation. One of the major
challenges is the data scarcity problem. Until now, only one multimodal
benchmark dataset on deception detection has been published, which contains 121
video clips for deception detection (61 for deceptive class and 60 for truthful
class). This amount of data is hard to drive deep neural network-based methods.
Hence, they often suffered from the overfitting problem and the bad
generalization ability. Also, the ground truth data contains some unusable
frames for many factors including the face is too small to be recognized the
facial expression, face is covered by text, file corruption, etc.
  However, most of the literature did not consider these problems. In this
paper, we design a series of data preprocessing methods to deal with the
problem first. Then, we propose a multimodal deception detection framework to
construct our novel emotional state-based feature and used open toolkit
openSMILE to extract the features from audio modality. A voting scheme is also
designed to combine the emotional state information obtained from both visual
modality and audio modality. Finally, the novel emotion state transformation
(EST) feature is determined by our algorithm. The critical analysis and
comparison of the proposed methods with the state-of-the-art multimodal method
are showed that the overall performance has a great improvement of accuracy
from 84.16% to 91.67% and ROC-AUC from 0.9211 to 0.9244.


Abstract:
  In solving simulation-based stochastic root-finding or optimization problems
that involve rare events, such as in extreme quantile estimation, running crude
Monte Carlo can be prohibitively inefficient. To address this issue, importance
sampling can be employed to drive down the sampling error to a desirable level.
However, selecting a good importance sampler requires knowledge of the solution
to the problem at hand, which is the goal to begin with and thus forms a
circular challenge. We investigate the use of adaptive importance sampling to
untie this circularity. Our procedure sequentially updates the importance
sampler to reach the optimal sampler and the optimal solution simultaneously,
and can be embedded in both sample average approximation and stochastic
approximation-type algorithms. Our theoretical analysis establishes strong
consistency and asymptotic normality of the resulting estimators. We also
demonstrate, via a minimax perspective, the key role of using adaptivity in
controlling asymptotic errors. Finally, we illustrate the effectiveness of our
approach via numerical experiments.


Abstract:
  The estimate of the remaining time of an ongoing wave of epidemic spreading
is a critical issue. Due to the variations of a wide range of parameters in an
epidemic, for simple models such as Susceptible-Infected-Removed (SIR) model,
it is difficult to estimate such a time scale. On the other hand,
multidimensional data with a large set attributes are precisely what one can
use in statistical learning algorithms to make predictions. Here we show, how
the predictability of the SIR model changes with various parameters using a
supervised learning algorithm. We then estimate the condition in which the
model gives the least error in predicting the duration of the first wave of the
COVID-19 pandemic in different states in India. Finally, we use the SIR model
with the above mentioned optimal conditions to generate a training data set and
use it in the supervised learning algorithm to estimate the end-time of the
ongoing second wave of the pandemic in different states in India.


Abstract:
  To better understand the potential of drug repurposing in COVID-19, we
analyzed control strategies over essential host factors for SARS-CoV-2
infection. We constructed comprehensive directed protein-protein interaction
networks integrating the top ranked host factors, drug target proteins, and
directed protein-protein interaction data. We analyzed the networks to identify
drug targets and combinations thereof that offer efficient control over the
host factors. We validated our findings against clinical studies data and
bioinformatics studies. Our method offers a new insight into the molecular
details of the disease and into potentially new therapy targets for it. Our
approach for drug repurposing is significant beyond COVID-19 and may be applied
also to other diseases.


Abstract:
  Laser-induced terahertz spin transport (TST) and ultrafast demagnetization
(UDM) are central but so far disconnected phenomena in femtomagnetism and
terahertz spintronics. Here, we use broadband terahertz emission spectroscopy
to reliably measure both processes in one setup. We find that the rate of UDM
of a single ferromagnetic metal film F has the same time evolution as the flux
of TST from F into an adjacent normal-metal layer N. This remarkable agreement
shows that UDM and TST are driven by the same force, which is fully determined
by the state of the ferromagnet. An analytical model consistently and
quantitatively explains our observations. It reveals that both UDM in F and TST
in the F|N stack arise from a generalized spin voltage, which is defined for
arbitrary, nonthermal electron distributions. We also conclude that
contributions due to a possible temperature difference between F and N are
minor and that the spin-current amplitude can, in principle, be increased by
one order of magnitude. In general, our findings allow one to apply the vast
knowledge of UDM to TST, thereby opening up new pathways toward large-amplitude
terahertz spin currents and, thus, energy-efficient ultrafast spintronic
devices.


Abstract:
  We further the theme of studying syntactic structures data from Longobardi
(2017b), Collins (2010), Ceolin et al. (2020) and Koopman (2011) using general
Markov models initiated in Shu et al. (2017), exploring the question of how
consistent the data is with the idea that general Markov models. The ideas
explored in the present paper are more generally applicable than to the setting
of syntactic structures, and can be used when analyzing consistency of data
with general Markov models. Additionally, we give an interpretation of the
methods of Ceolin et al. (2020) as an infinite sites evolutionary model and
compare it to the Markov model and explore each in the context of evolutionary
processes acting on human language syntax.


Abstract:
  We consider the inverse problem of recovering the probability distribution
function of $T_2$ relaxation times from NMR transverse relaxometry experiments.
This problem is a variant of the inverse Laplace transform and hence ill-posed.
We cast this within the framework of a Gaussian mixture model to obtain a
least-square problem with an $L_2$ regularization term. We propose a new method
for incorporating regularization into the solution; rather than seeking to
replace the native problem with a suitable mathematically close, regularized,
version, we instead augment the native formulation with regularization. We term
this new approach 'multi-regularization'; it avoids the treacherous process of
selecting a single best regularization parameter $\lambda$ and instead permits
incorporation of several degrees of regularization into the solution. We
illustrate the method with extensive simulation results as well as application
to real experimental data.


Abstract:
  This paper presents a classification of 7-dimensional real and complex
indecomposable solvable Lie algebras having some 5-dimensional nilradicals.
Afterwards, we combine our results with those of Rubin and Winternitz (1993),
Ndogmo and Winternitz (1994), Snobl and Winternitz (2005, 2009), Snobl and
Kar\'asek (2010) to obtain a complete classification of 7-dimensional real and
complex indecomposable solvable Lie algebras with 5-dimensional nilradicals. In
association with Gong (1998), Parry (2007), Hindeleh and Thompson (2008), we
achieve a classification of 7-dimensional real and complex indecomposable
solvable Lie algebras.


Abstract:
  Let $\Omega$ be a subdomain of $\mathbb{C}$ and let $\mu$ be a positive Borel
measure on $\Omega$. In this paper, we study the asymptotic behavior of the
eigenvalues of compact Toeplitz operator $T_\mu$ acting on Bergman spaces on
$\Omega$. Let $(\lambda_n(T_\mu))$ be the decreasing sequence of the
eigenvalues of $T_\mu$ and let $\rho$ be an increasing function such that $\rho
(n)/n^A$ is decreasing for some $A>0$. We give an explicit necessary and
sufficient geometric condition on $\mu$ in order to have
$\lambda_n(T_\mu)\asymp 1/\rho (n)$. As applications, we consider composition
operators $C_\varphi$, acting on some standard analytic spaces on the unit disc
$\mathbb{D}$. First, we give a general criterion ensuring that the singular
values of $C_\varphi$ satisfy $s_n(C_\varphi ) \asymp 1/\rho(n)$. Next, we
focus our attention on composition operators with univalent symbols, where we
express our general criterion in terms of the harmonic measure of $\varphi
\mathbb{D})$. We finally study the case where $\partial \varphi (\mathbb{D})$
meets the unit circle in one point and give several concrete examples. Our
method is based on upper and lower estimates of the trace of $h(T_\mu)$, where
$h$ is suitable concave or convex functions.


Abstract:
  Recently, considerable literature has grown up around the theme of few-shot
named entity recognition (NER), but little published benchmark data
specifically focused on the practical and challenging task. Current approaches
collect existing supervised NER datasets and re-organize them to the few-shot
setting for empirical study. These strategies conventionally aim to recognize
coarse-grained entity types with few examples, while in practice, most unseen
entity types are fine-grained. In this paper, we present Few-NERD, a
large-scale human-annotated few-shot NER dataset with a hierarchy of 8
coarse-grained and 66 fine-grained entity types. Few-NERD consists of 188,238
sentences from Wikipedia, 4,601,160 words are included and each is annotated as
context or a part of a two-level entity type. To the best of our knowledge,
this is the first few-shot NER dataset and the largest human-crafted NER
dataset. We construct benchmark tasks with different emphases to
comprehensively assess the generalization capability of models. Extensive
empirical results and analysis show that Few-NERD is challenging and the
problem requires further research. We make Few-NERD public at
https://ningding97.github.io/fewnerd/.


Abstract:
  The process of heating and reionization of the Universe at high redshift
links small scale structures/galaxy formation and large scale inter-galactic
medium properties. Even if the first one is difficult to observe, an
observation window is opening on the second one, with the promising development
of current and future radio telescopes. They will permit to observe the 21cm
brightness temperature global signal and fluctuations. The need of large scale
simulations is therefore strong to understand the properties of the IGM that
will be observed. But at the same time the urge to resolve the structures
responsible of those process is important. We introduce in this study, a
coupled hydro-radiative transfer simulations of the Cosmic Dawn and
Reionization with a simple sub-grid star formation process developed and
calibrated on the state of the art simulation CoDaII. This scheme permits to
follow consistently dark matter, hydrodynamics and radiative transfer
evolution's on large scales, while the sub-grid models bridges to the galaxy
formation scale. We process the simulation to produce 21cm signal as close as
possible to the observations.


Abstract:
  We examine the spectral stability and instability of periodic traveling waves
for regularized long-wave models. Examples include the regularized Boussinesq,
Benney--Luke, and Benjamin--Bona--Mahony equations. Of particular interest is a
striking new instability phenomenon -- spectrum off the imaginary axis
extending into infinity. The spectrum of the linearized operator of the
generalized Korteweg--de Vries equation, for instance, lies along the imaginary
axis outside a bounded set. The spectrum for a regularized long-wave model, by
contrast, can vary markedly with the parameters of the periodic traveling
waves. We carry out asymptotic spectral analysis to short wavelength
perturbations, distinguishing whether the spectrum tends to infinity along the
imaginary axis or some curve whose real part is nonzero. We conduct numerical
experiments to corroborate our analytical findings.


Abstract:
  Unmanned aerial vehicles (UAVs) can be integrated into wireless sensor
networks (WSNs) for smart city applications in several ways. Among them, a UAV
can be employed as a relay in a "store-carry and forward" fashion by uploading
data from ground sensors and metering devices and, then, downloading it to a
central unit. However, both the uploading and downloading phases can be prone
to potential threats and attacks. As a legacy from traditional wireless
networks, the jamming attack is still one of the major and serious threats to
UAV-aided communications, especially when also the jammer is mobile, e.g., it
is mounted on a UAV or inside a terrestrial vehicle. In this paper, we
investigate anti-jamming communications for UAV-aided WSNs operating over
doubly-selective channels in the downloading phase. In such a scenario, the
signals transmitted by the UAV and the malicious mobile jammer undergo both
time dispersion due to multipath propagation effects and frequency dispersion
caused by their mobility. To suppress high-power jamming signals, we propose a
blind physical-layer technique that jointly detects the UAV and jammer symbols
through serial disturbance cancellation based on symbol-level post-sorting of
the detector output. Amplitudes, phases, time delays, and Doppler shifts -
required to implement the proposed detection strategy - are blindly estimated
from data through the use of algorithms that exploit the
almost-cyclostationarity properties of the received signal and the detailed
structure of multicarrier modulation format. Simulation results corroborate the
anti-jamming capabilities of the proposed method, for different mobility
scenarios of the jammer.


Abstract:
  It is known that unsupervised nonlinear dimensionality reduction and
clustering is sensitive to the selection of hyperparameters, particularly for
deep learning based methods, which hinder its practical use. How to select a
proper network structure that may be dramatically different in different
applications is a hard issue for deep models, given little prior knowledge of
data. In this paper, we explore ensemble learning and selection techniques for
automatically determining the optimal network structure of a deep model, named
multilayer bootstrap networks (MBN). Specifically, we first propose an MBN
ensemble (MBN-E) algorithm which concatenates the sparse outputs of a set of
MBN base models with different network structures into a new representation.
Because training an ensemble of MBN is expensive, we propose a fast version of
MBN-E (fMBN-E), which replaces the step of random data resampling in MBN-E by
the random resampling of similarity scores. Theoretically, fMBN-E is even
faster than a single standard MBN. Then, we take the new representation
produced by MBN-E as a reference for selecting the optimal MBN base models. Two
kinds of ensemble selection criteria, named optimization-like selection
criteria and distribution divergence criteria, are applied. Importantly, MBN-E
and its ensemble selection techniques maintain the simple formulation of MBN
that is based on one-nearest-neighbor learning, and reach the state-of-the-art
performance without manual hyperparameter tuning. fMBN-E is empirically even
hundreds of times faster than MBN-E without suffering performance degradation.
The source code is available at http://www.xiaolei-zhang.net/mbn-e.htm.


Abstract:
  Inspired by the notion of K\"onig graphs we introduce graded ideals of
K\"onig type with respect to a monomial order $<$. It is shown that if $I$ is
of K\"onig type, then the Cohen--Macaulay property of $\ini_<(I)$ does not
depend on the characteristic of the base field. This happens to be the case
also for $I$ itself when $I$ is a binomial edge ideal. Attached to an ideal of
K\"onig type is a sequence of linear forms, whose elements are variables or
differences of variables. This sequence is a system of parameters for
$\ini_<(I)$, and is a potential system of parameters for $I$ itself. We study
in detail the ideals of K\"onig type among the edge ideals, binomial edge
ideals and the toric ideal of a Hibi ring and use the K\"onig property to
determine explicitly their canonical module.


Abstract:
  We present a comprehensive study of the linear response of interacting
underdamped Brownian particles to simple shear flow. We collect six different
routes for computing the response, two of which are based on the symmetry of
the considered system and observable with respect to the shear axes. We include
the extension of the Green-Kubo relation to underdamped cases, which shows two
unexpected additional terms. These six computational methods are applied to
investigate the relaxation of the response towards the steady state for
different observables, where interesting effects due to interactions and a
finite particle mass are observed. Moreover, we compare the different response
relations in terms of their statistical efficiency, identifying their relative
demand on experimental measurement time or computational resources in computer
simulations. Finally, several measures of breakdown of linear response theory
for larger shear rates are discussed.


Abstract:
  A plane non-parallel vortex flow in a square fluid domain is examined. The
energy dissipation of the flow is dominated by viscosity and linear friction
effect of a Hartmann layer. This is a traditional Navier-Stokes flow when the
linear friction effect is not involved, whereas it is a magnetohydrodynamic
flow when the energy dissipation is fundamentally dominated by the friction. It
is proved that linear critical values of a spectral problem are nonlinear
thresholds leading to the onset of secondary steady-state flows, the nonlinear
phenomenon observed in laboratory experiments.


Abstract:
  We prove a number of new results on the large-scale geometry of the
$L^p$-metrics on the group of area-preserving diffeomorphisms of each
orientable surface. Our proofs use in a key way the Fulton-MacPherson type
compactification of the configuration space of $n$ points on the surface due to
Axelrod-Singer and Kontsevich. This allows us to apply the Schwarz-Milnor lemma
to configuration spaces, a natural approach which we carry out successfully for
the first time. As sample results, we prove that all right-angled Artin groups
admit quasi-isometric embeddings into the group of area-preserving
diffeomorphisms endowed with the $L^p$-metric, and that all Gambaudo-Ghys
quasi-morphisms on this metric group coming from the braid group on $n$ strands
are Lipschitz. This was conjectured to hold, yet proven only for low values of
$n$ and the genus $g$ of the surface.


Abstract:
  Reinforcement learning algorithms, just like any other Machine learning
algorithm pose a serious threat from adversaries. The adversaries can
manipulate the learning algorithm resulting in non-optimal policies. In this
paper, we analyze the Multi-task Federated Reinforcement Learning algorithms,
where multiple collaborative agents in various environments are trying to
maximize the sum of discounted return, in the presence of adversarial agents.
We argue that the common attack methods are not guaranteed to carry out a
successful attack on Multi-task Federated Reinforcement Learning and propose an
adaptive attack method with better attack performance. Furthermore, we modify
the conventional federated reinforcement learning algorithm to address the
issue of adversaries that works equally well with and without the adversaries.
Experimentation on different small to mid-size reinforcement learning problems
show that the proposed attack method outperforms other general attack methods
and the proposed modification to federated reinforcement learning algorithm was
able to achieve near-optimal policies in the presence of adversarial agents.


Abstract:
  Heteroscedastic regression is the task of supervised learning where each
label is subject to noise from a different distribution. This noise can be
caused by the labelling process, and impacts negatively the performance of the
learning algorithm as it violates the i.i.d. assumptions. In many situations
however, the labelling process is able to estimate the variance of such
distribution for each label, which can be used as an additional information to
mitigate this impact. We adapt an inverse-variance weighted mean square error,
based on the Gauss-Markov theorem, for parameter optimization on neural
networks. We introduce Batch Inverse-Variance, a loss function which is robust
to near-ground truth samples, and allows to control the effective learning
rate. Our experimental results show that BIV improves significantly the
performance of the networks on two noisy datasets, compared to L2 loss,
inverse-variance weighting, as well as a filtering-based baseline.


Abstract:
  In-situ measurements by several spacecraft have revealed that the solar wind
is frequently perturbed by transient structures (magnetic folds, jets, waves,
flux-ropes) that propagate rapidly away from the Sun over large distances.
Parker Solar Probe has detected frequent rotations of the magnetic field vector
at small heliocentric distances, accompanied by surprisingly large solar wind
rotation rates. The physical origin of such magnetic field bends, the
conditions for their survival across the interplanetary space, and their
relation to solar wind rotation are yet to be clearly understood. We traced
measured solar wind flows from the spacecraft position down to the surface of
the Sun to identify their potential source regions and used a global MHD model
of the corona and solar wind to relate them to the rotational state of the low
solar corona. We identified regions of the solar corona for which solar wind
speed and rotational shear are important and long-lived, that can be favourable
to the development of magnetic deflections and to their propagation across
extended heights in the solar wind. We show that coronal rotation is highly
structured and that enhanced flow shear develops near the boundaries between
coronal holes and streamers, around and above pseudo-streamers, even when such
boundaries are aligned with the direction of solar rotation. A large fraction
of the switchbacks identified by PSP map back to these regions, both in terms
of instantaneous magnetic field connectivity and of the trajectories of wind
streams that reach the spacecraft. These regions of strong shears are likely to
leave an imprint on the solar wind over large distances and to increase the
transverse speed variability in the slow solar wind. The simulations and
connectivity analysis suggest they can be a source of the switchbacks and
spikes observed by Parker Solar Probe.


Abstract:
  For a class $\mathcal{G}$ of graphs, the problem SUBGRAPH COMPLEMENT TO
$\mathcal{G}$ asks whether one can find a subset $S$ of vertices of the input
graph $G$ such that complementing the subgraph induced by $S$ in $G$ results in
a graph in $\mathcal{G}$. We investigate the complexity of the problem when
$\mathcal{G}$ is $H$-free for $H$ being a complete graph, a star, a path, or a
cycle. We obtain the following results:
  - When $H$ is a $K_t$ (a complete graph on $t$ vertices) for any fixed $t\geq
1$, the problem is solvable in polynomial-time. This applies even when
$\mathcal{G}$ is a subclass of $K_t$-free graphs recognizable in
polynomial-time, for example, the class of $(t-2)$-degenerate graphs.
  - When $H$ is a $K_{1,t}$ (a star graph on $t+1$ vertices), we obtain that
the problem is NP-complete for every $t\geq 5$. This, along with known results,
leaves only two unresolved cases - $K_{1,3}$ and $K_{1,4}$.
  - When $H$ is a $P_t$ (a path on $t$ vertices), we obtain that the problem is
NP-complete for every $t\geq 7$, leaving behind only two unresolved cases -
$P_5$ and $P_6$.
  - When $H$ is a $C_t$ (a cycle on $t$ vertices), we obtain that the problem
is NP-complete for every $t\geq 8$, leaving behind four unresolved cases -
$C_4, C_5, C_6,$ and $C_7$.
  Further, we prove that these hard problems do not admit subexponential-time
algorithms (algorithms running in time $2^{o(|V(G)|)}$), assuming the
Exponential Time Hypothesis. A simple complementation argument implies that
results for $\mathcal{G}$ are applicable for $\overline{\mathcal{G}}$, thereby
obtaining similar results for $H$ being the complement of a complete graph, a
star, a path, or a cycle. Our results generalize two main results and resolve
one open question by Fomin et al. (Algorithmica, 2020).


Abstract:
  The concept of biological adaptation was closely connected to some
mathematical, engineering and physical ideas from the very beginning. Cannon in
his "The wisdom of the body" (1932) used the engineering vision of regulation.
In 1938, Selye enriched this approach by the notion of adaptation energy. This
term causes much debate when one takes it literally, i.e. as a sort of energy.
Selye did not use the language of mathematics, but the formalization of his
phenomenological theory in the spirit of thermodynamics was simple and led to
verifiable predictions. In 1980s, the dynamics of correlation and variance in
systems under adaptation to a load of environmental factors were studied and
the universal effect in ensembles of systems under a load of similar factors
was discovered: in a crisis, as a rule, even before the onset of obvious
symptoms of stress, the correlation increases together with variance (and
volatility). During 30 years, this effect has been supported by many
observations of groups of humans, mice, trees, grassy plants, and on financial
time series. In the last ten years, these results were supplemented by many new
experiments, from gene networks in cardiology and oncology to dynamics of
depression and clinical psychotherapy. Several systems of models were
developed: the thermodynamic-like theory of adaptation of ensembles and several
families of models of individual adaptation. Historically, the first group of
models was based on Selye's concept of adaptation energy and used fitness
estimates. Two other groups of models are based on the idea of hidden attractor
bifurcation and on the advection--diffusion model for distribution of
population in the space of physiological attributes. We explore this world of
models and experiments, starting with classic works, with particular attention
to the results of the last ten years and open questions.


Abstract:
  Several new improvements of the $A$-numerical radius inequalities for
operators acting on a semi-Hilbert space, i.e., a space generated by a positive
operator $A$, are proved. In particular, among other inequalities, we show that
\begin{align*} \frac{1}{4}\|T^{\sharp_A} T+TT^{\sharp_A}\|_A
\leq\frac{1}{4}\Big(2\omega_A^2(T)+\gamma(T)\Big) \leq \omega_A^2(T),
\end{align*} where
$$\gamma(T)=\sqrt{\left(\|\Re_A(T)\|_A^2-\|\Im_A(T)\|_A^2\right)^2+4\|\Re_A(T)\Im_A(T)\|_A^2}.$$
Here $\omega_A(X)$ and $\|X\|_A$ denote respectively the $A$-numerical radius
and the $A$-seminorm of an operator $X$. Also,
$\Re_A(T):=\frac{T+T^{\sharp_A}}{2}$ and $\Im_A(T):=\frac{T-T^{\sharp_A}}{2i}$,
where $T^{\sharp_A}$ is a distinguished $A$-adjoint operator of $T$. Further,
some new refinements of the triangle inequality related to $\|\cdot\|_A$ are
established.


Abstract:
  We consider the problem of learning a nonlinear function over a network of
learners in a fully decentralized fashion. Online learning is additionally
assumed, where every learner receives continuous streaming data locally. This
learning model is called a fully distributed online learning (or a fully
decentralized online federated learning). For this model, we propose a novel
learning framework with multiple kernels, which is named DOMKL. The proposed
DOMKL is devised by harnessing the principles of an online alternating
direction method of multipliers and a distributed Hedge algorithm. We
theoretically prove that DOMKL over T time slots can achieve an optimal
sublinear regret, implying that every learner in the network can learn a common
function which has a diminishing gap from the best function in hindsight. Our
analysis also reveals that DOMKL yields the same asymptotic performance of the
state-of-the-art centralized approach while keeping local data at edge
learners. Via numerical tests with real datasets, we demonstrate the
effectiveness of the proposed DOMKL on various online regression and
time-series prediction tasks.


Abstract:
  Scattered factor (circular) universality was firstly introduced by Barker et
al. in 2020. A word $w$ is called $k$-universal for some natural number $k$, if
every word of length $k$ of $w$'s alphabet occurs as a scattered factor in $w$;
it is called circular $k$-universal if a conjugate of $w$ is $k$-universal.
Here, a word $u=u_1\cdots u_n$ is called a scattered factor of $w$ if $u$ is
obtained from $w$ by deleting parts of $w$, i.e. there exists (possibly empty)
words $v_1,\dots,v_{n+1}$ with $w=v_1u_1v_2\cdots v_nu_nv_{n+1}$. In this work,
we prove two problems, left open in the aforementioned paper, namely a
generalisation of one of their main theorems to arbitrary alphabets and a
slight modification of another theorem such that we characterise the circular
universality by the universality. On the way, we present deep insights into the
behaviour of the remainder of the so called arch factorisation by Hebrard when
repetitions of words are considered.


Abstract:
  A theoretical investigation has been carried out to examine the ion-acoustic
shock waves (IASHWs) in a magnetized degenerate quantum plasma system
containing inertialess ultra-relativistically degenerate electrons, and
inertial non-relativistic positively charged heavy and light ions. The Burgers'
equation is derived by employing reductive perturbation method. It can be seen
that under consideration of non-relativistic positively charged heavy and light
ions, the plasma model supports only positive electrostatic shock structure. It
is also observed that the charge state and number density of the
non-relativistic heavy and light ions enhance the amplitude of IASHWs, and the
steepness of the shock profile is decreased with ion kinematic viscosity
($\eta$). The findings of our present investigation will be helpful in
understanding the nonlinear propagation of IASHWs in white dwarfs and neutron
stars.


Abstract:
  We experimentally demonstrate that the next-nearest-neighbor(NNN)coupling in
an array of waveguides can naturally be negative. To do so, dielectric zig-zag
shaped waveguide arrays are fabricated with direct laser writing (DLW). By
changing the angle of the zig-zag shape it is possible to tune between positive
and negative ratios of nearest and next-nearest-neighbor coupling, which also
allows to reduce the impact of the NNN-coupling to zero at the correct
respective angle. We describe how the correct higher order coupling constants
in tight-binding models can be derived, based on non-orthogonal coupled mode
theory. We confirm the existence of negative NNN-couplings experimentally and
show the improved accuracy of this refined tight-binding model. The negative
NNN-coupling has a noticeable impact especially when higher order coupling
terms can no longer be neglected. Our results are also of importance for other
discrete systems in which the tight-binding model is often used.


Abstract:
  We study by numerical simulation in two and three dimensions the coupling
layer between the transducer and the microfluidic chip in ultrasound
acoustofluidic devices. The model includes the transducer with electrodes, the
microfluidic chip with a liquid-filled microchannel, and the coupling layer
between the transducer and the chip. We consider two commonly used coupling
materials, solid epoxy glue and viscous glycerol, as well as two commonly used
device types, glass capillary tubes and silicon-glass chips. We study how
acoustic resonances in ideal devices without a coupling layer is either
sustained or attenuated as a coupling layer of increasing thickness is
inserted. We establish a simple criterion based on the phase of the acoustic
wave for whether a given zero-layer resonance is sustained or attenuated by the
addition of a coupling layer. Finally, we show that by controlling the
thickness and the material, the coupling layer can be used as a design
component for optimal and robust acoustofluidic resonances.


Abstract:
  We study text representation methods using deep models. Current methods, such
as word-level embedding and character-level embedding schemes, treat texts as
either a sequence of atomic words or a sequence of characters. These methods
either ignore word morphologies or word boundaries. To overcome these
limitations, we propose to convert texts into 2-D representations and develop
the Sent2Matrix method. Our method allows for the explicit incorporation of
both word morphologies and boundaries. When coupled with a novel serpentine
padding method, our Sent2Matrix method leads to an interesting visualization in
which 1-D character sequences are folded into 2-D serpentine manifolds.
Notably, our method is the first attempt to represent texts in 2-D formats.
Experimental results on text classification tasks shown that our method
consistently outperforms prior embedding methods.


Abstract:
  In a recent measurement LHCb reported pronounced structures in the $J/\psi
J/\psi$ spectrum. One of the various possible explanations of those is that
they emerge from non-perturbative interactions of vector charmonia. It is thus
important to understand whether it is possible to form a bound state of two
charmonia interacting through the exchange of gluons, which hadronise into two
pions at the longest distance. In this paper, we demonstrate that, given our
current understanding of hadron-hadron interactions, the exchange of correlated
light mesons (pions and kaons) is able to provide sizeable attraction to the
di-$J/\psi$ system, and it is possible for two $J/\psi$ mesons to form a bound
state. As a side result we find from an analysis of the data for the
$\psi(2S)\to J/\psi \pi\pi$ transition including both $\pi\pi$ and $K\bar K$
final state interactions an improved value for the $\psi(2S)\to J/\psi$
transition chromo-electric polarisability: $|\alpha_{\psi(2S)J/\psi}|= (1.8\pm
0.1)~\mbox{GeV}^{-3}$, where the uncertainty also includes the one induced by
the final state interactions.


Abstract:
  We study the decoherence effect of charge noise on a single flip-flop qubit
and two dipole-coupled qubits. We find that a single flip-flop qubit is highly
resistant to charge noise at its sweet spots. However, due to the proximity of
the charge excited states to the flip-flop logical states, the presence of
charge noise greatly reduces the fidelity of two-qubit operations. We identify
leakage from the qubit Hilbert space as the main culprit for the reduced gate
fidelity. We also explore different bias conditions to mitigate this
decoherence channel.


Abstract:
  We define a new family of spectral invariants associated to certain
Lagrangian links in compact and connected surfaces of any genus. We show that
our invariants recover the Calabi invariant of Hamiltonians in their limit. As
applications, we resolve several open questions from topological surface
dynamics and continuous symplectic topology: we show that the group of
Hamiltonian homeomorphisms of any compact surface with (possibly empty)
boundary is not simple; we extend the Calabi homomorphism to the group of
Hameomorphisms constructed by Oh-M\"uller; and, we construct an infinite
dimensional family of quasimorphisms on the group of area and orientation
preserving homeomorphisms of the two-sphere. Our invariants are inspired by
recent work of Polterovich and Shelukhin defining and applying spectral
invariants for certain classes of links in the two-sphere.


Abstract:
  Model selection, via penalized likelihood type criteria, is a standard task
in many statistical inference and machine learning problems. Progress has led
to deriving criteria with asymptotic consistency results and an increasing
emphasis on introducing non-asymptotic criteria. We focus on the problem of
modeling non-linear relationships in regression data with potential hidden
graph-structured interactions between the high-dimensional predictors, within
the mixture of experts modeling framework. In order to deal with such a complex
situation, we investigate a block-diagonal localized mixture of polynomial
experts (BLoMPE) regression model, which is constructed upon an inverse
regression and block-diagonal structures of the Gaussian expert covariance
matrices. We introduce a penalized maximum likelihood selection criterion to
estimate the unknown conditional density of the regression model. This model
selection criterion allows us to handle the challenging problem of inferring
the number of mixture components, the degree of polynomial mean functions, and
the hidden block-diagonal structures of the covariance matrices, which reduces
the number of parameters to be estimated and leads to a trade-off between
complexity and sparsity in the model. In particular, we provide a strong
theoretical guarantee: a finite-sample oracle inequality satisfied by the
penalized maximum likelihood estimator with a Jensen-Kullback-Leibler type
loss, to support the introduced non-asymptotic model selection criterion. The
penalty shape of this criterion depends on the complexity of the considered
random subcollection of BLoMPE models, including the relevant graph structures,
the degree of polynomial mean functions, and the number of mixture components.


Abstract:
  A continuous monitoring of the physical strength and mobility of elderly
people is important for maintaining their health and treating diseases at an
early stage. However, frequent screenings by physicians are exceeding the
logistic capacities. An alternate approach is the automatic and unobtrusive
collection of functional measures by ambient sensors. In the current
publication, we show the correlation among data of ambient motion sensors and
the well-established mobility assessments Short-Physical-Performance-Battery,
Tinetti and Timed Up & Go. We use the average number of motion sensor events as
activity measure for correlation with the assessment scores. The evaluation on
a real-world dataset shows a moderate to strong correlation with the scores of
standardised geriatrics physical assessments.


Abstract:
  We study epidemic models where the infectivity of each individual is a random
function of the infection age (the elapsed time of infection). To describe the
epidemic evolution dynamics, we use a stochastic process that tracks the number
of individuals at each time that have been infected for less than or equal to a
certain amount of time, together with the aggregate infectivity process. We
establish the functional law of large numbers (FLLN) for the stochastic
processes that describe the epidemic dynamics. The limits are described by a
set of deterministic integral equations, which has a further characterization
using PDEs under some regularity conditions. The solutions are characterized
with boundary conditions that are given by a system of Volterra equations. We
also characterize the equilibrium points for the PDEs in the SIS model with
infection-age dependent infectivity. To establish the FLLNs, we employ a useful
criterion for weak convergence for the two-parameter processes together with
useful representations for the relevant processes via Poisson random measures.


Abstract:
  We explicit some general properties regarding surfaces with Prym-canonical
hyperplane sections and the geometric genus of their possible singularities.
Moreover, we construct new examples of this type of surfaces.


Abstract:
  To reduce uploading bandwidth and address privacy concerns, deep learning at
the network edge has been an emerging topic. Typically, edge devices
collaboratively train a shared model using real-time generated data through the
Parameter Server framework. Although all the edge devices can share the
computing workloads, the distributed training processes over edge networks are
still time-consuming due to the parameters and gradients transmission
procedures between parameter servers and edge devices. Focusing on accelerating
distributed Convolutional Neural Networks (CNNs) training at the network edge,
we present DynaComm, a novel scheduler that dynamically decomposes each
transmission procedure into several segments to achieve optimal layer-wise
communications and computations overlapping during run-time. Through
experiments, we verify that DynaComm manages to achieve optimal layer-wise
scheduling for all cases compared to competing strategies while the model
accuracy remains untouched.


Abstract:
  We present a detailed 3D kinematic analysis of the central regions ($R<30''$)
of the low-mass and dynamically evolved galactic globular cluster NGC 6362. The
study is based on data obtained with ESO-VLT/MUSE used in combination with the
adaptive optics module and providing $\sim3000$ line-of-sight radial
velocities, which have been complemented with Hubble Space Telescope proper
motions. The quality of the data and the number of available radial velocities
allowed us to detect for the first time a significant rotation signal along the
line of sight in the cluster core with amplitude of $\sim 1$ km/s and with a
peak located at only $\sim20''$ from the cluster center, corresponding to only
$\sim10\%$ of the cluster half-light radius. This result is further supported
by the detection of a central and significant tangential anisotropy in the
cluster innermost regions. This is one of the most central rotation signals
ever observed in a globular cluster to date. We also explore the rotational
properties of the multiple populations hosted by this cluster and find that
Na-rich stars rotate about two times more rapidly than the Na-poor
sub-population thus suggesting that the interpretation of the present-day
globular cluster properties require a multi-component chemo-dynamical approach.
Both the rotation amplitude and peak position would fit qualitatively the
theoretical expectations for a system that lost a significant fraction of its
original mass because of the long-term dynamical evolution and interaction with
the Galaxy. However, to match the observations more quantitatively further
theoretical studies to explore the initial dynamical properties of the cluster
are needed.


Abstract:
  Covalent organic frameworks (COFs) have attracted significant attention due
to their chemical versatility combined with a significant number of potential
applications. Of particular interest are two-dimensional COFs, where the
organic building units are linked by covalent bonds within a plane. Most
properties of these COFs are determined by the relative arrangement of
neighboring layers. These are typically found to be laterally displaced, which,
for example, reduces the electronic coupling between the layers. In the present
contribution, we use dispersion-corrected density-functional theory to
elucidate the origin of that displacement, showing that the common notion that
the displacement is a consequence of electrostatic repulsions of polar building
blocks can be misleading. For the representative case of COF-1, we find that
electrostatic and van der Waals interactions would, actually, favor a cofacial
arrangement of the layers and that Pauli repulsion is the crucial factor
causing the serrated AA stacking. A more in-depth analysis of the electrostatic
contribution reveals that the "classical" Coulomb repulsion between the
boroxine building blocks of COF-1 suggested by chemical intuition does exist,
but is overcompensated by attractive effects due to charge-penetration in the
phenylene units. The situation becomes more involved when additionally allowing
the interlayer distance to relax for each displacement, as then the different
distance-dependences of the various types of interactions come into play. The
overall behavior calculated for COF-1 is recovered for several additional COFs
with differently sized pi-systems and topologies, implying that the presented
results are of more general relevance.


Abstract:
  Two key challenges within Reinforcement Learning involve improving (a) agent
learning within environments with sparse extrinsic rewards and (b) the
explainability of agent actions. We describe a curious subgoal focused agent to
address both these challenges. We use a novel method for curiosity produced
from a Generative Adversarial Network (GAN) based model of environment
transitions that is robust to stochastic environment transitions. Additionally,
we use a subgoal generating network to guide navigation. The explainability of
the agent's behavior is increased by decomposing complex tasks into a sequence
of interpretable subgoals that do not require any manual design. We show that
this method also enables the agent to solve challenging procedurally-generated
tasks that contain stochastic transitions above other state-of-the-art methods.


Abstract:
  Telerehabilitation systems for stroke survivors have been predominantly
designed to measure and quantify movement in order to guide and encourage
rehabilitation regular exercises at home. We set out to study what aspect of
the movement data was essential, to better inform sensor design. We
investigated face-to-face stroke rehabilitation sessions through a series of
interviews and observations involving 16 stroke rehabilitation specialists
including physiatrists, physical therapists, and occupational therapists. We
found that specialists are not solely interested in movement data, and that
experiential information about stroke survivors' lived experience plays an
essential role in specialists interpreting movement data and creating a
rehabilitation plan. We argue for a reconceptualization in stroke
telerehabilitation that is more inclusive of non-movement data, and present
design implications to better account for experiential information in
telerehabilitation systems.


Abstract:
  Motivated by entropic optimal transport, time reversal of Markov jump
processes in $\mathbb{R}^n$ is investigated. Relying on an abstract integration
by parts formula for the carr\'e du champ of a Markov process recently obtained
by Cattiaux, Gentil and the authors, and using an entropic improvement strategy
discovered by F\"ollmer in the eighties, we compute the semimartingale
characteristics of the time reversed process for a wide class of jump processes
with possibly unbounded variation sample paths and singular intensities of
jump.


Abstract:
  Simultaneous X-ray and optical observations of black hole X-ray binaries have
shown that the light curves contain multiple correlated and anti-correlated
variation components when the objects are in the hard state. In the case of the
black hole X-ray binary, GX 339-4, the cross correlation function (CCF) of the
light curves suggests a positive correlation with an optical lag of 0.15 s and
anti-correlations with an optical lag of 1 s and X-ray lag of 4 s. This
indicates the two light curves have some common signal components with
different delays. In this study, we extracted and reconstructed those signal
components from the data for GX 339-4. The results confirmed that correlation
and anti-correlation with the optical lag are two common components. However,
we found that the reconstructed light curve for the anti-correlated component
indicates a positively correlated variation with an X-ray lag of ~ +1 s. In
addition, the CCF for this signal component shows anti-correlations not only
with the optical lag, but also with the X-ray lag, which is consistent with the
CCF for the data. Therefore, our results suggest that the combination of the
two positively correlated components, that is, the X-ray preceding signal with
the 0.15-s optical lag and the optical preceding signal with the 1-s X-ray lag,
can make the observed CCF without anti-correlated signals. The optical
preceding signal may be caused by synchrotron emission in a magnetically
dominated accretion flow or in a jet, while further study is required to
understand the mechanism of the X-ray time lag.


Abstract:
  The Cohen-Kaplan-Nelson (CKN) bound formulates the condition that black hole
is not produced by the low energy effective field theory dynamics. In de Sitter
space it also constrains the maximal size of the matter distribution to be
smaller than the cosmological horizon determined by black hole. On the other
hand, the swampland distance conjecture (SDC) predicts that de Sitter space
becomes unstable by the descent of the low energy degrees of freedom from UV.
This results in the rapid increase in the energy inside the cosmological
horizon, the distribution of which can be constrained by the CKN bound. We
study the CKN bound in de Sitter space in detail and point out that when the
SDC is combined, the bound on the slow-roll parameter which forbids the eternal
inflation is obtained.


Abstract:
  Efficient, reliable and reproducible target volume delineation is a key step
in the effective planning of breast radiotherapy. However, post-operative
breast target delineation is challenging as the contrast between the tumor bed
volume (TBV) and normal breast tissue is relatively low in CT images. In this
study, we propose to mimic the marker-guidance procedure in manual target
delineation. We developed a saliency-based deep learning segmentation (SDL-Seg)
algorithm for accurate TBV segmentation in post-operative breast irradiation.
The SDL-Seg algorithm incorporates saliency information in the form of markers'
location cues into a U-Net model. The design forces the model to encode the
location-related features, which underscores regions with high saliency levels
and suppresses low saliency regions. The saliency maps were generated by
identifying markers on CT images. Markers' locations were then converted to
probability maps using a distance-transformation coupled with a Gaussian
filter. Subsequently, the CT images and the corresponding saliency maps formed
a multi-channel input for the SDL-Seg network. Our in-house dataset was
comprised of 145 prone CT images from 29 post-operative breast cancer patients,
who received 5-fraction partial breast irradiation (PBI) regimen on GammaPod.
The performance of the proposed method was compared against basic U-Net. Our
model achieved mean (standard deviation) of 76.4 %, 6.76 mm, and 1.9 mm for
DSC, HD95, and ASD respectively on the test set with computation time of below
11 seconds per one CT volume. SDL-Seg showed superior performance relative to
basic U-Net for all the evaluation metrics while preserving low computation
cost. The findings demonstrate that SDL-Seg is a promising approach for
improving the efficiency and accuracy of the on-line treatment planning
procedure of PBI, such as GammaPod based PBI.


Abstract:
  In recent years, many incidents have been reported where machine learning
models exhibited discrimination among people based on race, sex, age, etc.
Research has been conducted to measure and mitigate unfairness in machine
learning models. For a machine learning task, it is a common practice to build
a pipeline that includes an ordered set of data preprocessing stages followed
by a classifier. However, most of the research on fairness has considered a
single classifier based prediction task. What are the fairness impacts of the
preprocessing stages in machine learning pipeline? Furthermore, studies showed
that often the root cause of unfairness is ingrained in the data itself, rather
than the model. But no research has been conducted to measure the unfairness
caused by a specific transformation made in the data preprocessing stage. In
this paper, we introduced the causal method of fairness to reason about the
fairness impact of data preprocessing stages in ML pipeline. We leveraged
existing metrics to define the fairness measures of the stages. Then we
conducted a detailed fairness evaluation of the preprocessing stages in 37
pipelines collected from three different sources. Our results show that certain
data transformers are causing the model to exhibit unfairness. We identified a
number of fairness patterns in several categories of data transformers.
Finally, we showed how the local fairness of a preprocessing stage composes in
the global fairness of the pipeline. We used the fairness composition to choose
appropriate downstream transformer that mitigates unfairness in the machine
learning pipeline.


Abstract:
  We introduce $k$-maximal sub-polygon graphs ($k$-MSP graphs), the
intersection graphs of maximal polygons contained in a polygon with sides
parallel to a regular $2k$-gon. We prove that all complete graphs are $k$-MSP
graphs for all $k>1$; trees are $2$-MSP graphs; trees are $k$-MSP graphs for
$k>2$ if and only if they're caterpillars; and $n$-cycles are not $k$-MSP
graphs for $n>3$ and $k>1$. We derive bounds for which $j$-cycles appear as
induced subgraphs of $k$-MSP graphs. As our main result, we construct examples
of graphs which are $k$-MSP graphs and not $j$-MSP graphs for all $k>1$, $j>1$,
$k \neq j$.


Abstract:
  A typical galaxy survey geometry results in galaxy pairs of different
separation and angle to the line-of-sight having different distributions in
redshift and consequently a different effective redshift. However, clustering
measurements are analysed assuming that the clustering is representative of
that at a single effective redshift. We investigate the impact of variations in
the galaxy-pair effective redshift on the large-scale clustering measured in
galaxy surveys. We find that galaxy surveys spanning a large redshift range
have different effective redshifts as a function of both pair separation and
angle. Furthermore, when considering tracers whose clustering amplitude evolves
strongly with redshift, this combination can result in an additional
scale-dependent clustering anisotropy. We demonstrate the size of this effect
on the eBOSS DR16 Quasar sample and show that, while the impact on monopole is
negligible, neglecting this effect can result in a large-scale tilt of $\sim
4\%$ and $\sim40\%$ in quadrupole and hexadecapole, respectively. We discuss
strategies to mitigate this effect when making measurements.


Abstract:
  In Meta-Interpretive Learning (MIL) the metarules, second-order datalog
clauses acting as inductive bias, are manually defined by the user. In this
work we show that second-order metarules for MIL can be learned by MIL. We
define a generality ordering of metarules by $\theta$-subsumption and show that
user-defined \emph{sort metarules} are derivable by specialisation of the
most-general \emph{matrix metarules} in a language class; and that these matrix
metarules are in turn derivable by specialisation of third-order \emph{punch
metarules} with variables quantified over the set of atoms and for which only
an upper bound on their number of literals need be user-defined. We show that
the cardinality of a metarule language is polynomial in the number of literals
in punch metarules. We re-frame MIL as metarule specialisation by resolution.
We modify the MIL metarule specialisation operator to return new metarules
rather than first-order clauses and prove the correctness of the new operator.
We implement the new operator as TOIL, a sub-system of the MIL system Louise.
Our experiments show that as user-defined sort metarules are progressively
replaced by sort metarules learned by TOIL, Louise's predictive accuracy and
training times are maintained. We conclude that automatically derived metarules
can replace user-defined metarules.


Abstract:
  Decentralized online learning for seeking generalized Nash equilibrium (GNE)
of noncooperative games in dynamic environments is studied in this paper. Each
player aims at selfishly minimizing its own time-varying cost function subject
to time-varying coupled constraints and local feasible set constraints. Only
local cost functions and local constraints are available to individual players,
who can receive their neighbors' information through a fixed and connected
graph. In addition, players have no prior knowledge of cost functions and local
constraint functions in the future time. In this setting, a novel distributed
online learning algorithm for seeking GNE of the studied game is devised based
on mirror descent and a primal-dual strategy. It is shown that the presented
algorithm can achieve sublinearly bounded dynamic regrets and constraint
violation by appropriately choosing decreasing stepsizes. Finally, the obtained
theoretical result is corroborated by a numerical simulation.


Abstract:
  We have implemented a control system for experiments in atomic, molecular and
optical physics based on a commercial low-cost board, featuring a
field-programmable gate array as part of a system-on-a-chip on which a Linux
operating system is running. The board features Gigabit Ethernet, allowing for
fast data transmission and operation of remote experimental systems. A single
board can control a set of devices generating digital, analog and radio
frequency signals with a precise timing given either by an external or internal
clock. Contiguous output and input sampling rates of up to 40 MHz are
achievable. Several boards can run synchronously with a timing error
approaching 1 ns. For this purpose, a novel auto-synchronization scheme is
demonstrated, with possible application in complex distributed experimental
setups with demanding timing requests.


Abstract:
  Traditional scene graph generation methods are trained using cross-entropy
losses that treat objects and relationships as independent entities. Such a
formulation, however, ignores the structure in the output space, in an
inherently structured prediction problem. In this work, we introduce a novel
energy-based learning framework for generating scene graphs. The proposed
formulation allows for efficiently incorporating the structure of scene graphs
in the output space. This additional constraint in the learning framework acts
as an inductive bias and allows models to learn efficiently from a small number
of labels. We use the proposed energy-based framework to train existing
state-of-the-art models and obtain a significant performance improvement, of up
to 21% and 27%, on the Visual Genome and GQA benchmark datasets, respectively.
Furthermore, we showcase the learning efficiency of the proposed framework by
demonstrating superior performance in the zero- and few-shot settings where
data is scarce.


Abstract:
  The integration of Information and Communication Technology (ICT) tools into
mechanical devices found in aviation industry has raised security concerns. The
more integrated the system, the more vulnerable due to the inherent
vulnerabilities found in ICT tools and software that drives the system. The
security concerns have become more heightened as the concept of
electronic-enabled aircraft and smart airports get refined and implemented
underway. In line with the above, this paper undertakes a review of
cyber-security incidence in the aviation sector over the last 20 years. The
essence is to understand the common threat actors, their motivations, the type
of attacks, aviation infrastructure that is commonly attacked and then match
these so as to provide insight on the current state of the cyber-security in
the aviation sector. The review showed that the industry's threats come mainly
from Advance Persistent Threat (APT) groups that work in collaboration with
some state actors to steal intellectual property and intelligence, in order to
advance their domestic aerospace capabilities as well as possibly monitor,
infiltrate and subvert other nations' capabilities. The segment of the aviation
industry commonly attacked is the Information Technology infrastructure, and
the prominent type of attacks is malicious hacking activities that aim at
gaining unauthorised access using known malicious password cracking techniques
such as Brute force attacks, Dictionary attacks and so on. The review further
analysed the different attack surfaces that exist in aviation industry, threat
dynamics, and use these dynamics to predict future trends of cyberattacks in
the industry. The aim is to provide information for the cybersecurity
professionals and aviation stakeholders for proactive actions in protecting
these critical infrastructures against cyberincidence for an optimal customer
service oriented industry.


Abstract:
  We study the validity of Bekenstein's entropy bound for a charged black hole
in the context of nonlinear electrodynamics. Bekenstein's inequalities are
commonly understood as universal relations between the entropy, the charge, the
momentum, and the energy of a physical system but independent of its dynamics.
In particular, we consider the Born-Infeld electrodynamics coupled to gravity
as described by General Relativity. Following the steps that lead to these
inequalities, we study the absorption of a charged test particle by the black
hole and verify that the entropy bound is violated. We find a modified upper
bound for the entropy that depends on the maximum field parameter of the
Born-Infeld theory.


Abstract:
  We explore links between the thin concurrent games of Castellan, Clairambault
and Winskel, and the weighted relational models of linear logic studied by
Laird, Manzonetto, McCusker and Pagani. More precisely, we show that there is
an interpretationpreserving "collapse" functor from the former to the latter.
On objects, the functor defines for each game a set of possible execution
states. Defining the action on morphisms is more subtle, and this is the main
contribution of the paper. Given a strategy and an execution state, our functor
needs to count the witnesses for this state within the strategy. Strategies in
thin concurrent games describe non-linear behaviour explicitly, so in general
each witness exists in countably many symmetric copies. The challenge is to
define the right notion of witnesses, factoring out this infinity while
matching the weighted relational model. Understanding how witnesses compose is
particularly subtle and requires a delve into the combinatorics of witnesses
and their symmetries. In its basic form, this functor connects thin concurrent
games and a relational model weighted by N $\cup$ {+$\infty$}. We will
additionally consider a generalised setting where both models are weighted by
elements of an arbitrary continuous semiring; this covers the probabilistic
case, among others. Witnesses now additionally carry a value from the semiring,
and our interpretation-preserving collapse functor extends to this setting.


Abstract:
  In this article we report a case study of a Language Learning Bauhaus VR
hackathon with Goethe Institute. It was organized as an educational and
research project to tap into the dynamics of transdisciplinary teams challenged
with a specific requirement. In our case, it was to build a Bauhaus-themed
German Language Learning VR App. We constructed this experiment to simulate how
representatives of different disciplines may work together towards a very
specific purpose under time pressure. So, each participating team consisted of
members of various expert-fields: software development (Unity or Unreal),
design, psychology and linguistics. The results of this study cast light on the
recommended cycle of design thinking and customer-centered design in VR.
Especially in interdisciplinary rapid prototyping conditions, where
stakeholders initially do not share competences. They also showcase educational
benefits of working in transdisciplinary environments. This study, combined
with our previous work on human factors in rapid software development and
co-design, including hackathon dynamics, allowed us to formulate
recommendations for organizing content creation VR hackathons for specific
purposes. We also provide guidelines on how to prepare the participants to work
in rapid prototyping VR environments and benefit from such experiences in the
long term.


Abstract:
  We present new results on the equation of state and transition line of hot
and dense strongly interacting QCD matter, obtained from a bottom-up
Einstein-Maxwell-Dilaton holographic model. We considerably expand the previous
coverage in baryon densities in this model by implementing new numerical
methods to map the holographic black hole solutions onto the QCD phase diagram.
We are also able to obtain, for the first time, the first-order phase
transition line in a wide region of the phase diagram. Comparisons with the
most recent lattice results for the QCD thermodynamics are also presented.


Abstract:
  In the present paper we initiate the variational analysis of a super
sinh-Gordon system on compact surfaces, yielding the first example of
non-trivial solution of min-max type. The proof is based on a linking argument
jointly with a suitably defined Nehari manifold and a careful analysis of
Palais-Smale sequences. We complement this study with a multiplicity result
exploiting the symmetry of the problem.


Abstract:
  R package krippendorffsalpha provides tools for measuring agreement using
Krippendorff's Alpha coefficient, a well-known nonparametric measure of
agreement (also called inter-rater reliability and various other names). This
article first develops Krippendorff's Alpha in a natural way, and situates
Alpha among statistical procedures. Then the usage of package
krippendorffsalpha is illustrated via analyses of two datasets, the latter of
which was collected during an imaging study of hip cartilage. The package
permits users to apply the Alpha methodology using built-in distance functions
for the nominal, ordinal, interval, or ratio levels of measurement.
User-defined distance functions are also supported. The fitting function can
accommodate any number of units, any number of coders, and missingness.
Bootstrap inference is supported, and the bootstrap computation can be carried
out in parallel.


Abstract:
  Automatic modulation classification enables intelligent communications and it
is of crucial importance in today's and future wireless communication networks.
Although many automatic modulation classification schemes have been proposed,
they cannot tackle the intra-class diversity problem caused by the dynamic
changes of the wireless communication environment. In order to overcome this
problem, inspired by face recognition, a novel automatic modulation
classification scheme is proposed by using the multi-scale network in this
paper. Moreover, a novel loss function that combines the center loss and the
cross entropy loss is exploited to learn both discriminative and separable
features in order to further improve the classification performance. Extensive
simulation results demonstrate that our proposed automatic modulation
classification scheme can achieve better performance than the benchmark schemes
in terms of the classification accuracy. The influence of the network
parameters and the loss function with the two-stage training strategy on the
classification accuracy of our proposed scheme are investigated.


Abstract:
  We demonstrate collectively enhanced vacuum-induced quantum beat dynamics
from a three-level V-type atomic system. Exciting a dilute atomic gas of
magneto-optically trapped $^{85}$Rb atoms with a weak drive resonant on one of
the transitions, we observe the forward-scattered field after a sudden shut-off
of the laser. The subsequent radiative dynamics, measured for various optical
depths of the atomic cloud, exhibits superradiant decay rates, as well as
collectively enhanced quantum beats. Our work is also the first experimental
illustration of quantum beats arising from atoms initially prepared in a single
excited level as a result of the vacuum-induced coupling between excited
levels.


Abstract:
  We consider the problem of Byzantine fault-tolerance in the peer-to-peer
(P2P) distributed gradient-descent method -- a prominent algorithm for
distributed optimization in a P2P system. In this problem, the system comprises
of multiple agents, and each agent has a local cost function. In the fault-free
case, when all the agents are honest, the P2P distributed gradient-descent
method allows all the agents to reach a consensus on a solution that minimizes
their aggregate cost. However, we consider a scenario where a certain number of
agents may be Byzantine faulty. Such faulty agents may not follow an algorithm
correctly, and may share arbitrary incorrect information to prevent other
non-faulty agents from solving the optimization problem. In the presence of
Byzantine faulty agents, a more reasonable goal is to allow all the non-faulty
agents to reach a consensus on a solution that minimizes the aggregate cost of
all the non-faulty agents. We refer to this fault-tolerance goal as
$f$-resilience where $f$ is the maximum number of Byzantine faulty agents in a
system of $n$ agents, with $f < n$. Most prior work on fault-tolerance in P2P
distributed optimization only consider approximate fault-tolerance wherein,
unlike $f$-resilience, all the non-faulty agents' compute a minimum point of a
non-uniformly weighted aggregate of their cost functions. We propose a
fault-tolerance mechanism that confers provable $f$-resilience to the P2P
distributed gradient-descent method, provided the non-faulty agents satisfy the
necessary condition of $2f$-redundancy, defined later in the paper. Moreover,
compared to prior work, our algorithm is applicable to a larger class of
high-dimensional convex distributed optimization problems.


Abstract:
  Highly Oriented Pyrolytic Graphite was exfoliated via pulsed discharge plasma
in liquid nitrogen. The potential mechanisms involved were investigated by
observing the treated surface of the graphitic material and the obtained
particles. Non-exfoliating defects from the plasma treatment were observed and
experimental parameter were modified to counteract those. One experiment was
performed without exposing the HOPG directly to the discharges so as to better
understand the plasma role. The exfoliated particles were observed via TEM and
SEM to evaluate the defects, the size, the purity and the crystallinity but no
quantitative characterization of their thickness was possible so the actual
number of layer of each particle is unknown. Nonetheless, few layers graphene
(FLG) was successfully exfoliated through this process. The proposed mechanisms
were extrapolated from the observation of the damaged HOPG surface and the
obtained particles but the correlation found does not prove causation.


Abstract:
  Unsupervised continual learning remains a relatively uncharted territory in
the existing literature because the vast majority of existing works call for
unlimited access of ground truth incurring expensive labelling cost. Another
issue lies in the problem of task boundaries and task IDs which must be known
for model's updates or model's predictions hindering feasibility for real-time
deployment. Knowledge Retention in Self-Adaptive Deep Continual Learner,
(KIERA), is proposed in this paper. KIERA is developed from the notion of
flexible deep clustering approach possessing an elastic network structure to
cope with changing environments in the timely manner. The centroid-based
experience replay is put forward to overcome the catastrophic forgetting
problem. KIERA does not exploit any labelled samples for model updates while
featuring a task-agnostic merit. The advantage of KIERA has been numerically
validated in popular continual learning problems where it shows highly
competitive performance compared to state-of-the art approaches. Our
implementation is available in
\textit{\url{https://github.com/ContinualAL/KIERA}}.


Abstract:
  The prevailing framework for matching multimodal inputs is based on a
two-stage process: 1) detecting proposals with an object detector and 2)
matching text queries with proposals. Existing two-stage solutions mostly focus
on the matching step. In this paper, we argue that these methods overlook an
obvious \emph{mismatch} between the roles of proposals in the two stages: they
generate proposals solely based on the detection confidence (i.e.,
query-agnostic), hoping that the proposals contain all instances mentioned in
the text query (i.e., query-aware). Due to this mismatch, chances are that
proposals relevant to the text query are suppressed during the filtering
process, which in turn bounds the matching performance. To this end, we propose
VL-NMS, which is the first method to yield query-aware proposals at the first
stage. VL-NMS regards all mentioned instances as critical objects, and
introduces a lightweight module to predict a score for aligning each proposal
with a critical object. These scores can guide the NMS operation to filter out
proposals irrelevant to the text query, increasing the recall of critical
objects, resulting in a significantly improved matching performance. Since
VL-NMS is agnostic to the matching step, it can be easily integrated into any
state-of-the-art two-stage matching methods. We validate the effectiveness of
VL-NMS on two multimodal matching tasks, namely referring expression grounding
and image-text matching. Extensive ablation studies on several baselines and
benchmarks consistently demonstrate the superiority of VL-NMS.


Abstract:
  The passivation of thin Bi(1 1 1) films with hydrogen and oxide capping
layers is investigated from first principles. Considering termination related
changes of the crystal structure, we show how the bands and density of states
are affected. In the context of the much discussed semimetal-to-semiconductor
transition and the band topology of the bulk material, we consider the effects
of confinement in the whole Brillouin zone and go beyond standard density
functional theory by including many-body interactions via the G$_0$W$_0$
approximation. The conductivity of unterminated films is calculated via the
Boltzmann transport equation using the simple constant relaxation time
approximation and compared to experimental observations which have suggested a
two-channel model.


Abstract:
  We determine the anti-Ramsey numbers for paths. This confirms a conjecture
posed by Erd\H{o}s, Simonovits and S\'{o}s in 1970s.


Abstract:
  We study planar graphs with large negative curvature outside of a finite set
and the spectral theory of Schr{\"o}dinger operators on these graphs. We obtain
estimates on the first and second order term of the eigenvalue asymptotics.
Moreover, we prove a unique continuation result for eigenfunctions and decay
properties of general eigenfunctions. The proofs rely on a detailed analysis of
the geometry which employs a Copy-and-Paste procedure based on the
Gau{\ss}-Bonnet theorem.


Abstract:
  In the present paper, we study a two-player zero-sum deterministic
differential game with both players adopting impulse controls, in infinite time
horizon, under rather weak assumptions on the cost functions. We prove by means
of the dynamic programming principle (DPP) that the lower and upper value
functions are continuous and viscosity solutions to the corresponding
Hamilton-Jacobi-Bellman-Isaacs (HJBI) quasi-variational inequality (QVI). We
define a new HJBI QVI for which, under a proportional property assumption on
the maximizer cost, the value functions are the unique viscosity solution. We
then prove that the lower and upper value functions coincide.


Abstract:
  Researchers have compared machine learning (ML) classifiers and discrete
choice models (DCMs) in predicting travel behavior, but the generalizability of
the findings is limited by the specifics of data, contexts, and authors'
expertise. This study seeks to provide a generalizable empirical benchmark by
comparing hundreds of ML and DCM classifiers in a highly structured manner. The
experiments evaluate both prediction accuracy and computational cost by
spanning four hyper-dimensions, including 105 ML and DCM classifiers from 12
model families, 3 datasets, 3 sample sizes, and 3 outputs. This experimental
design leads to an immense number of 6,970 experiments, which are corroborated
with a meta dataset of 136 experiment points from 35 previous studies. This
study is hitherto the most comprehensive and almost exhaustive comparison of
the classifiers for travel behavioral prediction. We found that the ensemble
methods and deep neural networks achieve the highest predictive performance,
but at a relatively high computational cost. Random forests are the most
computationally efficient, balancing between prediction and computation. While
discrete choice models offer accuracy with only 3-4 percentage points lower
than the top ML classifiers, they have much longer computational time and
become computationally impossible with large sample size, high input
dimensions, or simulation-based estimation. The relative ranking of the ML and
DCM classifiers is highly stable, while the absolute values of the prediction
accuracy and computational time have large variations. Overall, this paper
suggests using deep neural networks, model ensembles, and random forests as
baseline models for future travel behavior prediction. For choice modeling, the
DCM community should switch more attention from fitting models to improving
computational efficiency, so that the DCMs can be widely adopted in the big
data context.


Abstract:
  In this paper, we derive an \emph{a priori} second order estimate for
solutions which are in $\Gamma_{k+1}$ cone to a class of complex Hessian
equations with both sides of the equation depending on the gradient on compact
Hermitian manifolds.


Abstract:
  In this paper we consider $4$-dimensional steady soliton singularity models,
i.e., complete steady gradient Ricci solitons that arise as the rescaled limit
of a finite time singular solution of the Ricci flow on a closed $4$-manifold.
In particular, we study the geometry at infinity of such Ricci solitons under
the assumption that their tangent flow at infinity is the product of
$\mathbb{R}$ with a $3$-dimensional spherical space form. We also classify the
tangent flows at infinity of $4$-dimensional steady soliton singularity models
in general.


Abstract:
  In this paper we strengthen a result due to Li by showing that the third
bi-power of a locally finite connected bipartite graph that admits a perfect
matching is Hamilton-laceable, i.e. any two vertices from different bipartition
classes are endpoints of some common Hamilton arc.


Abstract:
  We apply the theory of infinitesimal transformations for the study of a
family of 1+1 fifth-order partial differential equations which have been
proposed before for the description of multiple kink solutions. In this
analysis we perform a complete classification of the Lie symmetries and of the
one-dimensional optimal system. The results are applied for the derivation of
similarity solutions and in particular we find travel-wave and scaling
solutions. We show that the kink-solution of these equations can be recovered
by the use of the Lie symmetries, while new solutions are also derived.


Abstract:
  In a CMOS image sensor pixel, fast and complete charge transfer from pinned
photodiode (PPD) is desired and necessary in some applications. In special
cases such as time-of-flight imaging or large pinned photodiodes, the PPD
potential well shape highly affects the charge transfer performance and should
be engineered carefully. In the present work, a PPD structure named multi-level
PPD is introduced and examined through simulation study. Moreover, a fast and
effective way to analyze the pinning process for a lag-free design is
introduced. It is concluded that the proposed PPD achieves fast and complete
charge transfer without additional implementation masks or process steps. The
proposed PPD is compared with a similar conventional rectangular pixel and 31%
reduction in the charge transfer time is observed.


Abstract:
  In subscription-based businesses, the churn rate refers to the percentage of
customers who discontinue their subscriptions within a given time period.
Particularly, in the mobile games industry, the churn rate is often pronounced
due to the high competition and cost in customer acquisition; therefore, the
process of minimizing the churn rate is crucial. This needs churn prediction,
predicting users who will be churning within a given time period. Accurate
churn prediction can enable the businesses to devise and engage strategic
remediations to maintain a low churn rate. The paper presents our highly
accurate churn prediction method. We designed this method to take into account
each individual user's distinct usage period in churn prediction. As presented
in the paper, this approach was able to achieve 96.6% churn prediction accuracy
on a real game business. In addition, the paper shows that other existing churn
prediction algorithms are improved in prediction accuracy when this method is
applied.


Abstract:
  Few-shot segmentation~(FSS) performance has been extensively promoted by
introducing episodic training and class-wise prototypes. However, the FSS
problem remains challenging due to three limitations: (1) Models are distracted
by task-unrelated information; (2) The representation ability of a single
prototype is limited; (3) Class-related prototypes ignore the prior knowledge
of base classes. We propose the Prior-Enhanced network with Meta-Prototypes to
tackle these limitations. The prior-enhanced network leverages the support and
query (pseudo-) labels in feature extraction, which guides the model to focus
on the task-related features of the foreground objects, and suppress much noise
due to the lack of supervised knowledge. Moreover, we introduce multiple
meta-prototypes to encode hierarchical features and learn class-agnostic
structural information. The hierarchical features help the model highlight the
decision boundary and focus on hard pixels, and the structural information
learned from base classes is treated as the prior knowledge for novel classes.
Experiments show that our method achieves the mean-IoU scores of 60.79% and
41.16% on PASCAL-$5^i$ and COCO-$20^i$, outperforming the state-of-the-art
method by 3.49% and 5.64% in the 5-shot setting. Moreover, comparing with
1-shot results, our method promotes 5-shot accuracy by 3.73% and 10.32% on the
above two benchmarks. The source code of our method is available at
https://github.com/Jarvis73/PEMP.


Abstract:
  Let $p$ be a prime number such that $p=2$ or $p\equiv 1\pmod 4$. Let
$\varepsilon_p$ denote the fundamental unit of $\mathbb{Q}(\sqrt{p})$ and let
$a$ be a positive square-free integer. In the present paper, we construct the
Hilbert genus field of the real cyclic quartic fields
$\mathbb{Q}(\sqrt{a\varepsilon_p\sqrt{p}})$.


Abstract:
  Computers used for data analytics are often NUMA systems with multiple
sockets per machine, multiple cores per socket, and multiple thread contexts
per core. To get the peak performance out of these machines requires the
correct number of threads to be placed in the correct positions on the machine.
One particularly interesting element of the placement of memory and threads is
the way it effects the movement of data around the machine, and the increased
latency this can introduce to reads and writes. In this paper we describe work
on modeling the bandwidth requirements of an application on a NUMA compute node
based on the placement of threads. The model is parameterized by sampling
performance counters during 2 application runs with carefully chosen thread
placements. Evaluating the model with thousands of measurements shows a median
difference from predictions of 2.34% of the bandwidth. The results of this
modeling can be used in a number of ways varying from: Performance debugging
during development where the programmer can be alerted to potentially
problematic memory access patterns; To systems such as Pandia which take an
application and predict the performance and system load of a proposed thread
count and placement; To libraries of data structures such as Parallel
Collections and Smart Arrays that can abstract from the user memory placement
and thread placement issues when parallelizing code.


Abstract:
  We consider the problem of subset selection for $\ell_{p}$ subspace
approximation, i.e., given $n$ points in $d$ dimensions, we need to pick a
small, representative subset of the given points such that its span gives
$(1+\epsilon)$ approximation to the best $k$-dimensional subspace that
minimizes the sum of $p$-th powers of distances of all the points to this
subspace. Sampling-based subset selection techniques require adaptive sampling
iterations with multiple passes over the data. Matrix sketching techniques give
a single-pass $(1+\epsilon)$ approximation for $\ell_{p}$ subspace
approximation but require additional passes for subset selection.
  In this work, we propose an MCMC algorithm to reduce the number of passes
required by previous subset selection algorithms based on adaptive sampling.
For $p=2$, our algorithm gives subset selection of nearly optimal size in only
$2$ passes, whereas the number of passes required in previous work depend on
$k$. Our algorithm picks a subset of size $\mathrm{poly}(k/\epsilon)$ that
gives $(1+\epsilon)$ approximation to the optimal subspace. The running time of
the algorithm is $nd + d~\mathrm{poly}(k/\epsilon)$. We extend our results to
the case when outliers are present in the datasets, and suggest a two pass
algorithm for the same. Our ideas also extend to give a reduction in the number
of passes required by adaptive sampling algorithms for $\ell_{p}$ subspace
approximation and subset selection, for $p \geq 2$.


Abstract:
  In this work, we propose a secret key generation procedure specifically
designed for the inter-spacecraft communication links. As a novel secrecy
source, the spacecrafts utilize Doppler frequency shift based measurements. In
this way, the mobilities of the communication devices are exploited to generate
secret keys, where this resource can be utilized in the environments that the
channel fading based key generation methods are not available. The mobility of
a spacecraft is modeled as the superposition of a pre-determined component and
a dynamic component. We derive the maximum achievable secret key generation
rate from the Doppler frequency shift. The proposed secret key generation
procedure extracts the Doppler frequency shift in the form of nominal power
spectral density samples (NPSDS). We propose a maximum-likelihood (ML)
estimation for the NPSDS at the spacecrafts, then a uniform quantizer is
utilized to obtain secret key bits. The key disagreement rate (KDR) is
analytically obtained for the proposed key generation procedure. Through
numerical studies, the tightness of the provided approximations is shown. Both
the theoretical and numerical results demonstrate the validity and the
practicality of the presented physical layer key generation procedure
considering the security of the communication links of spacecrafts.


Abstract:
  The $^3{\rm H}(d,n)^4{\rm He}$ reaction is of significant interest in nuclear
astrophysics and nuclear applications. It is an important, early step in
big-bang nucleosynthesis and a key process in nuclear fusion reactors. We use
one- and two-level $R$-matrix approximations to analyze data on the cross
section for this reaction at center-of-mass energies below 215 keV. We
critically examine the data sets using a Bayesian statistical model that allows
for both common-mode and additional point-to-point uncertainties. We use Markov
Chain Monte Carlo sampling to evaluate this $R$-matrix-plus-statistical model
and find two-level $R$-matrix results that are stable with respect to
variations in the channel radii. The $S$ factor at 40 keV evaluates to
$25.36(19)$ MeV b (68% credibility interval). We discuss our Bayesian analysis
in detail and provide guidance for future applications of Bayesian methods to
$R$-matrix analyses. We also discuss possible paths to further reduction of the
$S$-factor uncertainty.


Abstract:
  In this paper, the multivariate tail covariance (MTCov) for generalized
skew-elliptical distributions is considered. Some special cases for this
distribution, such as generalized skew-normal, generalized skew student-t,
generalized skew-logistic and generalized skew-Laplace distributions, are also
considered. In order to test the theoretical feasibility of our results, the
MTCov for skewed and non skewed normal distributions are computed and compared.
Finally, we give a special formula of the MTCov for generalized skew-elliptical
distributions.


Abstract:
  Graph pattern cardinality estimation is the problem of estimating the number
of embeddings of a query graph in a data graph. This fundamental problem
arises, for example, during query planning in subgraph matching algorithms.
There are two major approaches to solving the problem: sampling and synopsis.
Synopsis (or summary)-based methods are fast and accurate if synopses capture
information of graphs well. However, these methods suffer from large errors due
to loss of information during summarization and inherent assumptions.
Sampling-based methods are unbiased but suffer from large estimation variance
due to large sample space.
  To address these limitations, we propose Alley, a hybrid method that combines
both sampling and synopses. Alley employs 1) a novel sampling strategy, random
walk with intersection, which effectively reduces the sample space, 2)
branching to further reduce variance, and 3) a novel mining approach that
extracts and indexes tangled patterns as synopses which are inherently
difficult to estimate by sampling. By using them in the online estimation
phase, we can effectively reduce the sample space while still ensuring
unbiasedness. We establish that Alley has worst-case optimal runtime and
approximation quality guarantees for any given error bound $\epsilon$ and
required confidence $\mu$. In addition to the theoretical aspect of Alley, our
extensive experiments show that Alley outperforms the state-of-the-art methods
by up to orders of magnitude higher accuracy with similar efficiency.