Abstract:
  Word vector embeddings have been shown to contain and amplify biases in data
they are extracted from. Consequently, many techniques have been proposed to
identify, mitigate, and attenuate these biases in word representations. In this
paper, we utilize interactive visualization to increase the interpretability
and accessibility of a collection of state-of-the-art debiasing techniques. To
aid this, we present Visualization of Embedding Representations for deBiasing
system ("VERB"), an open-source web-based visualization tool that helps the
users gain a technical understanding and visual intuition of the inner workings
of debiasing techniques, with a focus on their geometric properties. In
particular, VERB offers easy-to-follow use cases in exploring the effects of
these debiasing techniques on the geometry of high-dimensional word vectors. To
help understand how various debiasing techniques change the underlying
geometry, VERB decomposes each technique into interpretable sequences of
primitive transformations and highlights their effect on the word vectors using
dimensionality reduction and interactive visual exploration. VERB is designed
to target natural language processing (NLP) practitioners who are designing
decision-making systems on top of word embeddings, and also researchers working
with fairness and ethics of machine learning systems in NLP. It can also serve
as a visual medium for education, which helps an NLP novice to understand and
mitigate biases in word embeddings.


Abstract:
  We develop a quantum theory of plasmon polaritons in chains of metallic
nanoparticles, describing both near- and far-field interparticle distances, by
including plasmon-photon Umklapp processes. Taking into account the retardation
effects of the long-range dipole-dipole interaction between the nanoparticles,
which are induced by the coupling of the plasmonic degrees of freedom to the
photonic continuum, we reveal the polaritonic nature of the normal modes of the
system. We compute the dispersion relation and radiative linewidth, as well as
the group velocities of the eigenmodes, and compare our numerical results to
classical electrodynamic calculations within the point-dipole approximation.
Interestingly, the group velocities of the polaritonic excitations present an
almost periodic sign change and are found to be highly tunable by modifying the
spacing between the nanoparticles. We show that, away from the intersection of
the plasmonic eigenfrequencies with the free photon dispersion, an analytical
perturbative treatment of the light-matter interaction is in excellent
agreement with our fully retarded numerical calculations. We further study
quantitatively the hybridization of light and matter excitations, through an
analysis of Hopfield's coefficients. Finally, we consider the limit of
infinitely spaced nanoparticles and discuss some recent results on single
nanoparticles that can be found in the literature.


Abstract:
  When it comes to location-based services (LBS), user privacy protection can
be in conflict with security of both users and trips. While LBS providers could
adopt privacy preservation mechanisms to obfuscate customer data, the accuracy
of vehicle location data and trajectories is crucial for detecting anomalies,
especially when machine learning methods are adopted by LBS. This paper aims to
tackle this dilemma by evaluating the tradeoff between location privacy and
security in LBS. In particular, we investigate the impact of applying location
data privacy-preservation techniques on the performance of two detectors,
namely a Density-based spatial clustering of applications with noise (DBSCAN),
and a Recurrent Neural Network (RNN). The experimental results suggest that, by
applying privacy on location data, DBSCAN is more sensitive to Laplace noise
than RNN, although they achieve similar detection accuracy on the trip data
without privacy preservation. Further experiments reveal that DBSCAN is not
scalable to large size datasets containing millions of trips, because of the
large number of computations needed for clustering trips. On the other hand,
DBSCAN only requires less than 10 percent of the data used by RNN to achieve
similar performance when applied to vehicle data without obfuscation,
demonstrating that clustering-based methods can be easily applied to small
datasets. Based on the results, we recommend usage scenarios of the two types
of trajectory anomaly detectors when applying privacy preservation, by taking
into account customers' need for privacy, the size of the available vehicle
trip data, and real-time constraints of the LBS application.


Abstract:
  We perform a new Monte Carlo QCD analysis of pion parton distribution
functions, including, for the first time, transverse momentum dependent
pion-nucleus Drell-Yan cross sections together with $p_{\rm T}$-integrated
Drell-Yan and leading neutron electroproduction data from HERA. We assess the
sensitivity of the Monte Carlo fits to kinematic cuts, factorization scale, and
parametrization choice, and we discuss the impact of the various datasets on
the pion's quark and gluon distributions. This study provides the necessary
step towards the simultaneous analysis of collinear and transverse momentum
dependent pion distributions and the determination of the pion's
three-dimensional structure.


Abstract:
  The Ly-$\alpha$ line of $^{14}$Si with the energy 3.55-keV is formed in the
magnetic field $6\cdot 10^{12}$ G. In close binary stellar systems with a red
giant and a neutron star in super-strong magnetic fields recombination laser
radiation to the Landau ground level may appear from hydrogen-like ions. If the
3.55-keV Ly-$\alpha$ silicon laser realizes, then the detection of this line
from any distances in the Universe on $z\leq100$ is possible and this line may
be the X-ray candle because of a huge abundance of silicon in these binary
systems. Energies for a Ly-$\alpha$ line of hydrogen-like $^{14}$Si in magnetic
fields $4\cdot10^{12}-10^{13}$ G are calculated. This line is the unique event
because we see this radiation from perpendicular direction to the magnetic
column in near-wall layers. The laser radiation to the ground level in the
range of (1-20) keV may arise from other hydrogen-like ions. In the presence of
a laser, line narrowing is inevitable.


Abstract:
  The Reduced Density Matrix Functional Theory (RDMFT) is a remarkable tool for
studying properties of ground states of strongly interacting quantum many body
systems. As it gives access to the one-particle reduced density matrix of the
ground state, it provides a perfectly tailored approach to studying the
Bose-Einstein condensation or systems of strongly correlated electrons. In
particular, for homogeneous Bose-Einstein condensates as well as for the
Bose-Hubbard dimer it has been recently shown that the relevant density
functional exhibits a repulsive gradient (called the Bose-Einstein condensation
force) which diverges when the fraction of non-condensed bosons tends to zero.
In this paper, we show that the existence of the Bose-Einstein condensation
force is completely universal for any type of pair-interaction and also in the
non-homogeneous gases. To this end, we construct a universal family of
variational trial states which allows us to suitably approximate the relevant
density functional in a finite region around the set of the completely
condensed states. We also show the existence of an analogous repulsive gradient
in the fermionic RDMFT for the $N$-fermion singlet sector in the vicinity of
the set of the Hartree-Fock states. Finally, we show that our approximate
functional may perform well in electron transfer calculations involving low
numbers of electrons. This is demonstrated numerically in the Fermi-Hubbard
model in the strongly correlated limit where some other approximate functionals
are known to fail.


Abstract:
  We prove moment inequalities for exponential sums with respect to singular
measures, whose Fourier decay matches those of curved hypersurfaces. Our
emphasis will be on proving estimates that are sharp with respect to the scale
parameter $N$, apart from $N^\epsilon$ losses. In a few instances, we manage to
remove these losses.


Abstract:
  We investigate the problem of approximating the matrix function $f(A)$ by
$r(A)$, with $f$ a Markov function, $r$ a rational interpolant of $f$, and $A$
a symmetric Toeplitz matrix. In a first step, we obtain a new upper bound for
the relative interpolation error $1-r/f$ on the spectral interval of $A$. By
minimizing this upper bound over all interpolation points, we obtain a new,
simple and sharp a priori bound for the relative interpolation error. We then
consider three different approaches of representing and computing the rational
interpolant $r$. Theoretical and numerical evidence is given that any of these
methods for a scalar argument allows to achieve high precision, even in the
presence of finite precision arithmetic. We finally investigate the problem of
efficiently evaluating $r(A)$, where it turns out that the relative error for a
matrix argument is only small if we use a partial fraction decomposition for
$r$ following Antoulas and Mayo. An important role is played by a new stopping
criterion which ensures to automatically find the degree of $r$ leading to a
small error, even in presence of finite precision arithmetic.


Abstract:
  Microscopic-level understanding of the separation mechanism for
two-dimensional (2D) membranes is an active area of research due to potential
implications of this class of membranes for various technological processes.
Helium (He) purification from the natural resources is of particular interest
due to the shortfall in its production. In this work, we applied the ring
polymer molecular dynamics (RPMD) method to graphdiyne (Gr2) and graphtriyne
(Gr3) 2D membranes having variable pore sizes for the separation of He
isotopes. We found that the transmission rate through Gr3 is many orders of
magnitude greater than Gr2. The selectivity of either isotope at low
temperatures is a consequence of a delicate balance between the zero-point
energy effect and tunneling of $^4$He and $^3$He. RPMD provides an efficient
approach for studying the separation of He isotopes, taking into account
quantum effects of light nuclei motions at low temperatures, which classical
methods fail to capture.


Abstract:
  We discuss the $SU(5)$ grand unified extension of flavour models with
multiple modular symmetries. The proposed model involves two modular $S_4$
groups, one acting in the charged fermion sector, associated with a modulus
field value $\tau_T$ with residual $Z_3^T$ symmetry, and one acting in the
right-handed neutrino sector, associated with another modulus field value
$\tau_{SU}$ with residual $Z_2^{SU}$ symmetry. Quark and lepton mass
hierarchies are naturally generated with the help of weightons, which are SM
singlet fields, where their non-zero modular weights play the role of
Froggatt-Nielsen charges. The model predicts TM$_1$ lepton mixing, and
neutrinoless double beta decay at rates close to the sensitivity of current and
future experiments, for both normal and inverted orderings, with suppressed
corrections from charged lepton mixing due to the triangular form of its Yukawa
matrix.


Abstract:
  We present a novel method for approximating the probability density function
(PDF) of the first-passage times in the Ratcliff diffusion decision model
(DDM). We implemented this approximation method in $\texttt{C++}$ using the
$\texttt{R}$ package $\texttt{Rcpp}$ to utilize the faster $\texttt{C++}$
language while maintaining the $\texttt{R}$ language interface. In addition to
our novel approximation method, we also compiled all known approximation
methods for the DDM density function (with fixed and variable drift rate),
including previously unused combinations of techniques found in the relevant
literature. We ported these approximation methods to $\texttt{C++}$ and
optimized them to run in this new language. Given an acceptable error tolerance
in the value of the PDF approximation, we benchmarked all of these
approximation methods to compare their speed against each other and also
against commonly used $\texttt{R}$ functions from the literature. The results
of these tests show that our novel approximation method is not only orders of
magnitude faster than the current standards, but it is also faster than all of
the other approximation methods available even after translation and
optimization to the faster $\texttt{C++}$ language. All of these approximation
methods are bundled in the $\texttt{fddm}$ package for the $\texttt{R}$
statistical computing language; this package is available via CRAN, and the
source code is available on GitHub.


Abstract:
  We construct an anticyclotomic Euler system for the Rankin-Selberg
convolution of two modular forms, using $p$-adic families of generalized
Gross-Kudla-Schoen diagonal cycles. As applications of this construction, we
prove new cases of the Bloch-Kato conjecture in analytic rank zero (and results
towards new cases in analytic rank one), and a divisibility towards an Iwasawa
main conjecture.


Abstract:
  Recently, program autotuning has become very popular especially in embedded
systems, when we have limited resources such as computing power and memory
where these systems run generally time-critical applications. Compiler
optimization space gradually expands with the renewed compiler options and
inclusion of new architectures. These advancements bring autotuning even more
important position. In this paper, we introduced Flag Optimization with Genetic
Algorithm (FOGA) as an autotuning solution for GCC flag optimization. FOGA has
two main advantages over the other autotuning approaches: the first one is the
hyperparameter tuning of the genetic algorithm (GA), the second one is the
maximum iteration parameter to stop when no further improvement occurs. We
demonstrated remarkable speedup in the execution time of C++ source codes with
the help of optimization flags provided by FOGA when compared to the state of
the art framework OpenTuner.


Abstract:
  This paper introduces new scan statistics for multivariate functional data
indexed in space. The new methods are derivated from a MANOVA test statistic
for functional data, an adaptation of the Hotelling T2-test statistic, and a
multivariate extension of the Wilcoxon rank-sum test statistic. In a simulation
study, the latter two methods present very good performances and the adaptation
of the functional MANOVA also shows good performances for a normal
distribution. Our methods detect more accurate spatial clusters than an
existing nonparametric functional scan statistic. Lastly we applied the methods
on multivariate functional data to search for spatial clusters of abnormal
daily concentrations of air pollutants in the north of France in May and June
2020.


Abstract:
  Automated decision support can accelerate tedious tasks as users can focus
their attention where it is needed most. However, a key concern is whether
users overly trust or cede agency to automation. In this paper, we investigate
the effects of introducing automation to annotating clinical texts--a
multi-step, error-prone task of identifying clinical concepts (e.g.,
procedures) in medical notes, and mapping them to labels in a large ontology.
We consider two forms of decision aid: recommending which labels to map
concepts to, and pre-populating annotation suggestions. Through laboratory
studies, we find that 18 clinicians generally build intuition of when to rely
on automation and when to exercise their own judgement. However, when presented
with fully pre-populated suggestions, these expert users exhibit less agency:
accepting improper mentions, and taking less initiative in creating additional
annotations. Our findings inform how systems and algorithms should be designed
to mitigate the observed issues.


Abstract:
  There exists a large body of previous work using reduced two-dimensional
models of the SOL, which model fluctuations in the drift-plane but approximate
parallel transport with effective loss terms. Full size three-dimensional
simulations of SOL turbulence in experimental geometries are now possible, but
are far more computationally expensive than 2D models. We therefore use a
flux-tube geometry model of the scrape-off layer to compare the results of 2D
simulations to 3D simulations with a similar setup, looking for systematic
differences. Overall there is good agreement in the basic radial profiles,
probability distribution functions, and power spectra of fluctuations. However,
the average temperature is over-predicted in 2D relative to 3D, and we explain
the difference in terms of the effect of geometrical simplifications of devices
at low power. Varying geometric parameters, we find that supersonic flow in the
divertor leg, which occurs because our simulations do not include neutrals and
so represent low-recycling conditions, means that the divertor leg length only
has a weak effect on the output. Finally, we examine the effect of altering the
magnitude of source and sink terms in 2D, concluding that they cannot easily be
used to recreate both the density and temperature profiles observed in 3D
simultaneously.


Abstract:
  In this work, we examine an intelligent reflecting surface (IRS) assisted
downlink non-orthogonal multiple access (NOMA) scenario with the aim of
maximizing the sum rate of users. The optimization problem at the IRS is quite
complicated, and non-convex, since it requires the tuning of the phase shift
reflection matrix. Driven by the rising deployment of deep reinforcement
learning (DRL) techniques that are capable of coping with solving non-convex
optimization problems, we employ DRL to predict and optimally tune the IRS
phase shift matrices. Simulation results reveal that IRS assisted NOMA based on
our utilized DRL scheme achieves high sum rate compared to OMA based one, and
as the transmit power increases, the capability of serving more users
increases. Furthermore, results show that imperfect successive interference
cancellation (SIC) has a deleterious impact on the data rate of users
performing SIC. As the imperfection increases by ten times, the rate decreases
by more than 10%.


Abstract:
  Metasurfaces are a key photonic platform to manipulate classical light using
sub-wavelength structures with designer optical response. Static metasurfaces
have recently entered the realm of quantum photonics, showing their ability to
tailor nonclassical states of light. We introduce the concept of space-time
quantum metasurfaces for dynamical control of quantum light. We provide
illustrative examples of the impact of spatio-temporally modulated metasurfaces
in quantum photonics, including the creation of frequency-spin-path
hyperentanglement on a single photon and the realization of space-time
asymmetry at the deepest level of the quantum vacuum. Photonic platforms based
on the space-time quantum metasurface concept have the potential to enable
novel functionalities, such as on-demand entanglement generation for quantum
communications, nonreciprocal photon propagation for free-space quantum
isolation, and reconfigurable quantum imaging and sensing.


Abstract:
  There has been a paradigm-shift in urban logistic services in the last years;
demand for real-time, instant mobility and delivery services grows. This poses
new challenges to logistic service providers as the underlying stochastic
dynamic vehicle routing problems (SDVRPs) require anticipatory real-time
routing actions. Searching the combinatorial action space for efficient routing
actions is by itself a complex task of mixed-integer programming (MIP)
well-known by the operations research community. This complexity is now
multiplied by the challenge of evaluating such actions with respect to their
effectiveness given future dynamism and uncertainty, a potentially ideal case
for reinforcement learning (RL) well-known by the computer science community.
For solving SDVRPs, joint work of both communities is needed, but as we show,
essentially non-existing. Both communities focus on their individual strengths
leaving potential for improvement. Our survey paper highlights this potential
in research originating from both communities. We point out current obstacles
in SDVRPs and guide towards joint approaches to overcome them.


Abstract:
  We introduce a framework for universal algebra in categories of relational
structures given by finitary relational signatures and finitary or infinitary
Horn theories, with the arity $\lambda$ of a Horn theory understood as a strict
upper bound on the number of premisses in its axioms; key examples include
partial orders ($\lambda=\omega$) or metric spaces ($\lambda=\omega_1$). We
establish a bijective correspondence between $\lambda$-accessible enriched
monads on the given category of relational structures and a notion of
$\lambda$-ary algebraic theories (i.e. with operations of arity $<\lambda$),
with the syntax of algebraic theories induced by the relational signature (e.g.
inequations or equations-up-to-$\epsilon$). We provide a generic sound and
complete derivation system for such relational algebraic theories, thus in
particular recovering (extensions of) recent systems of this type for monads on
partial orders and metric spaces by instantiation. In particular, we present an
$\omega_1$-ary algebraic theory of metric completion. The theory-to-monad
direction of our correspondence remains true for the case of $\kappa$-ary
algebraic theories and $\kappa$-accessible monads for $\kappa<\lambda$, e.g.
for finitary theories over metric spaces.


Abstract:
  The use of Shiny in research publications is investigated. From the
appearance of this popular web application framework for R through to 2018, it
has been utilised in many diverse research areas. While it can be shown that
the complexity of Shiny applications is limited by the background architecture,
and real security concerns exist for novice app developers, the collaborative
benefits are worth attention from the wider research community. Shiny
simplifies the use of complex methodologies for users of different
specialities, at the level of proficiency appropriate for the end user. This
enables a diverse community of users to interact efficiently, utilising
cutting-edge methodologies. The literature reviewed demonstrates that complex
methodologies can be put into practice without the necessity for investment in
professional training. It would appear that Shiny opens up concurrent benefits
in communication between those who analyse data and those in other disciplines,
thereby potentially enriching research through this technology.


Abstract:
  In this paper, we present a novel method to constrain invexity on Neural
Networks (NN). Invex functions ensure every stationary point is global minima.
Hence, gradient descent commenced from any point will lead to the global
minima. Another advantage of invexity on NN is to divide data space locally
into two connected sets with a highly non-linear decision boundary by simply
thresholding the output. To this end, we formulate a universal invex function
approximator and employ it to enforce invexity in NN. We call it Input Invex
Neural Networks (II-NN). We first fit data with a known invex function,
followed by modification with a NN, compare the direction of the gradient and
penalize the direction of gradient on NN if it contradicts with the direction
of reference invex function. In order to penalize the direction of the gradient
we perform Gradient Clipped Gradient Penalty (GC-GP). We applied our method to
the existing NNs for both image classification and regression tasks. From the
extensive empirical and qualitative experiments, we observe that our method
gives the performance similar to ordinary NN yet having invexity. Our method
outperforms linear NN and Input Convex Neural Network (ICNN) with a large
margin. We publish our code and implementation details at github.


Abstract:
  We prove a transversality "lifting property" for compactified configuration
spaces as an application of the multijet transversality theorem: given a
submanifold of configurations of points on an embedding of a compact manifold
$M$ in Euclidean space, we can find a dense set of smooth embeddings of $M$ for
which the corresponding configuration space of points is transverse to any
submanifold of the configuration space of points in Euclidean space, as long as
the two submanifolds of compactified configuration space are boundary-disjoint.
We use this setup to provide an attractive proof of the square-peg problem:
there is a dense family of smoothly embedded circles in the plane where each
simple closed curve has an odd number of inscribed squares, and there is a
dense family of smoothly embedded circles in $\R^n$ where each simple closed
curve has an odd number of inscribed square-like quadrilaterals.


Abstract:
  A Digital Twin (DT) is a digital representation of a physical object used to
simulate it before it is built or to predict failures after the object is
deployed. In this article, we introduce our approach, which applies the concept
of a Cyber Digital Twin (CDT) to automotive software for the purpose of
security analysis. In our approach, automotive firmware is transformed into a
CDT, which contains automatically extracted, security-relevant information from
the firmware. Based on the CDT, we evaluate security requirements through
automated analysis and requirements verification using policy enforcement
checks and vulnerabilities detection. The evaluation of a CDT is conducted
continuously integrating new checks derived from new security requirements and
from newly disclosed vulnerabilities. We applied our approach to about 100
automotive firmwares. In average, about 600 publicly disclosed vulnerabilities
and 80 unknown weaknesses were detected per firmware in the pre-production
phase. Therefore, the use of a CDT enables efficient continuous verification of
security requirements.


Abstract:
  Word embedding learning methods require a large number of occurrences of a
word to accurately learn its embedding. However, out-of-vocabulary (OOV) words
which do not appear in the training corpus emerge frequently in the smaller
downstream data. Recent work formulated OOV embedding learning as a few-shot
regression problem and demonstrated that meta-learning can improve results
obtained. However, the algorithm used, model-agnostic meta-learning (MAML) is
known to be unstable and perform worse when a large number of gradient steps
are used for parameter updates. In this work, we propose the use of Leap, a
meta-learning algorithm which leverages the entire trajectory of the learning
process instead of just the beginning and the end points, and thus ameliorates
these two issues. In our experiments on a benchmark OOV embedding learning
dataset and in an extrinsic evaluation, Leap performs comparably or better than
MAML. We go on to examine which contexts are most beneficial to learn an OOV
embedding from, and propose that the choice of contexts may matter more than
the meta-learning employed.


Abstract:
  We propose a conjugate logic that can capture the behavior of quantum and
quantum-like systems. The proposal is similar to the more generic concept of
epistemic logic: it encodes knowledge or perhaps more correctly, predictions
about outcomes of future observations on some systems. For a quantum system,
these predictions are statements about future outcomes of measurements
performed on specific degrees of freedom of the system. The proposed logic will
include propositions and their relations including connectives, but importantly
also transformations between propositions on conjugate degrees of freedom of
the systems. A key point is the addition of a transformation that allows to
convert propositions about single systems into propositions about correlations
between systems. We will see that subtle choices of the properties of the
transformations lead to drastically different underlying mathematical models;
one choice gives stabilizer quantum mechanics, while another choice gives
Spekkens' toy theory. This points to a crucial basic property of quantum and
quantum-like systems that can be handled within the present conjugate logic by
adjusting the mentioned choice. It also enables a discussion on what behaviors
are properly quantum or only quantum-like, relating to that choice and how it
manifests in the system under scrutiny.


Abstract:
  We examine the double copy structure of anyons in gauge theory and gravity.
Using on-shell amplitude techniques, we construct little group covariant
spinor-helicity variables describing massive particles with spin, which
together with locality and unitarity enables us to derive the long-range
tree-level scattering amplitudes involving anyons. We discover that classical
gauge theory anyon solutions double copy to their gravitational counterparts in
a non-trivial manner. Interestingly, we show that the massless double copy
captures the topological structure of curved spacetime in three dimensions by
introducing a non-trivial mixing of the topological graviton and the dilaton.
Finally, we show that the celebrated Aharonov-Bohm phase can be derived
directly from the constructed on-shell amplitude and that it too enjoys a
simple double copy to its gravitational counterpart.


Abstract:
  Human interpretability of deep neural networks' decisions is crucial,
especially in domains where these directly affect human lives. Counterfactual
explanations of already trained neural networks can be generated by perturbing
input features and attributing importance according to the change in the
classifier's outcome after perturbation. Perturbation can be done by replacing
features using heuristic or generative in-filling methods. The choice of
in-filling function significantly impacts the number of artifacts, i.e.,
false-positive attributions. Heuristic methods result in false-positive
artifacts because the image after the perturbation is far from the original
data distribution. Generative in-filling methods reduce artifacts by producing
in-filling values that respect the original data distribution. However, current
generative in-filling methods may also increase false-negatives due to the high
correlation of in-filling values with the original data. In this paper, we
propose to alleviate this by generating in-fillings with the
statistically-grounded Knockoffs framework, which was developed by Barber and
Cand\`es in 2015 as a tool for variable selection with controllable false
discovery rate. Knockoffs are statistically null-variables as decorrelated as
possible from the original data, which can be swapped with the originals
without changing the underlying data distribution. A comparison of different
in-filling methods indicates that in-filling with knockoffs can reveal
explanations in a more causal sense while still maintaining the compactness of
the explanations.


Abstract:
  This study investigates simple games. A fundamental research question in this
field is to determine necessary and sufficient conditions for a simple game to
be a weighted majority game. Taylor and Zwicker (1992) showed that a simple
game is non-weighted if and only if there exists a trading transform of finite
size. They also provided an upper bound on the size of such a trading
transform, if it exists. Gvozdeva and Slinko (2011) improved that upper bound;
their proof employed a property of linear inequalities demonstrated by Muroga
(1971).In this study, we provide a new proof of the existence of a trading
transform when a given simple game is non-weighted. Our proof employs Farkas'
lemma (1894), and yields an improved upper bound on the size of a trading
transform.
  We also discuss an integer-weight representation of a weighted simple game,
improving the bounds obtained by Muroga (1971). We show that our bound on the
quota is tight when the number of players is less than or equal to five, based
on the computational results obtained by Kurz (2012).
  Furthermore, we discuss the problem of finding an integer-weight
representation under the assumption that we have minimal winning coalitions and
maximal losing coalitions.In particular, we show a performance of a rounding
method.
  Lastly, we address roughly weighted simple games. Gvozdeva and Slinko (2011)
showed that a given simple game is not roughly weighted if and only if there
exists a potent certificate of non-weightedness. We give an upper bound on the
length of a potent certificate of non-weightedness. We also discuss an
integer-weight representation of a roughly weighted simple game.


Abstract:
  We propose a multivariate extension of a well-known characterization by S.
Kusuoka of regular and coherent risk measures as maximal correlation
functionals. This involves an extension of the notion of comonotonicity to
random vectors through generalized quantile functions. Moreover, we propose to
replace the current law invariance, subadditivity and comonotonicity axioms by
an equivalent property we call strong coherence and that we argue has more
natural economic interpretation. Finally, we reformulate the computation of
regular and coherent risk measures as an optimal transportation problem, for
which we provide an algorithm and implementation.


Abstract:
  Crofton formulas on simply-connected Riemannian space forms allow to compute
the volumes, or more generally the Lipschitz-Killing curvature integrals of a
submanifold with corners, by integrating the Euler characteristic of its
intersection with all geodesic submanifolds. We develop a framework of Crofton
formulas with distributions replacing measures, which has in its core Alesker's
Radon transform on valuations. We then apply this framework, and our recent
Hadwiger-type classification, to compute explicit Crofton formulas for all
isometry-invariant valuations on all pseudospheres, pseudo-Euclidean and
pseudohyperbolic spaces. We find that, in essence, a single measure which
depends analytically on the metric, gives rise to all those Crofton formulas
through its distributional boundary values at parts of the boundary
corresponding to the different indefinite signatures. In particular, the
Crofton formulas we obtain are formally independent of signature.


Abstract:
  When data is collected in an adaptive manner, even simple methods like
ordinary least squares can exhibit non-normal asymptotic behavior. As an
undesirable consequence, hypothesis tests and confidence intervals based on
asymptotic normality can lead to erroneous results. We propose an online
debiasing estimator to correct these distributional anomalies in least squares
estimation. Our proposed method takes advantage of the covariance structure
present in the dataset and provides sharper estimates in directions for which
more information has accrued. We establish an asymptotic normality property for
our proposed online debiasing estimator under mild conditions on the data
collection process, and provide asymptotically exact confidence intervals. We
additionally prove a minimax lower bound for the adaptive linear regression
problem, thereby providing a baseline by which to compare estimators. There are
various conditions under which our proposed estimator achieves the minimax
lower bound up to logarithmic factors. We demonstrate the usefulness of our
theory via applications to multi-armed bandit, autoregressive time series
estimation, and active learning with exploration.


Abstract:
  A microscopic calculation is presented for the spin-transfer torques (STT)
and damping torques in metallic antiferromagnets (AF). It is found that the
sign of the STT is opposite to that in ferromagnets because of the AF transport
character, and the current-to-STT conversion factor is enhanced near the AF gap
edge. The dissipative torque parameter $\beta_n$ and the damping parameter
$\alpha_n$ for the N\'eel vector arise from spin relaxation of electrons.
Physical consequences are demonstrated for the AF domain wall motion using
collective coordinates, and some similarities to the ferromagnetic case are
pointed out such as intrinsic pinning and the specialty of $\alpha_n =
\beta_n$. A recent experiment on a ferrimagnetic GdFeCo near its
angular-momentum compensation temperature is discussed.


Abstract:
  This paper investigates the stability properties of discrete-time multilinear
dynamical systems via tensor spectral theory. In particular, if the dynamic
tensor of a multilinear dynamical system is orthogonally decomposable (odeco),
we can construct its explicit solution by exploiting tensor Z-eigenvalues and
Z-eigenvectors. Based on the form of the explicit solution, we illustrate that
the Z-eigenvalues of the dynamic tensor play a significant role in the
stability analysis, offering necessary and sufficient conditions. In addition,
by utilizing the upper bounds of Z-spectral radii, we are able to determine the
asymptotic stability of the multilinear dynamical system efficiently.
Furthermore, we extend the stability results to the multilinear dynamical
systems with non-odeco dynamic tensors by exploiting tensor singular values. We
demonstrate our results via numerical examples.


Abstract:
  The rapid spread of COVID-19 has already affected human lives throughout the
globe. Governments of different countries have taken various measures, but how
they affected people lives is not clear. In this study, a rule-based and a
machine-learning based models are applied to answer the above question using
public tweets from Japan, USA, UK, and Australia. Two polarity timeseries
(meanPol and pnRatio) and two events, namely "lockdown or emergency (LED)" and
"the economic support package (ESP)", are considered in this study. Statistical
testing on the sub-series around LED and ESP events showed their positive
impacts to the people of (UK and Australia) and (USA and UK), respectively
unlike Japanese people that showed opposite effects. Manual validation with the
relevant tweets showed an agreement with the statistical results. A case study
with Japanese tweets using supervised logistic regression classifies tweets
into heath-worry, economy-worry and other classes with 83.11% accuracy.
Predicted tweets around events re-confirm the statistical outcomes.


Abstract:
  We study the influence of the in-medium mass difference between boson and
antiboson on their spectra. The in-medium mass difference may lead to a
difference between the transverse momentum spectra of boson and antiboson. This
effect increases with the increasing in-medium mass difference between boson
and antiboson. The difference between the transverse momentum spectra of boson
and antiboson increases with the increasing expanding velocity of the source
and decreases with the increasing transverse momentum in large transverse mass
region (mT > 1:6 GeV). The interactions between the hadron and the medium may
increase with the increasing temperature of the medium and the higher
freeze-out temperature may lead to a larger mass difference between boson and
antiboson, and may give rise to a larger difference between the transverse
momentum spectra of boson and antiboson for higher freeze-out temperature.


Abstract:
  The use of iteration and piecewise functions allows analytic expression of
the trajectories of an R\"ossler-like attractor, avoiding infinite series
solution. It seems possible to extend this approach to other attractors, even
if the differential equations are not known, but the experimental data have
been collected.


Abstract:
  For geometrically finite Kleinian surface groups, Bonahon and Otal proved the
existence part, and partly the uniqueness part of the bending lamination
conjecture. In this paper, we generalise the existence part to general Kleinian
surface groups including geometrically infinite ones. We furthermore prove the
compactness of the set of Kleinian surface groups realising an arbitrarily
fixed data of bending laminations and ending laminations. Our proof is
independent of that of Bonahon and Otal.


Abstract:
  The rise of intelligent assistant systems like Siri and Alexa have led to the
emergence of Conversational Search, a research track of Information Retrieval
(IR) that involves interactive and iterative information-seeking user-system
dialog. Recently released OR-QuAC and TCAsT19 datasets narrow their research
focus on the retrieval aspect of conversational search i.e. fetching the
relevant documents (passages) from a large collection using the conversational
search history. Currently proposed models for these datasets incorporate
history in retrieval by appending the last N turns to the current question
before encoding. We propose to use another history selection approach that
dynamically selects and weighs history turns using the attention mechanism for
question embedding. The novelty of our approach lies in experimenting with soft
attention-based history selection approach in an open-retrieval setting.


Abstract:
  This paper presents an extension to an existing instruction set architecture,
which gains considerable reduction in power consumption. The reduction in power
consumption is achieved through coding of the most commonly executed
instructions in a short format done by the compiler based on a profile of
previous executions. This leads to fewer accesses to the instruction cache and
that more instructions can fit in the cache. As a secondary effect, this turned
out to be very beneficial in terms of power. Another major advantage, which is
the main concern of this paper is the reduction in the number of instruction
fetch cycles which will also contribute significantly towards reduction in
power consumption. The work involves implementing the new processor
architecture in ASIC and estimation of power-consumption compared to the normal
architecture.


Abstract:
  By analyzing the analogies between the effective system of $N$ spins
described by the Ising Hamiltonian and the phenomenon of the self--gravity in
mixed particle systems, we show that cooled ions held in a segmented ion trap
and exposed to a magnetic field gradient can simulate the proposed mechanism of
mutual interaction in mixed neutrino system. We show that with trapped ions one
can reproduce the expected corrections to the flavor transitions and the $CPT$
violation induced by gravity on flavor fields, which may have played an
important role in the early stages of the universe. The results presented are
experimentally testable. They indicate that ions confined in microtraps can
represent a new tool to test fundamental phenomena of nature.


Abstract:
  Deep Neural Networks lead the state of the art of computer vision tasks.
Despite this, Neural Networks are brittle in that small changes in the input
can drastically affect their prediction outcome and confidence. Consequently
and naturally, research in this area mainly focus on adversarial attacks and
defenses. In this paper, we take an alternative stance and introduce the
concept of Assistive Signals, which are optimized to improve a model's
confidence score regardless if it's under attack or not. We analyse some
interesting properties of these assistive perturbations and extend the idea to
optimize assistive signals in the 3D space for real-life scenarios simulating
different lighting conditions and viewing angles. Experimental evaluations show
that the assistive signals generated by our optimization method increase the
accuracy and confidence of deep models more than those generated by
conventional methods that work in the 2D space. In addition, our Assistive
Signals illustrate the intrinsic bias of ML models towards certain patterns in
real-life objects. We discuss how we can exploit these insights to re-think, or
avoid, some patterns that might contribute to, or degrade, the detectability of
objects in the real-world.


Abstract:
  Considering the growing demand and popularity of Do-It-Yourself (DIY)
networks, low-cost devices managed by the people, for the people and the ease
of deployment of localised/decentralised Internet services, it is convenient
for such networks to have an efficient low-cost monitoring platform to ensure
availability, responsiveness and users' Quality of Experience (QoE). This is
especially relevant for the developing world where Community Networks (CN) are
increasing in popularity and complexity. In this letter, we discuss Octopus: an
architecture for stream network monitoring.


Abstract:
  Learning an unknown $n$-qubit quantum state $\rho$ is a fundamental challenge
in quantum computing. Information-theoretically, it is known that tomography
requires exponential in $n$ many copies of $\rho$ to estimate it up to trace
distance. Motivated by computational learning theory, Aaronson et al.
introduced many (weaker) learning models: the PAC model of learning states
(Proceedings of Royal Society A'07), shadow tomography (STOC'18) for learning
"shadows" of a state, a model that also requires learners to be differentially
private (STOC'19) and the online model of learning states (NeurIPS'18). In
these models it was shown that an unknown state can be learned "approximately"
using linear-in-$n$ many copies of rho. But is there any relationship between
these models? In this paper we prove a sequence of (information-theoretic)
implications from differentially-private PAC learning, to communication
complexity, to online learning and then to quantum stability.
  Our main result generalizes the recent work of Bun, Livni and Moran (Journal
of the ACM'21) who showed that finite Littlestone dimension (of Boolean-valued
concept classes) implies PAC learnability in the (approximate) differentially
private (DP) setting. We first consider their work in the real-valued setting
and further extend their techniques to the setting of learning quantum states.
Key to our results is our generic quantum online learner, Robust Standard
Optimal Algorithm (RSOA), which is robust to adversarial imprecision. We then
show information-theoretic implications between DP learning quantum states in
the PAC model, learnability of quantum states in the one-way communication
model, online learning of quantum states, quantum stability (which is our
conceptual contribution), various combinatorial parameters and give further
applications to gentle shadow tomography and noisy quantum state learning.


Abstract:
  Vaccination strategy is crucial in fighting against the COVID-19 pandemic.
Since the supply is limited, contact network-based interventions can be most
powerful to set an optimal strategy by identifying high-risk individuals or
communities. However, due to the high dimension, only partial and noisy network
information can be available in practice, especially for dynamical systems
where the contact networks are highly time-variant. Furthermore, numerous
mutations of SARS-CoV-2 impact considerably the current infectious probability,
requiring real-time network updating algorithms. In this study, we propose a
sequential network updating approach based on data assimilation techniques to
combine different sources of temporal information. We then prioritise the
individuals with high-degree or high-centrality, obtained from the assimilated
networks, for vaccination. The assimilation-based approach is compared with the
standard method (based on partially observed networks) and a random selection
strategy in terms of vaccination effectiveness in a SIR model. The numerical
comparison is first carried out using real-world face-to-face dynamical
networks collected in a high school, following by sequential multi-layer
networks, generated relying on the Barabasi-Albert model emulating the
department of Computing at Imperial College London in the UK as an example.


Abstract:
  Monitoring the responses of plants to environmental changes is essential for
plant biodiversity research. This, however, is currently still being done
manually by botanists in the field. This work is very laborious, and the data
obtained is, though following a standardized method to estimate plant coverage,
usually subjective and has a coarse temporal resolution. To remedy these
caveats, we investigate approaches using convolutional neural networks (CNNs)
to automatically extract the relevant data from images, focusing on plant
community composition and species coverages of 9 herbaceous plant species. To
this end, we investigate several standard CNN architectures and different
pretraining methods. We find that we outperform our previous approach at higher
image resolutions using a custom CNN with a mean absolute error of 5.16%. In
addition to these investigations, we also conduct an error analysis based on
the temporal aspect of the plant cover images. This analysis gives insight into
where problems for automatic approaches lie, like occlusion and likely
misclassifications caused by temporal changes.


Abstract:
  We consider the dynamics of a two-dimensional incompressible perfect fluid on
a M\"obius strip embedded in $\mathbb{R}^3$. The vorticity-streamfunction
formulation of the Euler equations is derived from an exterior-calculus form of
the momentum equation. The non-orientability of the M\"obius strip and the
distinction between forms and pseudo-forms this introduces lead to unusual
properties: a boundary condition is provided by the conservation of circulation
along the single boundary of the strip, and there is no integral conservation
for the vorticity or for any odd function thereof. A finite-difference
numerical implementation is used to illustrate the M\"obius-strip realisation
of familiar phenomena: translation of vortices along boundaries, shear
instability, and decaying turbulence.


Abstract:
  We propose a novel algorithm named Expert Q-learning. Expert Q-learning was
inspired by Dueling Q-learning and aimed at incorporating the ideas from
semi-supervised learning into reinforcement learning through splitting Q-values
into state values and action advantages. Different from Generative Adversarial
Imitation Learning and Deep Q-Learning from Demonstrations, the offline expert
we have used only predicts the value of a state from {-1, 0, 1}, indicating
whether this is a bad, neutral or good state. An expert network was designed in
addition to the Q-network, which updates each time following the regular
offline minibatch update whenever the expert example buffer is not empty. The
Q-network plays the role of the advantage function only during the update. Our
algorithm also keeps asynchronous copies of the Q-network and expert network,
predicting the target values using the same manner as of Double Q-learning.
  We compared on the game of Othello our algorithm with the state-of-the-art
Q-learning algorithm, which was a combination of Double Q-learning and Dueling
Q-learning. The results showed that Expert Q-learning was indeed useful and
more resistant to the overestimation bias of Q-learning. The baseline
Q-learning algorithm exhibited unstable and suboptimal behavior, especially
when playing against a stochastic player, whereas Expert Q-learning
demonstrated more robust performance with higher scores. Expert Q-learning
without using examples has also gained better results than the baseline
algorithm when trained and tested against a fixed player. On the other hand,
Expert Q-learning without examples cannot win against the baseline Q-learning
algorithm in direct game competitions despite the fact that it has also shown
the strength of reducing the overestimation bias.


Abstract:
  This article deals with the most recent developments in the field of
exoplanetary science connecting the interior of the planets with their
habitability. In this issue, I have specified the importance of interior
dynamics and briefly reviewed some of the main factors by which interior of a
planet can effect the habitability of extra-solar planets.


Abstract:
  Two qubit density matrices which are of X-shape, are a natural generalization
of Bell Diagonal States recently simulated on the IBM quantum device. We
propose a quantum circuit for simulation of a general X-state on the same
quantum device and study its properties for several values of the extended
parameter space. We also show by specific measurements that their X-shape is
approximately robust against noisy quantum gates. To further physically
motivate this study, we invoke the two-spin Heisenberg XYZ system and show that
for a wide class of initial states, it leads to dynamical density matrices
which are X-states. Due to the symmetries of this Hamiltonian, we show that by
only two qubits, one can simulate the dynamics of this system on the IBM
quantum computer.


Abstract:
  Classes of two-nucleon ($2N$) contact interactions are developed in
configuration space at leading order (LO), next-to-leading order (NLO), and
next-to-next-to-next-to-leading order (N3LO) by fitting the experimental
singlet $np$ scattering length and deuteron binding energy at LO, and $np$ and
$pp$ scattering data in the laboratory-energy range 0--15 MeV at NLO and 0--25
MeV at N3LO. These interactions are regularized by including two Gaussian
cutoffs, one for $T\,$=$\,0$ and the other for $T\,$=$\,1$ channels. The
cutoffs are taken to vary in the ranges $R_0\,$=$(1.5$--2.3) fm and
$R_1\,$=$(1.5$--3.0) fm. The 780 (1,100) data points up to 15 (25) MeV energy,
primarily differential cross sections, are fitted by the NLO (N3LO) models with
a $\chi^2$/datum about 1.7 or less (well below 1.5), when harder cutoff values
are adopted. As a first application, we report results for the binding energies
of nuclei with mass numbers $A\,$=$\,3$--6 and 16 obtained with selected LO and
NLO $2N$ models both by themselves as well as in combination with a LO
three-nucleon ($3N$) contact interaction. The latter is characterized by a
single low-energy constant that is fixed to reproduce the experimental $^3$H
binding energy. The inclusion of the $3N$ interaction largely removes the
sensitivity to cutoff variations in the few-nucleon systems and leads to
predictions for the $^3$He and $^4$He binding energies that cluster around 7.8
MeV and 30 MeV, respectively. However, in $^{16}$O this cutoff sensitivity
remains rather strong. Finally, predictions at LO only are also reported for
medium-mass nuclei with $A\,$=$\,40$, 48, and 90.


Abstract:
  The article reviews the theory of open quantum system from a perspective of
the non-Hermiticity that emerges from the environment with an infinite number
of degrees of freedom. The non-Hermiticity produces resonant states with
complex eigenvalues, resulting in peak structures in scattering amplitudes and
transport coefficients. After introducing the definition of resonant states
with complex eigenvalues, we answer typical questions regarding the
non-Hermiticity of open quantum systems. What is the physical meaning of the
complex eigenmomenta and eigenenergies? How and why do the resonant states
break the time-reversal symmetry that the system observes? Can we make the
probabilistic interpretation of the resonant states with diverging wave
functions? What is the physical meaning of the divergence of the wave
functions? We also present an alternative way of finding resonant states,
namely the Feshbach formalism, in which we eliminate the infinite number of the
environmental degrees of freedom. In this formalism, we attribute the
non-Hermiticity to the introduction of the retarded and advanced Green's
functions.


Abstract:
  The binary neutron star merger gravitational-wave signal GW170817 was
followed by three electromagnetic counterparts, including a kilonova arising
from the radioactivity of freshly synthesized $r$-process elements in ejecta
from the merger. Finding kilonovae after gravitational-wave triggers is crucial
for (i) the search for further counterparts, such as the afterglow, (ii)
probing the diversity of kilonovae and their dependence on the system's
inclination angle, and (iii) building a sample for multi-messenger cosmology.
During the third observing run of the gravitational-wave interferometer
network, no kilonova counterpart was found. We aim to predict the expected
population of detectable kilonova signals for the upcoming O4 and O5 observing
runs of the LIGO-Virgo-KAGRA instruments. Using a simplified criterion for
gravitational-wave detection and a simple GW170817-calibrated model for the
kilonova peak magnitude, we determine the rate of kilonovae in reach of
follow-up campaigns and their distributions in magnitude for various bands. We
briefly consider the case of GW190425, the only binary neutron star merger
confirmed since GW170817, and obtain constraints on its inclination angle from
the non-detection of its kilonova, assuming the source was below the follow-up
thresholds. We also show that non-gravitational-wave-triggered kilonovae can be
a numerous class of sources in future surveys and briefly discuss associations
with short bright gamma-ray bursts. We finally discuss the detection of the
jetted outflow afterglow in addition to the kilonova.


Abstract:
  Narrow-band imaging surveys allow the study of the spectral characteristics
of galaxies without the need of performing their spectroscopic follow-up. In
this work, we forward-model the Physics of the Accelerating Universe Survey
(PAUS) narrow-band data. The aim is to improve the constraints on the spectral
coefficients used to create the galaxy spectral energy distributions (SED) of
the galaxy population model in Tortorelli et al. 2020. In that work, the model
parameters were inferred from the Canada-France-Hawaii Telescope Legacy Survey
(CFHTLS) data using Approximate Bayesian Computation (ABC). This led to
stringent constraints on the B-band galaxy luminosity function parameters, but
left the spectral coefficients only broadly constrained. To address that, we
perform an ABC inference using CFHTLS and PAUS data. This is the first time our
approach combining forward-modelling and ABC is applied simultaneously to
multiple datasets. We test the results of the ABC inference by comparing the
narrow-band magnitudes of the observed and simulated galaxies using Principal
Component Analysis, finding a very good agreement. Furthermore, we prove the
scientific potential of the constrained galaxy population model to provide
realistic stellar population properties by measuring them with the SED fitting
code \textsc{CIGALE}. We use CFHTLS broad-band and PAUS narrow-band photometry
for a flux-limited ($\mathrm{i}<22.5$) sample of galaxies up to redshift
$\mathrm{z \sim 0.8}$. We find that properties like stellar masses,
star-formation rates, mass-weighted stellar ages and metallicities are in
agreement within errors between observations and simulations. Overall, this
work shows the ability of our galaxy population model to correctly
forward-model a complex dataset such as PAUS and the ability to reproduce the
diversity of galaxy properties at the redshift range spanned by CFHTLS and
PAUS.


Abstract:
  We report the detection of multiple faint radio sources, that we identify as
AGN-jets, within CLJ1449+0856 at z=2 using 3 GHz VLA observations. We study the
effects of radio-jet based kinetic feedback at high redshifts, which has been
found to be crucial in low redshift clusters to explain the observed
thermodynamic properties of their ICM. We investigate this interaction at an
epoch featuring high levels of AGN activity and a transitional phase of ICM in
regards to the likelihood of residual cold-gas accretion. We measure a total
flux of $\rm 30.6 \pm 3.3~\mu Jy$ from the 6 detected jets. Their power
contribution is estimated to be $1.2 ~(\pm 0.6)~ \times 10^{44} ~\rm ergs~
s^{-1}$, although this value could be up to $4.7 ~ \times 10^{44} ~\rm ergs~
s^{-1}$. This is a factor $\sim 0.25 - 1.0$ of the previously estimated
instantaneous energy injection into the ICM of CLJ1449+0856 from AGN outflows
and star formation, that have already been found to be sufficient in globally
offsetting the cooling flows in the cluster core. In line with the already
detected abundance of star formation, this mode of feedback being distributed
over multiple sites, contrary to a single central source observed at low
redshifts, points to accretion of gas into the cluster centre. This also
suggests a 'steady state' of the cluster featuring non cool-core like
behaviour. Finally, we also examine the TIR-radio luminosity ratio for the
known sample of galaxies within the cluster core and find that dense
environments do not have any serious consequence on the compliance of galaxies
to the IR-radio correlation.


Abstract:
  In this work, we use the concept of quaternion time and demonstrate that it
can be applied for description of four-dimensional space-time intervals. We
demonstrate that the quaternion time interval together with the finite speed of
light propagation allow for a simple intuitive understanding of the time
interval measurement during arbitrary relative motion between a signal source
and observer. We derive a quaternion form of Lorentz time dilation and show
that the norm corresponds to the traditional expression of the Lorentz
transformation and represents the measured value of time intervals, making the
new theory inseparable from the theory of measurement. We determine that the
space-time interval in the observer reference frame is given by a conjugate
quaternion expression, which is essential for proper definition of the
quaternion derivatives in the observer reference frame. Then, we apply
quaternion differentiation to an arbitrary potential, which leads to
generalized Lorentz force. The second quaternion derivative of the potential
leads to expressions similar to generalized Maxwell equations. Finally, we
apply the resulting formalism to electromagnetic and gravitational interactions
and show that the new expressions are similar to the traditional expressions,
with the exception of additional terms, related to scalar fields, that need
further study and experimental verification. Therefore, the new mathematical
approach based on Hamilton's quaternions may serve as a useful foundation of
the unified theory of space-time and matter.


Abstract:
  This work investigates the importance of verification and validation (V&V) to
achieve predictive scale-resolving simulations (SRS) of turbulence, i.e.,
computations capable of resolving a fraction of the turbulent flow scales.
Toward this end, we present a novel but simple V&V strategy based on grid and
physical resolution refinement studies that can be used even when the exact
initial flow conditions are unknown, or reference data is unavailable. This is
particularly relevant for transient and transitional flow problems, as well as
for the improvement of turbulence models. We start by presenting a literature
survey of results obtained with distinct SRS models for flows past circular
cylinders. It confirms the importance of V&V by illustrating a large
variability of results, which is independent of the selected mathematical model
and Reynolds number. The proposed V&V strategy is then used on three
representative problems of practical interest. The results illustrate that it
is possible to conduct reliable verification and validation exercises with SRS
models, and evidence the importance of V&V to predictive SRS of turbulence. The
data also confirm the advantages of the proposed V\&V strategy.


Abstract:
  As narrow optical bandgap materials, semiconducting single-walled carbon
nanotubes (SWCNTs) are rarely regarded as charge donors in photoinduced
charge-transfer (PCT) reactions. However, the unique band structure and unusual
exciton dynamics of SWCNTs add more possibilities to the classical PCT
mechanism.In this work, we demonstrate PCT from photoexcited semiconducting
(6,5) SWCNTs to a wide-bandgap wrapping
poly-[(9,9-dioctylfluorenyl-2,7-diyl)-alt-(6,6')-(2,2'-bipyridine)] (PFO-BPy)
via femtosecond transient absorption spectroscopy. By monitoring the spectral
dynamics of the SWCNT polaron, we show that charge transfer from photoexcited
SWCNTs to PFO-BPy can be driven not only by the energetically favorable E33
transition but also by the energetically unfavorable E22 excitation under high
pump fluence. This unusual PCT from narrow-bandgap SWCNTs toward a wide-bandgap
polymer originates from the up-converted high-energy excitonic state (E33 or
higher) that is promoted by the Auger recombination of excitons and charge
carriers in SWCNTs. These insights provide new pathways for charge separation
in SWCNT-based photodetectors and photovoltaic cells.


Abstract:
  In this paper, we investigate whether overdensity formation via streaming
instability is consistent with recent multi-wavelength ALMA observations in the
Lupus star forming region. We simulate the local action of streaming
instability in 2D using the code ATHENA, and examine the radiative properties
at mm wavelengths of the resulting clumpy dust distribution by focusing on two
observable quantities: the optically thick fraction $ff$ (in ALMA band 6) and
the spectral index $\alpha$ (in bands 3-7). By comparing the simulated
distribution in the $ff-\alpha$ plane before and after the action of streaming
instability, we observe that clump formation causes $ff$ to drop, because of
the suppression of emission from grains that end up in optically thick clumps.
$\alpha$, instead, can either increase or decline after the action of streaming
instability; we use a simple toy model to demonstrate that this behaviour
depends on the sizes of the grains whose emission is suppressed by being
incorporated in optically thick clumps. In particular, the sign of evolution of
$\alpha$ depends on whether grains near the opacity maximum at a few tenths of
a mm end up in clumps. By comparing the simulation distributions before/after
clump formation to the data distribution, we note that the action of streaming
instability drives simulations towards the area of the plane where the data are
located. We furthermore demonstrate that this behaviour is replicated in
integrated disc models provided that the instability is operative over a region
of the disc that contributes significantly to the total mm flux.


Abstract:
  Platelet products are both expensive and have very short shelf lives. As
usage rates for platelets are highly variable, the effective management of
platelet demand and supply is very important yet challenging. The primary goal
of this paper is to present an efficient forecasting model for platelet demand
at Canadian Blood Services (CBS). To accomplish this goal, four different
demand forecasting methods, ARIMA (Auto Regressive Moving Average), Prophet,
lasso regression (least absolute shrinkage and selection operator) and LSTM
(Long Short-Term Memory) networks are utilized and evaluated. We use a large
clinical dataset for a centralized blood distribution centre for four hospitals
in Hamilton, Ontario, spanning from 2010 to 2018 and consisting of daily
platelet transfusions along with information such as the product
specifications, the recipients' characteristics, and the recipients' laboratory
test results. This study is the first to utilize different methods from
statistical time series models to data-driven regression and a machine learning
technique for platelet transfusion using clinical predictors and with different
amounts of data. We find that the multivariate approaches have the highest
accuracy in general, however, if sufficient data are available, a simpler time
series approach such as ARIMA appears to be sufficient. We also comment on the
approach to choose clinical indicators (inputs) for the multivariate models.


Abstract:
  We present a revised characterisation of the previously discovered transiting
planet systems HATS-34 and HATS-46. We make use of the newly available
space-based light curves from the NASA TESS mission and high-precision parallax
and absolute photometry measurements from the ESA Gaia mission to determine the
mass and radius of the planets and host stars with dramatically increased
precision and accuracy compared to published values, with the uncertainties in
some parameters reduced by as much as a factor of seven. Using an isochrone
based fit, for HATS-34 we measure a revised host star mass and radius of
$0.952_{-0.02}^{+0.04}M_S$ and of $0.9381\pm0.0080R_S$, respectively, and a
revised mass and radius for the transiting planet of $0.951\pm0.050 M_J$ and
$1.282 \pm0.064 R_J$ respectively. Similarly, for HATS-46 we measure a revised
mass and radius for the host star of $0.869\pm0.023M_S$, and $0.894\pm0.010
R_S$, respectively, and a revised mass and radius for the planet of $0.158
\pm0.042 M_J$, and $0.951 \pm 0.029 R_J$, respectively. The uncertainties that
we determine on the stellar and planetary masses and radii are also
substantially lower than re-determinations that incorporate the Gaia results
without performing a full re-analysis of the light curves and other
observational data. We argue that, in light of Gaia and TESS, a full
re-analysis of previously discovered transiting planets is warranted.


Abstract:
  We derive a novel framework called Bi-Directional Grid Constrained (BGC)
stochastic processes in which the further an Ito diffusion drifts away from the
origin, then the further it will be constrained. By making suitable
modifications to the Law of Iterated Logarithm (LIL), we derive a novel theorem
about the upper and lower bounds for BGC processes and their hidden barrier. To
visualize the theorem, we run many simulations of the Ito diffusions for a
representative expression for lambda(X, t), both before and after BGC and
uncover some interesting results with applications into finance and many other
areas.


Abstract:
  We present fully differential predictions for the production cross section of
a Higgs boson via the gluon fusion mechanism at next-to-next-to-next-to leading
order (N$^3$LO) in QCD perturbation theory. To perform our calculation we apply
the Projection-to-Born method for the first time to the calculation of the
non-factorising production of a colorless final state at the LHC at N$^3$LO. We
predict differential distributions for the two photon final state produced by
the decay of the Higgs boson and apply fiducial cuts on the photon rapidities
and momenta. The N$^3$LO corrections to these differential distributions have
complex features and are in part larger than the inclusive N$^3$LO corrections
to the production cross section. Overall, we observe that the inclusion of the
N$^3$LO QCD corrections significantly reduces the perturbative uncertainties
and leads to a stabilisation of the perturbative expansion.


Abstract:
  In this contribution I present results achieved recently in the field of the
OT forecast that push further the limit of the accuracy of the OT forecasts and
open to new perspectives in this field.


Abstract:
  Nonradiative processes limit optoelectronic functionality of nanocrystals and
curb their device performance. Nevertheless, the dynamic structural origins of
nonradiative relaxations in nanocrystals are not understood. Here, femtosecond
electron diffraction measurements corroborated by atomistic simulations uncover
transient lattice deformations accompanying radiationless electronic processes
in semiconductor nanocrystals. Investigation of the excitation energy
dependence shows that hot carriers created by a photon energy considerably
larger than the bandgap induce structural distortions at nanocrystal surfaces
on few picosecond timescales associated with the localization of trapped holes.
On the other hand, carriers created by a photon energy close to the bandgap
result in transient lattice heating that occurs on a much longer 200 ps
timescale, governed by an Auger heating mechanism. Elucidation of the
structural deformations associated with the surface trapping of hot holes
provides atomic-scale insights into the mechanisms deteriorating optoelectronic
performance and a pathway towards minimizing these losses in nanocrystal
devices.


Abstract:
  The 2020 NBA playoffs were played inside of a bubble in Disney World because
of the COVID-19 pandemic. This meant that there were no fans in attendance,
games played on neutral courts and no traveling for teams, which in theory
removes home-court advantage from the games. This setting has attracted much
discussion as analysts and fans debated the possible effects it may have on the
outcome of games. Home-court advantage has historically played an influential
role in NBA playoff series outcomes. The 2020 playoff provided a unique
opportunity to study the effects of the bubble and home-court advantage by
comparing the 2020 season with the seasons in the past. While many factors
contribute to the outcome of games, points scored is the deciding factor of who
wins games, so scoring is the primary focus of this study. The specific
measures of interest are team scoring totals and team shooting percentage on
two-pointers, three-pointers, and free throws. Comparing these measures for
home teams and away teams in 2020 vs. 2017-2019 shows that the 2020 playoffs
favored away teams more than usual, particularly with two point shooting and
total scoring.


Abstract:
  Word segmentation and part-of-speech tagging are two critical preliminary
steps for downstream tasks in Vietnamese natural language processing. In
reality, people tend to consider also the phrase boundary when performing word
segmentation and part of speech tagging rather than solely process word by word
from left to right. In this paper, we implement this idea to improve word
segmentation and part of speech tagging the Vietnamese language by employing a
simplified constituency parser. Our neural model for joint word segmentation
and part-of-speech tagging has the architecture of the syllable-based CRF
constituency parser. To reduce the complexity of parsing, we replace all
constituent labels with a single label indicating for phrases. This model can
be augmented with predicted word boundary and part-of-speech tags by other
tools. Because Vietnamese and Chinese have some similar linguistic phenomena,
we evaluated the proposed model and its augmented versions on three Vietnamese
benchmark datasets and six Chinese benchmark datasets. Our experimental results
show that the proposed model achieves higher performances than previous works
for both languages.


Abstract:
  It has been recently reported that classical systems have speed limit for
state evolution, although such a concept of speed limit had been considered to
be unique to quantum systems. Owing to the speed limit for classical system,
the lower bound for calculation time of simulated annealing with desired
calculation accuracy can be derived. However, such a lower bound does not work
as a criterion for completion of calculation in a practical time. In this
paper, we derive an inequality for classical system analogous to the quantum
adiabatic theorem that gives calculation time for an accuracy-guaranteed
fluctuation-exploiting computation. The trade-off relation between calculation
time and accuracy is given in the form tolerable in practical use.


Abstract:
  Cancer is responsible for millions of deaths worldwide every year. Although
significant progress has been achieved in cancer medicine, many issues remain
to be addressed for improving cancer therapy. Appropriate cancer patient
stratification is the prerequisite for selecting appropriate treatment plan, as
cancer patients are of known heterogeneous genetic make-ups and phenotypic
differences. In this study, built upon deep phenotypic characterizations
extractable from Mayo Clinic electronic health records (EHRs) and genetic test
reports for a collection of cancer patients, we developed a system leveraging a
joint of phenotypic and genetic features for cancer patient subgrouping.
  The workflow is roughly divided into three parts: feature preprocessing,
cancer patient classification, and cancer patient clustering based. In feature
preprocessing step, we performed filtering, retaining the most relevant
features. In cancer patient classification, we utilized joint categorical
features to build a patient-feature matrix and applied nine different machine
learning models, Random Forests (RF), Decision Tree (DT), Support Vector
Machine (SVM), Naive Bayes (NB), Logistic Regression (LR), Multilayer
Perceptron (MLP), Gradient Boosting (GB), Convolutional Neural Network (CNN),
and Feedforward Neural Network (FNN), for classification purposes. Finally, in
the cancer patient clustering step, we leveraged joint embeddings features and
patient-feature associations to build an undirected feature graph and then
trained the cancer feature node embeddings.


Abstract:
  Geolocated time series, i.e., time series associated with certain locations,
abound in many modern applications. In this paper, we consider hybrid queries
for retrieving geolocated time series based on filters that combine spatial
distance and time series similarity. For the latter, unlike existing work, we
allow filtering based on local similarity, which is computed based on
subsequences rather than the entire length of each series, thus allowing the
discovery of more fine-grained trends and patterns. To efficiently support such
queries, we first leverage the state-of-the-art BTSR-tree index, which utilizes
bounds over both the locations and the shapes of time series to prune the
search space. Moreover, we propose optimizations that check at specific
timestamps to identify candidate time series that may exceed the required local
similarity threshold. To further increase pruning power, we introduce the
SBTSR-tree index, an extension to BTSR-tree, which additionally segments the
time series temporally, allowing the construction of tighter bounds. Our
experimental results on several real-world datasets demonstrate that SBTSR-tree
can provide answers much faster for all examined query types. This paper has
been published in the 27th International Conference on Advances in Geographic
Information Systems (ACM SIGSPATIAL 2019).


Abstract:
  The secondary component of GW190814 with a mass of 2.50-2.67 $M_{\odot}$ may
be the lightest black hole or the heaviest neutron star ever observed in a
binary compact object system. To explore the possible equation of state (EOS),
which can support such massive neutron star, we apply the relativistic
mean-field model with a density-dependent isovector coupling constant to
describe the neutron-star matter. The acceptable EOS should satisfy some
constraints: the EOS model can provide a satisfactory description of the
nuclei; the maximum mass $M_\textrm{TOV}$ is above 2.6 $M_{\odot}$; the tidal
deformability of a canonical 1.4 $M_{\odot}$ neutron star $\Lambda_{1.4}$
should lie in the constrained range from GW170817. In this paper, we find that
the nuclear symmetry energy and its density dependence play a crucial role in
determining the EOS of neutron-star matter. The constraints from the mass of
2.6 $M_{\odot}$ and the tidal deformability $\Lambda_{1.4}=616_{-158}^{+273}$
(based on the assumption that GW190814 is a neutron star-black hole binary) can
be satisfied as the slope of symmetry energy $L \leq 50$ MeV. Even including
the constraint of $\Lambda_{1.4}=190_{-120}^{+390}$ from GW170817 which
suppresses the EOS stiffness at low density, the possibility that the secondary
component of GW190814 is a massive neutron star cannot be excluded in this
study.


Abstract:
  Existing video stabilization methods often generate visible distortion or
require aggressive cropping of frame boundaries, resulting in smaller field of
views. In this work, we present a frame synthesis algorithm to achieve
full-frame video stabilization. We first estimate dense warp fields from
neighboring frames and then synthesize the stabilized frame by fusing the
warped contents. Our core technical novelty lies in the learning-based
hybrid-space fusion that alleviates artifacts caused by optical flow inaccuracy
and fast-moving objects. We validate the effectiveness of our method on the
NUS, selfie, and DeepStab video datasets. Extensive experiment results
demonstrate the merits of our approach over prior video stabilization methods.


Abstract:
  In this paper we publish the largest identity-annotated Holstein-Friesian
cattle dataset Cows2021 and a first self-supervision framework for video
identification of individual animals. The dataset contains 10,402 RGB images
with labels for localisation and identity as well as 301 videos from the same
herd. The data shows top-down in-barn imagery, which captures the breed's
individually distinctive black and white coat pattern. Motivated by the
labelling burden involved in constructing visual cattle identification systems,
we propose exploiting the temporal coat pattern appearance across videos as a
self-supervision signal for animal identity learning. Using an
individual-agnostic cattle detector that yields oriented bounding-boxes,
rotation-normalised tracklets of individuals are formed via
tracking-by-detection and enriched via augmentations. This produces a
`positive' sample set per tracklet, which is paired against a `negative' set
sampled from random cattle of other videos. Frame-triplet contrastive learning
is then employed to construct a metric latent space. The fitting of a Gaussian
Mixture Model to this space yields a cattle identity classifier. Results show
an accuracy of Top-1 57.0% and Top-4: 76.9% and an Adjusted Rand Index: 0.53
compared to the ground truth. Whilst supervised training surpasses this
benchmark by a large margin, we conclude that self-supervision can nevertheless
play a highly effective role in speeding up labelling efforts when initially
constructing supervision information. We provide all data and full source code
alongside an analysis and evaluation of the system.


Abstract:
  Unsupervised person re-identification (re-ID) aims at closing the performance
gap to supervised methods. These methods build reliable relationship between
data points while learning representations. However, we empirically show that
the reason why they are successful is not only their label generation
mechanisms, but also their unexplored designs. By studying two unsupervised
person re-ID methods in a cross-method way, we point out a hard negative
problem is handled implicitly by their designs of data augmentations and PK
sampler respectively. In this paper, we find another simple solution for the
problem, i.e., taking more positives during training, by which we generate
pseudo-labels and update models in an iterative manner. Based on our findings,
we propose a contrastive learning method without a memory back for unsupervised
person re-ID. Our method works well on benchmark datasets and outperforms the
state-of-the-art methods. Code will be made available.


Abstract:
  Generative Adversarial Networks (GANs) have recently attracted considerable
attention in the AI community due to its ability to generate high-quality data
of significant statistical resemblance to real data. Fundamentally, GAN is a
game between two neural networks trained in an adversarial manner to reach a
zero-sum Nash equilibrium profile. Despite the improvement accomplished in GANs
in the last few years, several issues remain to be solved. This paper reviews
the literature on the game theoretic aspects of GANs and addresses how game
theory models can address specific challenges of generative model and improve
the GAN's performance. We first present some preliminaries, including the basic
GAN model and some game theory background. We then present taxonomy to classify
state-of-the-art solutions into three main categories: modified game models,
modified architectures, and modified learning methods. The classification is
based on modifications made to the basic GAN model by proposed game-theoretic
approaches in the literature. We then explore the objectives of each category
and discuss recent works in each category. Finally, we discuss the remaining
challenges in this field and present future research directions.


Abstract:
  The impact of individual scientists is commonly quantified using
citation-based measures. The most common such measure is the h-index. A
scientist's h-index affects hiring, promotion, and funding decisions, and thus
shapes the progress of science. Here we report a large-scale study of
scientometric measures, analyzing millions of articles and hundreds of millions
of citations across four scientific fields and two data platforms. We find that
the correlation of the h-index with awards that indicate recognition by the
scientific community has substantially declined. These trends are associated
with changing authorship patterns. We show that these declines can be mitigated
by fractional allocation of citations among authors, which has been discussed
in the literature but not implemented at scale. We find that a fractional
analogue of the h-index outperforms other measures as a correlate and predictor
of scientific awards. Our results suggest that the use of the h-index in
ranking scientists should be reconsidered, and that fractional allocation
measures such as h-frac provide more robust alternatives. An interactive
visualization of our work can be found at https://h-frac.org


Abstract:
  Unsupervised domain adaptation (UDA) aims to learn models for a target domain
of unlabeled data by transferring knowledge from a labeled source domain. In
the traditional UDA setting, labeled source data are assumed to be available
for adaptation. Due to increasing concerns for data privacy, source-free UDA is
highly appreciated as a new UDA setting, where only a trained source model is
assumed to be available, while labeled source data remain private. However,
trained source models may also be unavailable in practice since source models
may have commercial values and exposing source models brings risks to the
source domain, e.g., problems of model misuse and white-box attacks. In this
work, we study a subtly different setting, named Black-Box Unsupervised Domain
Adaptation (B$^2$UDA), where only the application programming interface of
source model is accessible to the target domain; in other words, the source
model itself is kept as a black-box one. To tackle B$^2$UDA, we propose a
simple yet effective method, termed Iterative Learning with Noisy Labels
(IterLNL). With black-box models as tools of noisy labeling, IterLNL conducts
noisy labeling and learning with noisy labels (LNL), iteratively. To facilitate
the implementation of LNL in B$^2$UDA, we estimate the noise rate from model
predictions of unlabeled target data and propose category-wise sampling to
tackle the unbalanced label noise among categories. Experiments on benchmark
datasets show the efficacy of IterLNL. Given neither source data nor source
models, IterLNL performs comparably with traditional UDA methods that make full
use of labeled source data.


Abstract:
  For machine learning systems to be reliable, we must understand their
performance in unseen, out-of-distribution environments. In this paper, we
empirically show that out-of-distribution performance is strongly correlated
with in-distribution performance for a wide range of models and distribution
shifts. Specifically, we demonstrate strong correlations between
in-distribution and out-of-distribution performance on variants of CIFAR-10 &
ImageNet, a synthetic pose estimation task derived from YCB objects, satellite
imagery classification in FMoW-WILDS, and wildlife classification in
iWildCam-WILDS. The strong correlations hold across model architectures,
hyperparameters, training set size, and training duration, and are more precise
than what is expected from existing domain adaptation theory. To complete the
picture, we also investigate cases where the correlation is weaker, for
instance some synthetic distribution shifts from CIFAR-10-C and the tissue
classification dataset Camelyon17-WILDS. Finally, we provide a candidate theory
based on a Gaussian data model that shows how changes in the data covariance
arising from distribution shift can affect the observed correlations.


Abstract:
  The China Spallation Neutron Source (CSNS) operates in pulsed mode and has a
high neutron flux. This provides opportunities for energy resolved neutron
imaging by using the TOF (Time Of Flight) approach. An Energy resolved neutron
imaging instrument (ERNI) is being built at the CSNS but significant challenges
for the detector persist because it simultaneously requires a spatial
resolution of less than 100 {\mu}m, as well as a microsecond-scale timing
resolution. This study constructs a prototype of an energy resolved neutron
imaging detector based on the fast optical camera, TPX3Cam coupled with an
image intensifier. To evaluate its performance, a series of proof of principle
experiments were performed in the BL20 at the CSNS to measure the spatial
resolution and the neutron wavelength spectrum, and perform neutron imaging
with sliced wavelengths and Bragg edge imaging of the steel sample. A spatial
resolution of 57 {\mu}m was obtained for neutron imaging by using the
centroiding algorithm, the timing resolution was on the microsecond scale and
the measured wavelength spectrum was identical to that measured by a beam
monitor. In addition, any wavelengths can be selected for the neutron imaging
of the given object, and the detector can be used for Bragg edge imaging. The
results show that our detector has good performances and can satisfy the
requirements of ERNI for detectors at the CSNS


Abstract:
  The main result of the paper is the first polynomial-time algorithm for
ranking bracelets. The time-complexity of the algorithm is O(k^2 n^4), where k
is the size of the alphabet and n is the length of the considered bracelets.
The key part of the algorithm is to compute the rank of any word with respect
to the set of bracelets by finding three other ranks: the rank over all
necklaces, the rank over palindromic necklaces, and the rank over enclosing
apalindromic necklaces. The last two concepts are introduced in this paper.
These ranks are key components to our algorithm in order to decompose the
problem into parts. Additionally, this ranking procedure is used to build a
polynomial-time unranking algorithm.


Abstract:
  In this paper, we propose a progressive margin loss (PML) approach for
unconstrained facial age classification. Conventional methods make strong
assumption on that each class owns adequate instances to outline its data
distribution, likely leading to bias prediction where the training samples are
sparse across age classes. Instead, our PML aims to adaptively refine the age
label pattern by enforcing a couple of margins, which fully takes in the
in-between discrepancy of the intra-class variance, inter-class variance and
class center. Our PML typically incorporates with the ordinal margin and the
variational margin, simultaneously plugging in the globally-tuned deep neural
network paradigm. More specifically, the ordinal margin learns to exploit the
correlated relationship of the real-world age labels. Accordingly, the
variational margin is leveraged to minimize the influence of head classes that
misleads the prediction of tailed samples. Moreover, our optimization carefully
seeks a series of indicator curricula to achieve robust and efficient model
training. Extensive experimental results on three face aging datasets
demonstrate that our PML achieves compelling performance compared to state of
the arts. Code will be made publicly.


Abstract:
  Graph states are a central resource in measurement-based quantum information
processing. In the photonic qubit architecture based on
Gottesman-Kitaev-Preskill (GKP) encoding, the generation of high-fidelity graph
states composed of realistic, finite-energy approximate GKP-encoded qubits thus
constitutes a key task. We consider the finite-energy approximation of GKP
qubit states given by a coherent superposition of shifted finite-squeezed
vacuum states, where the displacements are Gaussian distributed. We present an
exact description of graph states composed of such approximate GKP qubits as a
coherent superposition of a Gaussian ensemble of randomly displaced ideal
GKP-qubit graph states. We determine the transformation rules for the
covariance matrix and the mean displacement vector of the Gaussian distribution
of the ensemble under tools such as GKP-Steane error correction and fusion
operations that can be used to grow large, high-fidelity GKP-qubit graph
states. The former captures the noise in the graph state due to the
finite-energy approximation of GKP qubits, while the latter relates to the
possible absolute displacement errors on the individual qubits due to the
homodyne measurements that are a part of these tools. The rules thus help in
pinning down an exact coherent error model for graph states generated from
truly finite-energy GKP qubits, which can shed light on their error correction
properties.


Abstract:
  Rapid solidification experiments on thin film aluminum samples reveal the
presence of lattice orientation gradients within crystallizing grains. To study
this phenomenon, a single-component phase-field crystal (PFC) model that
captures the properties of solid, liquid, and vapor phases is proposed to model
pure aluminium quantitatively. A coarse-grained amplitude representation of
this model is used to simulate solidification in samples approaching micrometer
scales. The simulations reproduce the experimentally observed orientation
gradients within crystallizing grains when grown at experimentally relevant
rapid quenches. We propose a causal connection between formation of defects and
orientation gradients.


Abstract:
  It is well known that active galactic nuclei (AGN) show various forms of
interaction with their host galaxy, in a number of phenomena generally called
AGN feedback. In particular, the relativistic plasma jets launched by a
fraction of AGN can strongly affect their environment. We present here a study
of the [O III] $\lambda\lambda$4959,5007 lines in a diverse sample of early
evolution stage AGN -- specifically narrow-line Seyfert 1 galaxies. Radio
imaging observations of all of the sources enable a division to jetted and
non-jetted sources, and exploiting this we show that the ionized gas properties
are significantly influenced by the presence of the jets, as we often find the
[O III] lines (blue-)shifted with respect to their restframe wavelength. We
also show how the radio morphology and the radio spectral index do not seem to
play a role in the origin of the [O III] shifts, thus suggesting that the
source inclination is not relevant to the lines displacement. We do not find a
strong relation between the [O III] line properties and the bolometric
luminosity, suggesting that within our sample radiatively driven outflows do
not seem to have a significant contribution to the [O III] line kinematics. We
finally suggest that [O~ III] shifts may be a good proxy to identify the
presence of relativistic jets. Additional studies, especially with
integral-field spectroscopy, will provide a deeper insight into the relation
between jets and their environment in early evolution stage AGN.


Abstract:
  This paper presents a novel storage aware routing (STAR) protocol designed to
provide a general networking solution over a broad range of wired and wireless
usage scenarios. STAR enables routing policies which adapt seamlessly from a
well-connected wired network to a disconnected wireless network. STAR uses a
2-Dimensional routing metric composed of a short and a long term route cost and
storage availability on downstream routers to make store or forward routing
decisions. Temporary in-network storage is preferred over forwarding along a
path that is slower than average and opportunistic transmission is encouraged
when faster than average routes become available. Results from ns2 based
simulations show that STAR achieves $40-50\%$ higher throughput compared to
OLSR in mobile vehicular and DTN scenarios and does $12-20\%$ better than OLSR
in the static mesh case. Experimental evaluation of STAR on the ORBIT testbed
validates the protocol implementation, and demonstrates significant performance
improvements with 25\% higher peak throughput compared to OLSR in a wireless
mesh network.


Abstract:
  Several early transition metal dipnictides have been found to host
topological semimetal states and exhibit large magnetoresistance. In this
study, we use angle-resolved photoemission spectroscopy (ARPES) and
magneto-transport to study the electronic properties of a new transition metal
dipnictide ZrP$_2$. We find that ZrP$_2$ exhibits an extremely large and
unsaturated magnetoresistance of up to 40,000 % at 2 K, which originates from
an almost perfect electron-hole compensation. Our band structure calculations
further show that ZrP$_2$ hosts a topological nodal loop in proximity to the
Fermi level. Based on the ARPES measurements, we confirm the results of our
calculations and determine the surface band structure. Our study establishes
ZrP$_2$ as a new platform to investigate near-perfect electron-hole
compensation and its interplay with topological band structures.


Abstract:
  We present a kinetic version of the Watanabe-Strogatz(WS) transform for
vector models in this paper. From the generalized WS-transform, we obtain the
cross-ratio type constant of motion functionals for kinetic vector models under
suitable conditions. We present the sufficient and necessary conditions for the
existence of the suggested constant of motion functional. As an application of
the constant of motion functional, we provide the instability of bipolar states
of the kinetic swarm sphere model. We also provide the WS-transform and
constant of motion functional for non-identical kinetic vector models.


Abstract:
  THESEUS is a medium size space mission of the European Space Agency,
currently under evaluation for a possible launch in 2032. Its main objectives
are to investigate the early Universe through the observation of gamma-ray
bursts and to study the gravitational waves electromagnetic counterparts and
neutrino events. On the other hand, its instruments, which include a wide field
of view X-ray (0.3-5 keV) telescope based on lobster-eye focusing optics and a
gamma-ray spectrometer with imaging capabilities in the 2-150 keV range, are
also ideal for carrying out unprecedented studies in time domain astrophysics.
In addition, the presence onboard of a 70 cm near infrared telescope will allow
simultaneous multi-wavelegth studies. Here we present the THESEUS capabilities
for studying the time variability of different classes of sources in parallel
to, and without affecting, the gamma-ray bursts hunt.


Abstract:
  The paper considers a boundary value problem for the high-order
Lavrent'ev-Bitsadze equation. Necessary and sufficient conditions for the
uniqueness of the solution are found. When substantiating the existence, the
problem of "small denominators" arises. Sufficient conditions for the
separability of the "small denominator" from zero are found. An example of a
boundary value problem not solvable by the Fourier method is given, in the
cases rectangle with integer side measurements.