title:
Short Paper: Device- and Locality-Specific Fingerprinting of Shared NISQ
  Quantum Computers

abstract:
  Fingerprinting of quantum computer devices is a new threat that poses a
challenge to shared, cloud-based quantum computers. Fingerprinting can allow
adversaries to map quantum computer infrastructures, uniquely identify
cloud-based devices which otherwise have no public identifiers, and it can
assist other adversarial attacks. This work shows idle tomography-based
fingerprinting method based on crosstalk-induced errors in NISQ quantum
computers. The device- and locality-specific fingerprinting results show
prediction accuracy values of $99.1\%$ and $95.3\%$, respectively.


title:
Evaluating forecasts for high-impact events using transformed kernel
  scores

abstract:
  It is informative to evaluate a forecaster's ability to predict outcomes that
have a large impact on the forecast user. Although weighted scoring rules have
become a well-established tool to achieve this, such scores have been studied
almost exclusively in the univariate case, with interest typically placed on
extreme events. However, a large impact may also result from events not
considered to be extreme from a statistical perspective: the interaction of
several moderate events could also generate a high impact. Compound weather
events provide a good example of this. To assess forecasts made for high-impact
events, this work extends existing results on weighted scoring rules by
introducing weighted multivariate scores. To do so, we utilise kernel scores.
We demonstrate that the threshold-weighted continuous ranked probability score
(twCRPS), arguably the most well-known weighted scoring rule, is a kernel
score. This result leads to a convenient representation of the twCRPS when the
forecast is an ensemble, and also permits a generalisation that can be employed
with alternative kernels, allowing us to introduce, for example, a
threshold-weighted energy score and threshold-weighted variogram score. To
illustrate the additional information that these weighted multivariate scoring
rules provide, results are presented for a case study in which the weighted
scores are used to evaluate daily precipitation accumulation forecasts, with
particular interest on events that could lead to flooding.


title: The emerging commercial landscape of quantum computing

abstract:
  Quantum computing technologies are advancing, and the class of addressable
problems is expanding. Together with the emergence of new ventures and
government-sponsored partnerships, these trends will help lower the barrier for
new technology adoption and provide stability in an uncertain market. Until
then, quantum computing presents an exciting testbed for different strategies
in an emerging market.


title: Do Some Virtual Bound States Carry Torsion Trace?

abstract:
  This article presents theoretical arguments that certain virtual bound states
carry the trace component of affine torsion. The motivation for this work is
that Einstein Cartan theory, which extends general relativity by including
torsion to model intrinsic angular momentum, is becoming more credible. The
author is not aware of any situation for which there is evidence or substantial
argument for the presence of torsion trace, except in the continuum theory of
edge dislocations in crystals. The main evidence for the hypothesis consists of
analogies between the structure of virtual bound states and (a) geometry of
dislocations in crystal lattices, which are modeled with torsion; and (b)
modeling of intrinsic angular momentum by torsion in Einstein Cartan theory and
the theory of microelasticity. The work focuses on conjectured presence of
torsion in para-positronium, which intermediates annihilation of an electron
and a positron with opposite z spins. If the virtual bound state carries
torsion, then the local law of conservation of angular momentum can hold over
the spacelike separation during annihilation.


title:
On a new LISA dynamics feedback control scheme: Common-mode isolation of
  test mass control and probes of test-mass acceleration

abstract:
  The Drag-Free and Attitude Control System is a central element of LISA
technology, ensuring the very high dynamic stability of spacecraft and test
masses required in order to reach the sensitivity that gravitational wave
astronomy in space requires. Applying electrostatic forces on test-masses is
unavoidable but should be restricted to the minimum necessary to keep the
spacecraft-test masses system in place, while granting the optimal quality of
test-mass free-fall. To realise this, we propose a new test-mass suspension
scheme that applies forces and torques only in proportion to any differential
test mass motion observed, and we demonstrate that the new scheme significantly
mitigates the amount of suspension forces and torques needed to control the
whole system. The mathematical method involved allows us to derive a new
observable measuring the differential acceleration of test masses projected on
the relevant sensitive axes, which will have important consequences for LISA
data calibration, processing and analysis.


title:
Learning-Based Fault-Tolerant Control for an Hexarotor with Model
  Uncertainty

abstract:
  In this paper we present a learning-based tracking controller based on
Gaussian processes (GP) for a fault-tolerant hexarotor in a recovery maneuver.
In particular, to estimate certain uncertainties that appear in a hexacopter
vehicle with the ability to reconfigure its rotors to compensate for failures.
The rotors reconfiguration introduces disturbances that make the dynamic model
of the vehicle differ from the nominal model. The control algorithm is designed
to learn and compensate the amount of modeling uncertainties after a failure in
the control allocation reconfiguration by using GP as a learning-based model
for the predictions. In particular the presented approach guarantees a
probabilistic bounded tracking error with high probability. The performance of
the learning-based fault-tolerant controller is evaluated through experimental
tests with an hexarotor UAV.


title: Alpha-NML Universal Predictors

abstract:
  Inspired by Sibson's alpha-mutual information, we introduce a new class of
universal predictors that depend on a real parameter greater than one. This
class interpolates two well-known predictors, the mixture estimator, that
includes the Laplace and the Krichevsky-Trofimov predictors, and the Normalized
Maximum Likelihood (NML) estimator. We point out some advantages of this class
of predictors and study its performance in terms of known regret measures under
logarithmic loss, in particular for the well-studied case of discrete
memoryless sources.


title:
Sensitivity limits on the weak dipole moments of the top-quark at the
  Bestest Little Higgs Model

abstract:
  We presented new researches for the sensitivity limits on the weak dipole
moments of the top quark-$Z$ boson interactions in the context of the Bestest
Little Higgs Model (BLHM). For this purpose, we derive the corresponding
Feynman rules. Among the new contributions, there are those arising from the
vertices of scalar bosons, vector bosons and heavy quarks contribution:
$tq_iS_i$, ($S_i=h_0, H_0, A_0, \phi^{0}, \eta^{0}, \sigma, H^{\pm},
\phi^{\pm}, \eta^{\pm}$); $tq_iV_i$, ($V_i= \gamma, Z, W, Z', W'$); and
$Zq_i\bar q_i$ ($q_i=b, t, B, T, T_5, T_6, T^{2/3}, T^{5/3}$). With these new
vertices, we calculate the one-loop contributions to the weak dipole moments
$a^W_t$ and $d^W_t$ of the top-quark in several scenarios. We found that with
the parameters of the BLHM of $m_{A_0}= 1000$ GeV, $m_{\eta^0}= 1000$ GeV,
$F=4000$ GeV, $f=[1000, 3000]$ GeV and $\tan\beta=3$, the $a^W_t$ reaches
values with a sensitivity of $a^W_t=-2.36 \times 10^{-3}-1.70 \times 10^{-6}i$,
$-1.05 \times 10^{-3}-1.28 \times 10^{-6}i$, where the main contribution comes
from the scalars $h_0$ and $\eta_0$, which couples to the top-quark of the
Standard Model and the top quarks of the BLHM. These values seem to be within
of the reach of the expected experimental sensitivity of future experiments.
Our study complements other studies at the level of one-loop with an extended
scalar sector.


title: Ultra-high-linearity integrated lithium niobate electro-optic modulators

abstract:
  Integrated lithium niobate (LN) photonics is a promising platform for future
chip-scale microwave photonics systems owing to its unique electro-optic
properties, low optical loss and excellent scalability. A key enabler for such
systems is a highly linear electro-optic modulator that could faithfully covert
analog electrical signals into optical signals. In this work, we demonstrate a
monolithic integrated LN modulator with an ultrahigh spurious-free dynamic
range (SFDR) of 120.04 dB Hz4/5 at 1 GHz, using a ring-assisted Mach-Zehnder
interferometer configuration. The excellent synergy between the intrinsically
linear electro-optic response of LN and an optimized linearization strategy
allows us to fully suppress the cubic terms of third-order intermodulation
distortions (IMD3) without active feedback controls, leading to ~ 20 dB
improvement over previous results in the thin-film LN platform. Our
ultra-high-linearity LN modulators could become a core building block for
future large-scale functional microwave photonic integrated circuits, by
further integration with other high-performance components like low-loss delay
lines, tunable filters and phase shifters available on the LN platform.


title:
A note on Cops and Robbers, independence number, domination number and
  diameter

abstract:
  We study relations between diameter $D(G)$, domination number $\gamma(G)$,
independence number $\alpha(G)$ and cop number $c(G)$ of a connected graph $G$,
showing (i.) $c(G) \leq \alpha(G)-\lfloor \frac{D(G)-3}{2} \rfloor$, and (ii.)
$c(G) \leq \gamma (G) - \frac{D(G)}{3} + O (\sqrt{D(G)})$.


title:
On rectifiable measures in Carnot groups: Marstrand-Mattila
  rectifiability criterion

abstract:
  In this paper we continue the study of the notion of
$\mathscr{P}$-rectifiability in Carnot groups. We say that a Radon measure is
$\mathscr{P}_h$-rectifiable, for $h\in\mathbb N$, if it has positive $h$-lower
density and finite $h$-upper density almost everywhere, and, at almost every
point, it admits a unique tangent measure up to multiples. In this paper we
prove a Marstrand--Mattila rectifiability criterion in arbitrary Carnot groups
for $\mathscr{P}$-rectifiable measures with tangent planes that admit a normal
complementary subgroup. Namely, in this co-normal case, even if a priori the
tangent planes at a point might not be the same at different scales, a
posteriori the measure has a unique tangent almost everywhere.
  Since every horizontal subgroup of a Carnot group has a normal complement,
our criterion applies in the particular case in which the tangents are
one-dimensional horizontal subgroups. Hence, as an immediate consequence of our
Marstrand--Mattila rectifiability criterion and a result of
Chousionis--Magnani--Tyson, we obtain the one-dimensional Preiss's theorem in
the first Heisenberg group $\mathbb H^1$. More precisely, we show that a Radon
measure $\phi$ on $\mathbb H^1$ with positive and finite one-density with
respect to the Koranyi distance is absolutely continuous with respect to the
one-dimensional Hausdorff measure $\mathcal{H}^1$, and it is supported on a
one-rectifiable set in the sense of Federer, i.e., it is supported on the
countable union of the images of Lipschitz maps from $A\subseteq \mathbb R$ to
$\mathbb H^1$.


title:
Learning Relative Return Policies With Upside-Down Reinforcement
  Learning

abstract:
  Lately, there has been a resurgence of interest in using supervised learning
to solve reinforcement learning problems. Recent work in this area has largely
focused on learning command-conditioned policies. We investigate the potential
of one such method -- upside-down reinforcement learning -- to work with
commands that specify a desired relationship between some scalar value and the
observed return. We show that upside-down reinforcement learning can learn to
carry out such commands online in a tabular bandit setting and in CartPole with
non-linear function approximation. By doing so, we demonstrate the power of
this family of methods and open the way for their practical use under more
complicated command structures.


title: Negative Newton constant may destroy some conjectures

abstract:
  The magnitude and sign of the gravitational coupling $1/G$ depend on the
relations between different contributions from scalar, fermionic and vector
fields. In principle, this may give the zero and negative values of $1/G$ in
some hypothetical Universes with the proper relations between the fermionic and
bosonic species. We consider different conjectures related to gravity, such as
the wave function collapse caused by gravity, entropic gravity, and maximum
force. We find that some of them do not work in the Universes with zero or
negative $1/G$, and thus cannot be considered as universal. Thus the extension
of the gravitational coupling to $1/G \leq 0$ provides the test on the
universality of the proposed theories of gravity.


title: A Lyapunov function for robust stability of moving horizon estimation

abstract:
  We provide a novel robust stability analysis for moving horizon estimation
(MHE) using a Lyapunov function. Additionally, we introduce linear matrix
inequalities (LMIs) to verify the necessary incremental input/output-to-state
stability ($\delta$-IOSS) detectability condition. We consider an MHE
formulation with time-discounted quadratic objective for nonlinear systems
admitting an exponential $\delta$-IOSS Lyapunov function. We show that with a
suitable parameterization of the MHE objective, the $\delta$-IOSS Lyapunov
function serves as an $M$-step Lyapunov function for MHE. Provided that the
estimation horizon is chosen large enough, this directly implies exponential
stability of MHE. The stability analysis is also applicable to full information
estimation, where the restriction to exponential $\delta$-IOSS can be relaxed.
Moreover, we provide simple LMI conditions to systematically derive
$\delta$-IOSS Lyapunov functions, which allows us to easily verify
$\delta$-IOSS for a large class of nonlinear detectable systems. This is useful
in the context of MHE in general, since most of the existing nonlinear (robust)
stability results for MHE depend on the system being $\delta$-IOSS
(detectable). In combination, we thus provide sufficient conditions that
directly provide a framework to design MHE schemes with guaranteed robust
exponential stability. The applicability of the overall MHE design is
demonstrated with a nonlinear chemical reactor process.


title:
Delta family approach for the stochastic control problems of utility
  maximization

abstract:
  In this paper, we propose a new approach for stochastic control problems
arising from utility maximization. The main idea is to directly start from the
dynamical programming equation and compute the conditional expectation using a
novel representation of the conditional density function through the Dirac
Delta function and the corresponding series representation. We obtain an
explicit series representation of the value function, whose coefficients are
expressed through integration of the value function at a later time point
against a chosen basis function. Thus we are able to set up a recursive
integration time-stepping scheme to compute the optimal value function given
the known terminal condition, e.g. utility function. Due to tensor
decomposition property of the Dirac Delta function in high dimensions, it is
straightforward to extend our approach to solving high-dimensional stochastic
control problems. The backward recursive nature of the method also allows for
solving stochastic control and stopping problems, i.e. mixed control problems.
We illustrate the method through solving some two-dimensional stochastic
control (and stopping) problems, including the case under the classical and
rough Heston stochastic volatility models, and stochastic local volatility
models such as the stochastic alpha beta rho (SABR) model.


title: Markov dilations of semigroups of Fourier multipliers

abstract:
  We describe a Markov dilation for any weak* continuous semigroup $(T_t)_{t
\geq 0}$ of selfadjoint unital completely positive Fourier multipliers acting
on the group von Neumann algebra $\mathrm{VN}(G)$ of a locally compact group
$G$.


title: Revisiting WASP-47 with ESPRESSO and TESS

abstract:
  WASP-47 hosts a remarkable planetary system containing a hot Jupiter (WASP-47
b; P = 4.159 days) with an inner super-Earth (WASP-47 e; P = 0.7896 days), a
close-orbiting outer Neptune (WASP-47 d; P = 9.031 days), and a long period
giant planet (WASP-47 c; P = 588.4 days). We use the new TESS photometry to
refine the orbital ephemerides of the transiting planets in the system,
particularly the hot Jupiter WASP-47 b, for which we find an update equating to
a 17.4 min shift in the transit time. We report new radial velocity
measurements from the ESPRESSO spectrograph for WASP-47, which we use to refine
the masses of WASP-47 d and WASP-47 e, with a high cadence observing strategy
aimed to focus on the super-Earth WASP-47 e. We detect a periodic modulation in
the K2 photometry that corresponds to a 32.5$\pm$3.9 day stellar rotation, and
find further stellar activity signals in our ESPRESSO data consistent with this
rotation period. For WASP-47 e we measure a mass of 6.77$\pm$0.57 M$_{\oplus}$
and a bulk density of 6.29$\pm$0.60 gcm$^{-3}$, giving WASP-47 e the second
most precisely measured density to date of any super-Earth. The mass and radius
of WASP-47 e, combined with the exotic configuration of the planetary system,
suggest the WASP-47 system formed through a mechanism different to systems with
multiple small planets or more typical isolated hot Jupiters.


title:
Measurement of the cross section of $e^{+}e^{-}\to\eta\pi^{+}\pi^{-}$ at
  center-of-mass energies from 3.872 GeV to 4.700 GeV

abstract:
  Using data samples with an integrated luminosity of 19 fb$^{-1}$ at
twenty-eight center-of-mass energies from 3.872 GeV to 4.700 GeV collected with
the BESIII detector at the BEPCII electron--positron collider, the process
$e^{+}e^{-}\to\eta\pi^{+}\pi^{-}$ and the intermediate process
$e^{+}e^{-}\to\eta\rho^{0}$ are studied for the first time. The Born cross
sections are measured. No significant resonance structure is observed in the
cross section lineshape.


title:
On the direct diagonalization method for a few particles trapped in
  harmonic potentials

abstract:
  We describe a procedure to systematically improve direct diagonalization
results for few-particle systems trapped in one-dimensional harmonic potentials
interacting by contact interactions. We start from the two-body problem to
define a renormalization method for the interparticle interactions. The
procedure is benchmarked with state-of-the-art numerical results for three and
four symmetric fermions.


title:
Impact of the Coulomb field on charged-pion spectra in few-GeV heavy-ion
  collisions

abstract:
  In nuclear collisions the incident protons generate a Coulomb field which
acts on produced charged particles. The impact of these interactions on charged
pion transverse-mass and rapidity spectra, as well as on pion-pion momentum
correlations is investigated in Au+Au collisions at $\sqrt{s_{NN}}$ = 2.4 GeV.
We show that the low-mt part of the data ($m_t < 0.2$ GeV/c$^2$) can be well
described with a Coulomb-modified Boltzmann distribution that also takes
changes of the Coulomb field during the expansion of the fireball into account.
The observed centrality dependence of the fitted mean Coulomb potential
deviates strongly from a $A_{part}^{2/3}$ scaling, indicating that, next to the
fireball, the non-interacting charged spectators have to be taken into account.
For the most central collisions, the Coulomb modifications of the HBT source
radii are found to be consistent with the potential extracted from the
single-pion transverse-mass distributions. This finding suggests that the
region of homogeneity obtained from two-pion correlations coincides with the
region in which the pions freeze-out. Using the inferred mean-square radius of
the charge distribution at freeze-out, we have deduced a baryon density, in
fair agreement with values obtained from statistical hadronization model fits
to the particle yields.


title: FedCAT: Towards Accurate Federated Learning via Device Concatenation

abstract:
  As a promising distributed machine learning paradigm, Federated Learning (FL)
enables all the involved devices to train a global model collaboratively
without exposing their local data privacy. However, for non-IID scenarios, the
classification accuracy of FL models decreases drastically due to the weight
divergence caused by data heterogeneity. Although various FL variants have been
studied to improve model accuracy, most of them still suffer from the problem
of non-negligible communication and computation overhead. In this paper, we
introduce a novel FL approach named Fed-Cat that can achieve high model
accuracy based on our proposed device selection strategy and device
concatenation-based local training method. Unlike conventional FL methods that
aggregate local models trained on individual devices, FedCat periodically
aggregates local models after their traversals through a series of logically
concatenated devices, which can effectively alleviate the model weight
divergence problem. Comprehensive experimental results on four well-known
benchmarks show that our approach can significantly improve the model accuracy
of state-of-the-art FL methods without causing extra communication overhead.


title: Synthesizing Photorealistic Images with Deep Generative Learning

abstract:
  The goal of this thesis is to present my research contributions towards
solving various visual synthesis and generation tasks, comprising image
translation, image completion, and completed scene decomposition. This thesis
consists of five pieces of work, each of which presents a new learning-based
approach for synthesizing images with plausible content as well as visually
realistic appearance. Each work demonstrates the superiority of the proposed
approach on image synthesis, with some further contributing to other tasks,
such as depth estimation.


title:
TiFe$_{0.85}$Mn$_{0.05}$ alloy produced at industrial level for a
  hydrogen storage plant

abstract:
  In the use of hydrogen as large-scale energy vector, metal hydrides based on
intermetallic compounds play a key role, thanks to mild temperatures and
pressures required for the storage. It is increasingly necessary to develop
hydrogen-based devices, and in this work, the intermetallic compound
TiFe$_{0.85}$Mn$_{0.05}$ is evaluated and selected as H$_2$-carrier for a
storage system of 50 kg of H$_2$. A batch of 5 kg of alloy was synthesized at
industrial level and characterized through scanning electron microscopy and
powder X-ray diffraction, to determine the structure and phase abundance.
Moreover, the hydrogen sorption properties were investigated through
thermodynamic and kinetic analyses, followed by a long-term cycling study and
resistance to O$_2$ and H$_2$O poisoning. Comparing results for alloys with
same nominal composition, but prepared either under industrial or laboratory
conditions, it was found that the alloy synthesis promotes discrepancies in
phase abundance and microstructure and promotes the formation of a passive
layer that deeply affect the hydrogen sorption properties. A scheme based on
Monte Carlo simulation and structural results was developed to explain the key
role of the passive layer (Ti$_3$Fe$_3$O) and of the secondary phases
(Ti$_4$Fe$_2$O$_{0.4}$ and $\beta$-Ti$_{80}$(Fe,Mn)$_2$0) in promoting the
hydrogenation of the TiFe$_{0.85}$Mn$_{0.05}$. A storage system based on this
alloy can be integrated with an electrolyser upstream (25 bar) and a fuel cell
downstream (1 bar) at 55 $^\circ$C, storing 1.0 H$_2$ wt.%, displaying fast
kinetic, resistance to oxygen, water and nitrogen gas impurities, and stability
over more than 250 cycles.


title:
Singlet-doublet transitions of a quantum dot Josephson junction detected
  in a transmon circuit

abstract:
  We realize a hybrid superconductor-semiconductor transmon device in which the
Josephson effect is controlled by a gate-defined quantum dot in an InAs/Al
nanowire. Microwave spectroscopy of the transmon's transition spectrum allows
us to probe the ground state parity of the quantum dot as a function of gate
voltages, external magnetic flux, and magnetic field applied parallel to the
nanowire. The measured parity phase diagram is in agreement with that predicted
by a single-impurity Anderson model with superconducting leads. Through
continuous time monitoring of the circuit we furthermore resolve the
quasiparticle dynamics of the quantum dot Josephson junction across the phase
boundaries. Our results can facilitate the realization of semiconductor-based
$0-\pi$ qubits and Andreev qubits.


title: 'Cyber security is a dark art': The CISO as soothsayer

abstract:
  Commercial organisations continue to face a growing and evolving threat of
data breaches and system compromises, making their cyber-security function
critically important. Many organisations employ a Chief Information Security
Officer (CISO) to lead such a function. We conducted in-depth, semi-structured
interviews with 15 CISOs and six senior organisational leaders, between October
2019 and July 2020, as part of a wider exploration into the purpose of CISOs
and cyber-security functions. In this paper, we employ broader security
scholarship related to ontological security and sociological notions of
identity work to provide an interpretative analysis of the CISO role in
organisations. Research findings reveal that cyber security is an expert system
that positions the CISO as an interpreter of something that is mystical,
unknown and fearful to the uninitiated. They show how the fearful nature of
cyber security contributes to it being considered an ontological threat by the
organisation, while responding to that threat contributes to the organisation's
overall identity. We further show how cyber security is analogous to a belief
system and how one of the roles of the CISO is akin to that of a modern-day
soothsayer for senior management; that this role is precarious and, at the same
time, superior, leading to alienation within the organisation. Our study also
highlights that the CISO identity of protector-from-threat, linked to the
precarious position, motivates self-serving actions that we term `cyber
sophistry'. We conclude by outlining a series of implications for both
organisations and CISOs.


title: Scalable Multi-node Fast Fourier Transform on GPUs

abstract:
  In this paper, we present the details of our multi-node GPU-FFT library, as
well its scaling on Selene HPC system. Our library employs slab decomposition
for data division and MPI for communication among GPUs. We performed GPU-FFT on
$1024^3$, $2048^3$, and $4096^3$ grids using a maximum of 512 A100 GPUs. We
observed good scaling for $4096^3$ grid with 64 to 512 GPUs. We report that the
timings of multicore FFT of $1536^3$ grid with 196608 cores of Cray XC40 is
comparable to that of GPU-FFT of $2048^3$ grid with 128 GPUs. The efficiency of
GPU-FFT is due to the fast computation capabilities of A100 card and efficient
communication via NVlink.


title:
The role of correlated hopping in many-body physics of flat-band
  systems: Nagaoka ferromagnetism

abstract:
  In narrow-band systems, correlated contribution to effective hopping becomes
important, and one needs to carefully consider three types of hopping
processes: between doubly- and singly-occupied sites, between singly-occupied
and empty sites, and between doubly-occupied and empty or two singly-occupied
sites. All three hopping parameters cannot vanish simultaneously, and one
should specify for which of these processes the band becomes flat. By means of
exact diagonalization of finite systems we demonstrate that three hoppings play
qualitatively different roles in many-body effects, in particular, in the
formation of half-metallic ferromagnetic (Nagaoka) states.


title:
Constraining variability of coupling constants with bright and extreme
  quasars

abstract:
  The 'extreme Population A (xA) quasars' approaching, sometimes exceeding the
Eddington limit, are a type of quasars that could serve as a standard candle to
measure distances too large for supernovae type Ia (SNe Ia) to be observable.
For using xA quasars as standard candles, it would be beneficial to know how
their luminosities would vary if coupling constants varied over cosmic time.
Alternatively, when calibrated using SN Ia standard candle, xA quasar
observations could constrain the variation of coupling constants. We show that
the Hubble diagram of xA quasars provides the same constraint on the constants'
variation as the Hubble diagram of SNe Ia from the Pantheon data. The coupling
constants vary concurrently in our model, i.e., the variation of the speed of
light $c$, the gravitational constant $G$, the Planck constant $h$, and the
Boltzmann constant $k$, are interrelated as $G\thicksim c^{3}\thicksim
h^{3}\thicksim k^{3/2}$. The constraint thus determined can be expressed in
terms of the Hubble constant $H_{0}$ as
$(\dot{G}/G)_{0}=3(\dot{c}/c)_{0}=3(\dot{h}/h)_{0}=1.5 (\dot{k}/k)_{0}=5.4H_{0}
=3.90(\pm 0.04)\times 10^{-10} yr^{-1}$, where subscript 0 is for the current
time. This conclusion is corroborated with the analysis of data on the quasars
with correlated UV and X-ray emissions.


title:
Data refinement for fully unsupervised visual inspection using
  pre-trained networks

abstract:
  Anomaly detection has recently seen great progress in the field of visual
inspection. More specifically, the use of classical outlier detection
techniques on features extracted by deep pre-trained neural networks have been
shown to deliver remarkable performances on the MVTec Anomaly Detection (MVTec
AD) dataset. However, like most other anomaly detection strategies, these
pre-trained methods assume all training data to be normal. As a consequence,
they cannot be considered as fully unsupervised. There exists to our knowledge
no work studying these pre-trained methods under fully unsupervised setting. In
this work, we first assess the robustness of these pre-trained methods to fully
unsupervised context, using polluted training sets (i.e. containing defective
samples), and show that these methods are more robust to pollution compared to
methods such as CutPaste. We then propose SROC, a Simple Refinement strategy
for One Class classification. SROC enables to remove most of the polluted
images from the training set, and to recover some of the lost AUC. We further
show that our simple heuristic competes with, and even outperforms much more
complex strategies from the existing literature.


title:
Handwriting Biometrics: Applications and Future Trends in e-Security and
  e-Health

abstract:
  Background- This paper summarizes the state-of-the-art and applications based
on online handwritting signals with special emphasis on e-security and e-health
fields. Methods- In particular, we focus on the main achievements and
challenges that should be addressed by the scientific community, providing a
guide document for future research. Conclusions- Among all the points discussed
in this article, we remark the importance of considering security, health, and
metadata from a joint perspective. This is especially critical due to the
double use possibilities of these behavioral signals.


title: ALMA as a prominence thermometer: First observations

abstract:
  We present first prominence observations obtained with ALMA in Band 3 at the
wavelength of 3 mm. High-resolution observations have been coaligned with the
MSDP H$\alpha$ data from Wroclaw-Bialk\'{o}w large coronagraph at similar
spatial resolution. We analyze one particular co-temporal snapshot, first
calibrating both ALMA and MSDP data and then demonstrating a reasonable
correlation between both. In particular we can see quite similar fine-structure
patterns in both ALMA brightness temperature maps and MSDP maps of H$\alpha$
intensities. Using ALMA we intend to derive the prominence kinetic
temperatures. However, having current observations only in one band, we use an
independent diagnostic constraint which is the H$\alpha$ line integrated
intensity. We develop an inversion code and show that it can provide realistic
temperatures for brighter parts of the prominence where one gets a unique
solution, while within faint structures such inversion is ill conditioned. In
brighter parts ALMA serves as a prominence thermometer, provided that the
optical thickness in Band 3 is large enough. In order to find a relation
between brightness and kinetic temperatures for a given observed H$\alpha$
intensity, we constructed an extended grid of non-LTE prominence models
covering a broad range of prominence parameters. We also show the effect of the
plane-of-sky filling factor on our results.


title:
Parameter estimation with non stationary noise in gravitational waves
  data

abstract:
  The sensitivity of gravitational-waves detectors is characterized by their
noise curves which determine the detector's reach and the ability to accurately
measure the parameters of astrophysical sources. The detector noise is
typically modelled as stationary and Gaussian for many practical purposes.
However, physical changes in the state of detectors due to environmental and
instrumental factors, including extreme cases where a detector discontinues
observing for some time, introduce non-stationarity into the noise. Even slow
evolution of the detector sensitivity will affect long duration signals such as
binary neutron star (BNS) mergers. Mis-estimation of the noise behavior
directly impacts the posterior width of the signal parameters. This becomes an
issue for studies which depend on accurate localization volumes such as i)
probing cosmological parameters (such as Hubble constant, clustering bias)
using cross-correlation methods with galaxies, ii) doing electromagnetic
follow-up using localization information from parameter estimation done from
pre-merger data. We study the effects of dynamical noise on the parameter
estimation of the GW events. We develop a new method to correct dynamical noise
by estimating a locally-valid pseudo PSD which is normalized along the
time-frequency track of a potential signal. We do simulations by injecting the
BNS signal in various scenarios where the detector goes through a period of
non-stationarity with reference noise curve of third generation detectors
(Cosmic explorer, Einstein telescope). As an example, for a source where
mis-modelling of the noise biases the signal-to-noise estimate by even $10\%$,
one would expect the estimated localization volume to be either under or over
reported by $\sim 30\%$; errors like this, especially in low-latency, could
potentially cause follow-up campaigns to miss the true source location.


title:
Magnetization of Relativistic Current-Carrying Jets with Radial Velocity
  Shear

abstract:
  Astrophysical jets, launched from the immediate vicinity of accreting black
holes, carry away large amounts of power in a form of bulk kinetic energy of
jet particles and electromagnetic flux. Here we consider a simple analytical
model for relativistic jets at larger distances from their launching sites,
assuming a cylindrical axisymmetric geometry with a radial velocity shear, and
purely toroidal magnetic field. We argue that, as long as the jet plasma is in
magnetohydrostatic equilibrium, such outflows tend to be particle dominated,
i.e. the ratio of the electromagnetic to particle energy flux, integrated over
the jet cross-sectional area, is typically below unity, $\sigma < 1$. At the
same time, for particular magnetic and radial velocity profiles, magnetic
pressure may still dominate over particle pressure for certain ranges of the
jet radius, i.e. the local jet plasma parameter $\beta_{pl} < 1$, and this may
be relevant in the context of particle acceleration and production of
high-energy emission in such systems. The jet magnetization parameter can be
elevated up to the modest values $\sigma \lesssim \mathcal{O}(10)$ only in the
case of extreme gradients or discontinuities in the gaseous pressure, and a
significantly suppressed velocity shear. Such configurations, which consist of
a narrow, unmagnetized jet spine surrounded by an extended, force-free layer,
may require an additional poloidal field component to stabilize them against
current-driven oscillations, but even this will not elevate substantially their
$\sigma$ parameter.


title: Data-driven distributed MPC of dynamically coupled linear systems

abstract:
  In this paper, we present a data-driven distributed model predictive control
(MPC) scheme to stabilise the origin of dynamically coupled discrete-time
linear systems subject to decoupled input constraints. The local optimisation
problems solved by the subsystems rely on a distributed adaptation of the
Fundamental Lemma by Willems et al., allowing to parametrise system
trajectories using only measured input-output data without explicit model
knowledge. For the local predictions, the subsystems rely on communicated
assumed trajectories of neighbours. Each subsystem guarantees a small deviation
from these trajectories via a consistency constraint. We provide a theoretical
analysis of the resulting non-iterative distributed MPC scheme, including
proofs of recursive feasibility and (practical) stability. Finally, the
approach is successfully applied to a numerical example.


title: Regularized Zero-Range Hamiltonian for a Bose Gas with an Impurity

abstract:
  We study the Hamiltonian for a system of N identical bosons interacting with
an impurity, i.e., a different particle, via zero-range forces in dimension
three. It is well known that, following the standard approach, one obtains the
Ter-Martirosyan Skornyakov Hamiltonian which is unbounded from below. In order
to avoid such instability problem, we introduce a three-body force acting at
short distances. The effect of this force is to reduce to zero the strength of
the zero-range interaction between two particles, i.e., the impurity and a
boson, when another boson approaches the common position of the first two
particles. We show that the Hamiltonian defined with such regularized
interaction is self-adjoint and bounded from below if the strength of the
three-body force is sufficiently large. The method of the proof is based on a
careful analysis of the corresponding quadratic form.


title: Full quantum crossed products, invariant measures, and type-I lifting

abstract:
  We show that for a closed embedding $\mathbb{H}\le \mathbb{G}$ of locally
compact quantum groups (LCQGs) with $\mathbb{G}/\mathbb{H}$ admitting an
invariant probability measure, a unitary $\mathbb{G}$-representation is type-I
if its restriction to $\mathbb{H}$ is. On a related note, we also prove that if
an action $\mathbb{G}\circlearrowright A$ of an LCQG on a unital $C^*$-algebra
admits an invariant state then the full group algebra of $\mathbb{G}$ embeds
into the resulting full crossed product (and into the multiplier algebra of
that crossed product if the original algebra is not unital).
  We also prove a few other results on crossed products of LCQG actions, some
of which seem to be folklore; among them are (a) the fact that two mutually
dual quantum-group morphisms produce isomorphic full crossed products, and (b)
the fact that full and reduced crossed products by dual-coamenable LCQGs are
isomorphic.


title: Stochastic Games with Synchronizing Objectives

abstract:
  We consider two-player stochastic games played on a finite graph for
infinitely many rounds. Stochastic games generalize both Markov decision
processes (MDP) by adding an adversary player, and two-player deterministic
games by adding stochasticity. The outcome of the game is a sequence of
distributions over the states of the game graph. We consider synchronizing
objectives, which require the probability mass to accumulate in a set of target
states, either always, once, infinitely often, or always after some point in
the outcome sequence; and the winning modes of sure winning (if the accumulated
probability is equal to 1) and almost-sure winning (if the accumulated
probability is arbitrarily close to 1).
  We present algorithms to compute the set of winning distributions for each of
these synchronizing modes, showing that the corresponding decision problem is
PSPACE-complete for synchronizing once and infinitely often, and PTIME-complete
for synchronizing always and always after some point. These bounds are
remarkably in line with the special case of MDPs, while the algorithmic
solution and proof technique are considerably more involved, even for
deterministic games. This is because those games have a flavour of imperfect
information, in particular they are not determined and randomized strategies
need to be considered, even if there is no stochastic choice in the game graph.
Moreover, in combination with stochasticity in the game graph, finite-memory
strategies are not sufficient in general (for synchronizing infinitely often).


title:
Orbital Dynamics at Atmospheric Pressure in a Lensed, Dual-beam, Optical
  Trap

abstract:
  Orbital optical trapping of a dielectric micro-particle in air was studied
experimentally using a lensed, counter-propagating dual-beam trap, and by
numerical simulations employing ray optics. The essential attributes of
particle dynamics are evaluated as functions of the transverse offset between
the beams, the axial offset between the laser foci and the total laser power,
both experimentally and computationally. We find that the Q-factor of the
orbital motion in this previously unexplored scheme is at least two orders of
magnitude higher than values attainable with conventional trapping. Under our
experimental conditions, silica micro-spheres orbit up to a maximum frequency
of ~2 kHz at atmospheric pressure, which can be further increased by increasing
the optical power in the trap. With the help of simulations, we discuss how the
experimental technique presented here can be further modified to enhance the Q
factor of particle's orbital motion. The evolution of orbital frequencies can
be a useful signature in analyzing the kinetics of deposition or loss of
materials from the surface of levitated particles in a controlled environment.
Hence, the approach reported here could find application as an \emph{in situ}
single particle technique for probing reactions relevant to atmospheric
chemistry.


title: Core-periphery detection in hypergraphs

abstract:
  Core-periphery detection is a key task in exploratory network analysis where
one aims to find a core, a set of nodes well-connected internally and with the
periphery, and a periphery, a set of nodes connected only (or mostly) with the
core. In this work we propose a model of core-periphery for higher-order
networks modeled as hypergraphs and we propose a method for computing a
core-score vector that quantifies how close each node is to the core. In
particular, we show that this method solves the corresponding non-convex
core-periphery optimization problem globally to an arbitrary precision. This
method turns out to coincide with the computation of the Perron eigenvector of
a nonlinear hypergraph operator, suitably defined in term of the incidence
matrix of the hypergraph, generalizing recently proposed centrality models for
hypergraphs. We perform several experiments on synthetic and real-world
hypergraphs showing that the proposed method outperforms alternative
core-periphery detection algorithms, in particular those obtained by
transferring established graph methods to the hypergraph setting via clique
expansion.


title: Large deviations for stochastic fluid networks with Weibullian tails

abstract:
  We consider a stochastic fluid network where the external input processes are
compound Poisson with heavy-tailed Weibullian jumps. Our results comprise of
large deviations estimates for the buffer content process in the vector-valued
Skorokhod space which is endowed with the product $J_1$ topology. To illustrate
our framework, we provide explicit results for a tandem queue. At the heart of
our proof is a recent sample-path large deviations result, and a novel
continuity result for the Skorokhod reflection map in the product $J_1$
topology.


title:
Positive Toeplitz Operators from a Harmonic Bergman-Besov space into
  Another

abstract:
  We define positive toeplitz operators between harmonic Bergman-Besov spaces
$b^p_\alpha$ on the unit ball of $\mathbb{R}^n$ for the full ranges of
parameters $0<p<\infty$, $\alpha\in\mathbb{R}$. We give characterizations of
bounded and compact Toeplitz operators taking one harmonic Bergman-Besov space
into another in terms of Carleson and vanishing Carleson measures. We also give
characterizations for a positive Toeplitz operator on $b^{2}_{\alpha}$ to be a
Schatten class operator $S_{p}$ in terms of averaging functions and Berezin
transforms for $1\leq p<\infty$, $\alpha\in\mathbb{R}$. Our results extend
those known for harmonic weighted Bergman spaces.


title: Cyclic duality for slice and orbit 2-categories

abstract:
  The self-duality of the paracyclic category is extended to a certain class of
homotopy categories of (2,1)-categories. These generalise the orbit category of
a group and are associated to certain self-dual preorders equipped with a
presheaf of groups and a cosieve. Slice 2-categories of equidimensional
submanifolds of a compact manifold without boundary form a particular case, and
for $S^1$, one recovers cyclic duality. This provides in particular a
visualisation of the results of B\"ohm and \c{S}tefan on the topic.


title:
Towards Safe, Real-Time Systems: Stereo vs Images and LiDAR for 3D
  Object Detection

abstract:
  As object detectors rapidly improve, attention has expanded past image-only
networks to include a range of 3D and multimodal frameworks, especially ones
that incorporate LiDAR. However, due to cost, logistics, and even some safety
considerations, stereo can be an appealing alternative. Towards understanding
the efficacy of stereo as a replacement for monocular input or LiDAR in object
detectors, we show that multimodal learning with traditional disparity
algorithms can improve image-based results without increasing the number of
parameters, and that learning over stereo error can impart similar 3D
localization power to LiDAR in certain contexts. Furthermore, doing so also has
calibration benefits with respect to image-only methods. We benchmark on the
public dataset KITTI, and in doing so, reveal a few small but common
algorithmic mistakes currently used in computing metrics on that set, and offer
efficient, provably correct alternatives.


title:
Electroweak phase transition in the nearly aligned Higgs effective field
  theory

abstract:
  We investigate the strongly first-order electroweak phase transition using an
effective field theoretical approach. The standard effective field theory with
finite number truncation of higher dimensional operators fails in the typical
parameter space where the strongly first-order phase transition is realized
because it cannot describe the non-decoupling quantum effect of new physics
beyond the standard model. To parameterize the non-decoupling quantum effect,
we employ the nearly aligned Higgs effective theory in which the Higgs
potential is parameterized by a Coleman-Weinberg like form. Extending this
framework with finite temperature corrections, we study the parameter space for
realizing the strongly first-order phase transition, and estimate the
gravitational wave produced at the phase transition.


title:
The sensitivity of GPz estimates of photo-z posterior PDFs to
  realistically complex training set imperfections

abstract:
  The accurate estimation of photometric redshifts is crucial to many upcoming
galaxy surveys, for example the Vera C. Rubin Observatory Legacy Survey of
Space and Time (LSST). Almost all Rubin extragalactic and cosmological science
requires accurate and precise calculation of photometric redshifts; many
diverse approaches to this problem are currently in the process of being
developed, validated, and tested. In this work, we use the photometric redshift
code GPz to examine two realistically complex training set imperfections
scenarios for machine learning based photometric redshift calculation: i) where
the spectroscopic training set has a very different distribution in
colour-magnitude space to the test set, and ii) where the effect of emission
line confusion causes a fraction of the training spectroscopic sample to not
have the true redshift. By evaluating the sensitivity of GPz to a range of
increasingly severe imperfections, with a range of metrics (both of photo-z
point estimates as well as posterior probability distribution functions, PDFs),
we quantify the degree to which predictions get worse with higher degrees of
degradation. In particular we find that there is a substantial drop-off in
photo-z quality when line-confusion goes above ~1%, and sample incompleteness
below a redshift of 1.5, for an experimental setup using data from the Buzzard
Flock synthetic sky catalogues.


title:
Detecting gravitational lenses using machine learning: exploring
  interpretability and sensitivity to rare lensing configurations

abstract:
  Forthcoming large imaging surveys such as Euclid and the Vera Rubin
Observatory Legacy Survey of Space and Time are expected to find more than
$10^5$ strong gravitational lens systems, including many rare and exotic
populations such as compound lenses, but these $10^5$ systems will be
interspersed among much larger catalogues of $\sim10^9$ galaxies. This volume
of data is too much for visual inspection by volunteers alone to be feasible
and gravitational lenses will only appear in a small fraction of these data
which could cause a large amount of false positives. Machine learning is the
obvious alternative but the algorithms' internal workings are not obviously
interpretable, so their selection functions are opaque and it is not clear
whether they would select against important rare populations. We design, build,
and train several Convolutional Neural Networks (CNNs) to identify strong
gravitational lenses using VIS, Y, J, and H bands of simulated data, with F1
scores between 0.83 and 0.91 on 100,000 test set images. We demonstrate for the
first time that such CNNs do not select against compound lenses, obtaining
recall scores as high as 76\% for compound arcs and 52\% for double rings. We
verify this performance using Hubble Space Telescope (HST) and Hyper
Suprime-Cam (HSC) data of all known compound lens systems. Finally, we explore
for the first time the interpretability of these CNNs using Deep Dream, Guided
Grad-CAM, and by exploring the kernels of the convolutional layers, to
illuminate why CNNs succeed in compound lens selection.


title: Towards Automated Video Game Testing: Still a Long Way to Go

abstract:
  As the complexity and scope of game development increase, playtesting remains
an essential activity to ensure the quality of video games. Yet, the manual,
ad-hoc nature of playtesting gives space to improvements in the process. In
this study, we investigate gaps between academic solutions in the literature
for automated video game testing and the needs of video game developers in the
industry. We performed a literature review on video game automated testing and
applied an online survey with video game developers. The literature results
show a rise in research topics related to automated video game testing. The
survey results show that game developers are skeptical about using automated
agents to test games. We conclude that there is a need for new testing
approaches that did not disrupt the developer workflow. As for the researchers,
the focus should be on the testing goal and testing oracle.


title:
CCOMPASSION: A Hybrid Cloudlet Placement Framework over Passive Optical
  Access Networks

abstract:
  Cloud-based computing technology is one of the most significant technical
advents of the last decade and extension of this facility towards access
networks by aggregation of cloudlets is a step further. To fulfill the ravenous
demand for computational resources entangled with the stringent latency
requirements of computationally-heavy applications related to augmented
reality, cognitive assistance and context-aware computation, installation of
cloudlets near the access segment is a very promising solution because of its
support for wide geographical network distribution, low latency, mobility and
heterogeneity. In this paper, we propose a novel framework, Cloudlet Cost
OptiMization over PASSIve Optical Network (CCOMPASSION), and formulate a
nonlinear mixed-integer program to identify optimal cloudlet placement
locations such that installation cost is minimized whilst meeting the capacity
and latency constraints. Considering urban, suburban and rural scenarios as
commonly-used network deployment models, we investigate the feasibility of the
proposed model over them and provide guidance on the overall cloudlet facility
installation over optical access network. We also study the percentage of
incremental energy budget in the presence of cloudlets of the existing network.
The final results from our proposed model can be considered as fundamental
cornerstones for network planning with hybrid cloudlet network architectures.


title:
Orbits and adiabatic contraction in scalar field dark matter halos:
  revisiting the cusp-core problem in dwarf galaxies

abstract:
  Bose-Einstein-condensed dark matter, also called scalar-field dark matter
(SFDM), has become a popular alternative to cold dark matter (CDM), because it
predicts galactic cores, in contrast to the cusps of CDM halos ("cusp-core
problem"). We continue the study of SFDM with a strong, repulsive
self-interaction; the Thomas-Fermi regime of SFDM (SFDM-TF). In this model,
structure formation is suppressed below a scale related to the TF radius
$R_\text{TF}$, which is close to the radius of central cores in these halos. We
investigate for the first time the impact of baryons onto realistic galactic
SFDM-TF halo profiles by studying the process of adiabatic contraction (AC) in
such halos. In doing so, we first analyse the underlying quantum
Hamilton-Jacobi framework appropriate for SFDM and calculate dark matter
orbits, in order to verify the validity of the assumptions usually required for
AC. Then, we calculate the impact of AC onto SFDM-TF halos of mass $\sim
10^{11}~M_{\odot}$, with various baryon fractions and core radii, $R_\text{TF}
\sim (0.1 - 4)$ kpc, and compare our results with observational velocity data
of dwarf galaxies. We find that AC-modified SFDM-TF halos with kpc-size core
radii reproduce the data well, suggesting stellar feedback may not be required.
On the other hand, halos with sub-kpc core radii face the same issue than CDM,
in that they are not in accordance with galaxy data in the central halo parts.


title:
Model Comparison and Calibration Assessment: User Guide for Consistent
  Scoring Functions in Machine Learning and Actuarial Practice

abstract:
  One of the main tasks of actuaries and data scientists is to build good
predictive models for certain phenomena such as the claim size or the number of
claims in insurance. These models ideally exploit given feature information to
enhance the accuracy of prediction. This user guide revisits and clarifies
statistical techniques to assess the calibration or adequacy of a model on the
one hand, and to compare and rank different models on the other hand. In doing
so, it emphasises the importance of specifying the prediction target functional
at hand a priori (e.g. the mean or a quantile) and of choosing the scoring
function in model comparison in line with this target functional. Guidance for
the practical choice of the scoring function is provided. Striving to bridge
the gap between science and daily practice in application, it focuses mainly on
the pedagogical presentation of existing results and of best practice. The
results are accompanied and illustrated by two real data case studies on
workers' compensation and customer churn.


title:
Magneto-optical rotation: Accurate approximated analytical solutions for
  single probe atomic magnetometers

abstract:
  We report an approximated analytical solution for a single-probe four-state
atomic magnetometer where no analytical solution exists. This approximated
analytical solution demonstrates excellent accuracy in broad probe power and
detuning ranges when compared with the numerical solution obtained using a 4th
order Runge-Kutta differential equation solver on MATLAB. The theoretical
framework and results also encompass widely applied single-probe three-state
atomic magnetometers for which no analytical solution, even approximated, is
available to date in small detuning regions.


title:
A narrow-stencil framework for convergent numerical approximations of
  fully nonlinear second order PDEs

abstract:
  This paper develops a unified general framework for designing convergent
finite difference and discontinuous Galerkin methods for approximating
viscosity and regular solutions of fully nonlinear second order PDEs. Unlike
the well-known monotone (finite difference) framework, the proposed new
framework allows for the use of narrow stencils and unstructured grids which
makes it possible to construct high order methods. The general framework is
based on the concepts of consistency and g-monotonicity which are both defined
in terms of various numerical derivative operators. Specific methods that
satisfy the framework are constructed using numerical moments. Admissibility,
stability, and convergence properties are proved, and numerical experiments are
provided along with some computer implementation details.


title:
Jet modifications from colour rope formation in dense systems of
  non-parallel strings

abstract:
  We revisit our rope model for string fragmentation that has been shown to
give a reasonable description of strangeness and baryon enhancement in
high-multiplicity pp events at the LHC. A key feature of the model is that the
enhancement is driven by the increased string tension due to strings
overlapping in dense systems. By introducing an improved space-time picture for
the overlap between fragmenting strings, where also non-parallel strings are
properly taken into account, we are now able to investigate the enhancement
both in jets and in the underlying event in a consistent way.


title: Localization of gravitational waves using machine learning

abstract:
  An observation of gravitational waves is a trigger of the multi-messenger
search of an astronomical event. A combination of the data from two or three
gravitational wave telescopes indicates the location of a source and
low-latency data analysis is key to transferring the information to other
telescopes sensitive at different wavelengths. In contrast to the current
method, which relies on the matched-filtering technique, we proposed the use of
machine learning that is much faster and possibly more accurate than matched
filtering. Our machine-learning method is a combination of the method proposed
by Chatterjee {\it et al.} and a method using the temporal convolutional
network. We demonstrate the sky localization of a gravitational-wave source
using four telescopes: LIGO H1, LIGO L1, Virgo, and KAGRA, and compare the
result in the case without KAGRA to examine the positive influence of having
the fourth telescope in the global gravitational-wave network.


title: Confidence Calibration for Object Detection and Segmentation

abstract:
  Calibrated confidence estimates obtained from neural networks are crucial,
particularly for safety-critical applications such as autonomous driving or
medical image diagnosis. However, although the task of confidence calibration
has been investigated on classification problems, thorough investigations on
object detection and segmentation problems are still missing. Therefore, we
focus on the investigation of confidence calibration for object detection and
segmentation models in this chapter. We introduce the concept of multivariate
confidence calibration that is an extension of well-known calibration methods
to the task of object detection and segmentation. This allows for an extended
confidence calibration that is also aware of additional features such as
bounding box/pixel position, shape information, etc. Furthermore, we extend the
expected calibration error (ECE) to measure miscalibration of object detection
and segmentation models. We examine several network architectures on MS COCO as
well as on Cityscapes and show that especially object detection as well as
instance segmentation models are intrinsically miscalibrated given the
introduced definition of calibration. Using our proposed calibration methods,
we have been able to improve calibration so that it also has a positive impact
on the quality of segmentation masks as well.


title:
Behaviorally Grounded Model-Based and Model Free Cost Reduction in a
  Simulated Multi-Echelon Supply Chain

abstract:
  Amplification and phase shift in ordering signals, commonly referred to as
bullwhip, are responsible for both excessive strain on real world inventory
management systems, stock outs, and unnecessary capital reservation though
safety stock building. Bullwhip is a classic, yet persisting, problem with
reverberating consequences in inventory management. Research on bullwhip has
consistently emphasized behavioral influences for this phenomenon and leveraged
behavioral ordering models to suggest interventions. However more recent
model-free approaches have also seen success. In this work, the author develops
algorithmic approaches towards mitigating bullwhip using both behaviorally
grounded model-based approaches alongside a model-free dual deep Q-network
reinforcement learning approach. In addition to exploring the utility of this
specific model-free architecture to multi-echelon supply chains with imperfect
information sharing and information delays, the author directly compares the
performance of these model-based and model-free approaches. In doing so, this
work highlights both the insights gained from exploring model-based approaches
in the context of prior behavioral operations management literature and
emphasizes the complementary nature of model-based and model-free approaches in
approaching behaviorally grounded supply chain management problems.


title: Testing Symmetry for Bivariate Copulas using Bernstein Polynomials

abstract:
  In this paper, tests of symmetry for bivariate copulas are introduced and
studied using empirical Bernstein copula. Three statistics are proposed and
their asymptotic properties are established. Besides, a multiplier bootstrap
Bernstein version is investigated for implementation purpose. Simulation study
and real data application showed that the Bernstein tests outperform the tests
based on the empirical copula.


title:
Sensing accident-prone features in urban scenes for proactive driving
  and accident prevention

abstract:
  In urban cities, visual information along and on roadways is likely to
distract drivers and leads to missing traffic signs and other accident-prone
features. As a solution to avoid accidents due to missing these visual cues,
this paper proposes a visual notification of accident-prone features to
drivers, based on real-time images obtained via dashcam. For this purpose,
Google Street View images around accident hotspots (areas of dense accident
occurrence) identified by accident dataset are used to train a family of deep
convolutional neural networks (CNNs). Trained CNNs are able to detect
accident-prone features and classify a given urban scene into an accident
hotspot and a non-hotspot (area of sparse accident occurrence). For given
accident hotspot, the trained CNNs can classify it into an accident hotspot
with the accuracy up to 90%. The capability of detecting accident-prone
features by the family of CNNs is analyzed by a comparative study of four
different class activation map (CAM) methods, which are used to inspect
specific accident-prone features causing the decision of CNNs, and pixel-level
object class classification. The outputs of CAM methods are processed by an
image processing pipeline to extract only the accident-prone features that are
explainable to drivers with the help of visual notification system. To prove
the efficacy of accident-prone features, an ablation study is conducted.
Ablation of accident-prone features taking 7.7%, on average, of total area in
each image sample causes up to 13.7% more chance of given area to be classified
as a non-hotspot.


title:
Kinematical Analysis of Substructure in the Southern Periphery of the
  Large Magellanic Cloud

abstract:
  We report the first 3-D kinematical measurements of 88 stars in the direction
of several recently discovered substructures in the southern periphery of the
Large Magellanic Cloud (LMC) using a combination of Gaia proper motions and
radial velocities from the APOGEE-2 survey. More specifically, we explore stars
lie in assorted APOGEE-2 pointings in a region of the LMC periphery where
various overdensities of stars have previously been identified in maps of stars
from Gaia and DECam. By using a model of the LMC disk rotation, we find that a
sizeable fraction of the APOGEE-2 stars have extreme space velocities that are
distinct from, and not a simple extension of, the LMC disk. Using N-body
hydrodynamical simulations of the past dynamical evolution and interaction of
the LMC and Small Magellanic Cloud (SMC), we explore whether the extreme
velocity stars may be accounted for as tidal debris created in the course of
that interaction. We conclude that the combination of LMC and SMC debris
produced from their interaction is a promising explanation, although we cannot
rule out other possible origins, and that these new data should be used to
constrain future simulations of the LMC-SMC interaction. We also conclude that
many of the stars in the southern periphery of the LMC lie out of the LMC plane
by several kpc. Given that the metallicity of these stars suggest they are
likely of Magellanic origin, our results suggest that a wider exploration of
the past interaction history of the Magellanic Clouds is needed.


title:
A Unifying Approach to Distributional Limits for Empirical Optimal
  Transport

abstract:
  We provide a unifying approach to central limit type theorems for empirical
optimal transport (OT). In general, the limit distributions are characterized
as suprema of Gaussian processes. We explicitly characterize when the limit
distribution is centered normal or degenerates to a Dirac measure. Moreover, in
contrast to recent contributions on distributional limit laws for empirical OT
on Euclidean spaces which require centering around its expectation, the
distributional limits obtained here are centered around the population
quantity, which is well-suited for statistical applications.
  At the heart of our theory is Kantorovich duality representing OT as a
supremum over a function class $\mathcal{F}_{c}$ for an underlying sufficiently
regular cost function $c$. In this regard, OT is considered as a functional
defined on $\ell^{\infty}(\mathcal{F}_{c})$ the Banach space of bounded
functionals from $\mathcal{F}_{c}$ to $\mathbb{R}$ and equipped with uniform
norm. We prove the OT functional to be Hadamard directional differentiable and
conclude distributional convergence via a functional delta method that
necessitates weak convergence of an underlying empirical process in
$\ell^{\infty}(\mathcal{F}_{c})$. The latter can be dealt with empirical
process theory and requires $\mathcal{F}_{c}$ to be a Donsker class. We give
sufficient conditions depending on the dimension of the ground space, the
underlying cost function and the probability measures under consideration to
guarantee the Donsker property. Overall, our approach reveals a noteworthy
trade-off inherent in central limit theorems for empirical OT: Kantorovich
duality requires $\mathcal{F}_{c}$ to be sufficiently rich, while the empirical
processes only converges weakly if $\mathcal{F}_{c}$ is not too complex.


title:
Symbol repetition in interstellar communications: methods and
  observations

abstract:
  Discoverable interstellar communication signals are expected to exhibit al
least one signal characteristic clearly distinct from random noise. A
hypothesis is proposed that radio telescope received signals may contain
transmitted delta-t delta-f opposite circular polarized pulse pairs, conveying
a combination of information content and discovery methods, including symbol
repetition. Hypothetical signals are experimentally measured using a 26 foot
diameter radio telescope, a chosen matched filter receiver, and machine post
processing system. Measurements are expected to present likelihoods explained
by an Additive White Gaussian Noise model, augmented to reduce radio frequency
interference. In addition, measurements are expected to present no significant
differences across a population of Right Ascension ranges, during long duration
experiments. The hypothesis and experimental methods described in this paper
are based on multiple radio telescope delta-t delta-f polarized pulse pair
experiments previously reported. (ref. arXiv:2105.03727, arXiv:2106.10168). In
the current work, a Right Ascension filter spans twenty-one 0.3 hour Right
Ascension bins over a 0 to 6.3 hr range, during a 143 day experiment. Apparent
symbol repetition is measured and analyzed. The 5.25 plus or minus 0.15 hr
Right Ascension, -7.6 degree plus or minus 1 degree Declination celestial
direction has been associated with anomalous observations in previous work, and
continues to present anomalies, having unknown cause.


title: Separable symmetric tensors and separable anti-symmetric tensors

abstract:
  In this paper, we first introduce the invertibility of even-order tensors and
the separable tensors, including separable symmetry tensors and separable
anti-symmetry tensors, defined respectively as the sum and the algebraic sum of
rank-1 tensors generated by the tensor product of some vectors, say, $v_{1},
v_{2}, \ldots, v_{m}$. We show that the $m!$ sumrands, each in form
$v_{\sigma(1)}\times v_{\sigma(2)}\times\ldots\times v_{\sigma(m)}$, are
linearly independent if $v_{1},v_{2}, \ldots, v_{m}$ are linearly independent,
where $\sigma$ is any permutation on $\set{1,2,\ldots,m}$. We offer a class of
tensors to achieve the upper bound for $\rank(A) \leq 6$ for all $A\in
R^{3\times 3\times 3}$. We also show that each $3\times 3\times 3$
anti-symmetric tensor is separable.


title: Towards Optimal Lower Bounds for k-median and k-means Coresets

abstract:
  Given a set of points in a metric space, the $(k,z)$-clustering problem
consists of finding a set of $k$ points called centers, such that the sum of
distances raised to the power of $z$ of every data point to its closest center
is minimized. Special cases include the famous k-median problem ($z = 1$) and
k-means problem ($z = 2$). The $k$-median and $k$-means problems are at the
heart of modern data analysis and massive data applications have given raise to
the notion of coreset: a small (weighted) subset of the input point set
preserving the cost of any solution to the problem up to a multiplicative $(1
\pm \varepsilon)$ factor, hence reducing from large to small scale the input to
the problem.
  In this paper, we present improved lower bounds for coresets in various
metric spaces. In finite metrics consisting of $n$ points and doubling metrics
with doubling constant $D$, we show that any coreset for $(k,z)$ clustering
must consist of at least $\Omega(k \varepsilon^{-2} \log n)$ and $\Omega(k
\varepsilon^{-2} D)$ points, respectively. Both bounds match previous upper
bounds up to polylog factors. In Euclidean spaces, we show that any coreset for
$(k,z)$ clustering must consists of at least $\Omega(k\varepsilon^{-2})$
points. We complement these lower bounds with a coreset construction consisting
of at most $\tilde{O}(k\varepsilon^{-2}\cdot \min(\varepsilon^{-z},k))$ points.


title:
Far-Field Expansions for Harmonic Maps and the Electrostatics Analogy in
  Nematic Suspensions

abstract:
  For a smooth bounded domain $G\subset\mathbb{R}^3$ we consider maps
$n\colon\mathbb R^3\setminus G\to\mathbb S^2$ minimizing the energy
$E(n)=\int_{\mathbb R^3\setminus G}|\nabla n|^2 +F_s(n_{\lfloor\partial G})$
among $\mathbb S^2$-valued map such that $n(x)\approx n_0$ as $|x|\to\infty$.
This is a model for a particle $G$ immersed in nematic liquid crystal. The
surface energy $F_s$ describes the anchoring properties of the particle, and
can be quite general. We prove that such minimizing map $n$ has an asymptotic
expansion in powers of $1/r$. Further, we show that the leading order $1/r$
term is uniquely determined by the far-field condition $n_0$ for almost all
$n_0\in\mathbb S^2$, by relating it to the gradient of the minimal energy with
respect to $n_0$. We derive various consequences of this relation in physically
motivated situations: when the orientation of the particle $G$ is stable
relative to a prescribed far-field alignment $n_0$; and when the particle $G$
has some rotational symmetries. In particular, these corollaries justify some
approximations that can be found in the physics literature to describe nematic
suspensions via a so-called electrostatics analogy.


title: Equilibrium Aggregation: Encoding Sets via Optimization

abstract:
  Processing sets or other unordered, potentially variable-sized inputs in
neural networks is usually handled by \emph{aggregating} a number of input
tensors into a single representation. While a number of aggregation methods
already exist from simple sum pooling to multi-head attention, they are limited
in their representational power both from theoretical and empirical
perspectives. On the search of a principally more powerful aggregation
strategy, we propose an optimization-based method called Equilibrium
Aggregation. We show that many existing aggregation methods can be recovered as
special cases of Equilibrium Aggregation and that it is provably more efficient
in some important cases. Equilibrium Aggregation can be used as a drop-in
replacement in many existing architectures and applications. We validate its
efficiency on three different tasks: median estimation, class counting, and
molecular property prediction. In all experiments, Equilibrium Aggregation
achieves higher performance than the other aggregation techniques we test.


title:
Hybrid Robotic Grasping with a Soft Multimodal Gripper and a Deep
  Multistage Learning Scheme

abstract:
  Grasping has long been considered an important and practical task in robotic
manipulation. Yet achieving robust and efficient grasps of diverse objects is
challenging, since it involves gripper design, perception, control and
learning, etc. Recent learning-based approaches have shown excellent
performance in grasping a variety of novel objects. However, these methods
either are typically limited to one single grasping mode, or else more end
effectors are needed to grasp various objects. In addition, gripper design and
learning methods are commonly developed separately, which may not adequately
explore the ability of a multimodal gripper. In this paper, we present a deep
reinforcement learning (DRL) framework to achieve multistage hybrid robotic
grasping with a new soft multimodal gripper. A soft gripper with three grasping
modes (i.e., \textit{enveloping}, \textit{sucking}, and
\textit{enveloping\_then\_sucking}) can both deal with objects of different
shapes and grasp more than one object simultaneously. We propose a novel hybrid
grasping method integrated with the multimodal gripper to optimize the number
of grasping actions. We evaluate the DRL framework under different scenarios
(i.e., with different ratios of objects of two grasp types). The proposed
algorithm is shown to reduce the number of grasping actions (i.e., enlarge the
grasping efficiency, with maximum values of 161\% in simulations and 154\% in
real-world experiments) compared to single grasping modes.


title:
Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement
  Learning Approach

abstract:
  Dynamic mechanism design studies how mechanism designers should allocate
resources among agents in a time-varying environment. We consider the problem
where the agents interact with the mechanism designer according to an unknown
Markov Decision Process (MDP), where agent rewards and the mechanism designer's
state evolve according to an episodic MDP with unknown reward functions and
transition kernels. We focus on the online setting with linear function
approximation and attempt to recover the dynamic Vickrey-Clarke-Grove (VCG)
mechanism over multiple rounds of interaction. A key contribution of our work
is incorporating reward-free online Reinforcement Learning (RL) to aid
exploration over a rich policy space to estimate prices in the dynamic VCG
mechanism. We show that the regret of our proposed method is upper bounded by
$\tilde{\mathcal{O}}(T^{2/3})$ and further devise a lower bound to show that
our algorithm is efficient, incurring the same $\tilde{\mathcal{O}}(T^{2 / 3})$
regret as the lower bound, where $T$ is the total number of rounds. Our work
establishes the regret guarantee for online RL in solving dynamic mechanism
design problems without prior knowledge of the underlying model.


title:
Decomposition of tracial positive maps and their applications in quantum
  information

abstract:
  The Johnson--Kadison--Ringrose theorem states that the Aron--Berner extension
of a multilinear map from $C^*$-algebras to second dual spaces is unique and
separately weak$^*$-weak$^*$-continuous. We show that every positive
multilinear map between $C^*$-algebras is jointly weak$^*$-weak$^*$-continuous.
Utilizing this fact, we obtain joint weak$^*$-weak$^*$-continuity of the
Aron--Berner extension in the case of positive maps.This result allows us to
prove that every tracial positive multilinear map and every tracial completely
positive (not necessarily linear or multilinear) map $\Phi$ between
$C^*$-algebras, enjoy a decomposition $\Phi=\varphi_2 \circ \varphi_1$, in
which $\varphi_1$ is a tracial positive linear map with the commutative range
and $\varphi_2$ is a tracial completely positive map with commutative domain.
As a first result, tracial positive multilinear maps are completely positive.
As an application, we investigate the generalized variance and covariance in
quantum mechanics via arbitrary positive maps. Among others, an uncertainty
relation type inequality for commuting observables in a composite physical
system has been presented: if $ \Phi: \mathscr{A}\bigotimes \mathscr{B} \to
\mathscr{C}$ is a unital positive linear map between unital $C^*$-algebras and
$A\otimes I_\mathscr{B}$ and $ I_ \mathscr{A} \otimes B$ are compatible
observables in the composite system, then \[ {\rm Var}_\Phi(A\otimes
I_\mathscr{B}) {\rm Var}_\Phi(I_\mathscr{A}\otimes B) \geq \frac{1}{16} \frac{
\left|\Phi([{\textbf{A}}, {\textbf{C}}])
\Phi([{\textbf{B}},{\textbf{D}}])\right|^2}{(\|C-\alpha I_\mathscr{A}\|
\|D-\beta I_\mathscr{B}\|)^2 }\] for all self-adjoint elements $ C\in
\mathscr{A}$ and $D\in\mathscr{B}$ and every $\alpha, \beta \in \mathbb{C}$
with nonzero $ \|C-\alpha I_\mathscr{A}\|$ and $\|D-\beta I_\mathscr{B}\|$.


title:
Subjective Functionality and Comfort Prediction for Apartment Floor
  Plans and Its Application to Intuitive Searches

abstract:
  This study presents a new user experience in apartment searches using
functionality and comfort as query items. This study has three technical
contributions. First, we present a new dataset on the perceived functionality
and comfort scores of residential floor plans using nine question statements
about the level of comfort, openness, privacy, etc. Second, we propose an
algorithm to predict the scores from the floor plan images. Lastly, we
implement a new apartment search system and conduct a large-scale usability
study using crowdsourcing. The experimental results show that our apartment
search system can provide a better user experience. To the best of our
knowledge, this study is the first work to propose a highly accurate prediction
model for the subjective functionality and comfort of apartments using machine
learning.


title:
Precise radial velocities of giant stars XVI. Planet occurrence rates
  from the combined analysis of the Lick, EXPRESS, and PPPS giant star surveys

abstract:
  RV surveys of evolved stars allow us to probe a higher stellar mass range
compared to main-sequence samples. Differences between the planet populations
can be caused by either the differing stellar mass or stellar evolution. To
properly disentangle the effects of both, the planet population around giant
stars needs to be characterized as accurately as possible. Our goal is to
investigate the giant planet occurrence rate around evolved stars and determine
its dependence on stellar mass, metallicity, and orbital period. We combine
data from the Lick, EXPRESS, and PPPS giant star surveys, yielding a sample of
482 evolved stars and 37 planets. We homogeneously rederived the stellar
parameters and accounted for varying observational coverage, precision, and
stellar noise properties by computing detection maps via injection and
retrieval of synthetic planetary signals. We then computed occurrence rates as
a function of period, stellar mass, and metallicity, corrected for
incompleteness. Our findings agree with previous studies that found a positive
planet-metallicity correlation for evolved stars and identified a peak in the
occurrence rate as a function of stellar mass, but our results place it at a
slightly smaller mass of 1.68Msun. The period dependence of the occurrence rate
seems to follow a broken power-law or log-normal peaking at 700-800 days,
roughly corresponding to 1.6AU for a 1Msun star and 2.0AU for a 2Msun star.
This peak could be a remnant from halted migration around intermediate-mass
stars, caused by stellar evolution, or an artifact from contamination by false
positives. The global occurrence rate of giant planetary systems is 10.7% for
the entire sample, while the subsets of RGB and HB stars exhibit 14.2% and
6.6%, respectively. However, we demonstrate that the different stellar mass
distributions suffice to explain the apparent change of occurrence with
evolutionary stage.


title: On the data requirements of probing

abstract:
  As large and powerful neural language models are developed, researchers have
been increasingly interested in developing diagnostic tools to probe them.
There are many papers with conclusions of the form "observation X is found in
model Y", using their own datasets with varying sizes. Larger probing datasets
bring more reliability, but are also expensive to collect. There is yet to be a
quantitative method for estimating reasonable probing dataset sizes. We tackle
this omission in the context of comparing two probing configurations: after we
have collected a small dataset from a pilot study, how many additional data
samples are sufficient to distinguish two different configurations? We present
a novel method to estimate the required number of data samples in such
experiments and, across several case studies, we verify that our estimations
have sufficient statistical power. Our framework helps to systematically
construct probing datasets to diagnose neural NLP models.


title: Probabilistic Data Association for Semantic SLAM at Scale

abstract:
  With advances in image processing and machine learning, it is now feasible to
incorporate semantic information into the problem of simultaneous localisation
and mapping (SLAM). Previously, SLAM was carried out using lower level
geometric features (points, lines, and planes) which are often view-point
dependent and error prone in visually repetitive environments. Semantic
information can improve the ability to recognise previously visited locations,
as well as maintain sparser maps for long term SLAM applications. However, SLAM
in repetitive environments has the critical problem of assigning measurements
to the landmarks which generated them. In this paper, we use k-best assignment
enumeration to compute marginal assignment probabilities for each measurement
landmark pair, in real time. We present numerical studies on the KITTI dataset
to demonstrate the effectiveness and speed of the proposed framework.


title:
Development of a Model Predictive Airpath Controller for a Diesel Engine
  on a High-Fidelity Engine Model with Transient Thermal Dynamics

abstract:
  This paper presents the results of a model predictive controller (MPC)
development for diesel engine air-path regulation. The control objective is to
track the intake manifold pressure and exhaust gas recirculation (EGR) rate
targets by manipulating the EGR valve and variable geometry turbine (VGT) while
satisfying state and control constraints. The MPC controller is designed and
verified using a high-fidelity engine model in GT-Power. The controller
exploits a low-order rate-based linear parameter-varying (LPV) model for
prediction which is identified from transient response data generated by the
GT-Power model. It is shown that transient engine thermal dynamics influence
the airpath dynamics, specifically the intake manifold pressure response,
however, MPC demonstrates robustness against inaccuracies in modeling these
thermal dynamics. In particular, we show that MPC can be successfully
implemented using a rate-based prediction model with two inputs (EGR and VGT
positions) identified from data with steady-state wall temperature dynamics,
however, closed-loop performance can be improved if a prediction model (i) is
identified from data with transient thermal dynamics, and (ii) has the fuel
injection rate as extra model input. Further, the MPC calibration process
across the engine operating range to achieve improved performance is addressed.
As the MPC calibration is shown to be sensitive to the operating conditions, a
fast calibration process is proposed.


title: Hadronization and Color Transparency

abstract:
  In this article we review our work on the production of hadrons in the
nuclear environment. We work with a string-breaking model for the initial
production of hadrons and with a quantum-kinetic transport model (GiBUU) to
describe the final state interactions of the newly formed (pre)hadrons. The
latter are determined both by the formation times and by the time-development
of the hadron-hadron cross section. We first show that only a linear
time-dependence is able to describe the available hadronization data. We then
compare with detailed data from HERMES and JLAB; very good agreement is reached
in all reactions studied without any tuning of parameters. We also repeat
predictions of spectra for pions and kaons at JLAB@12GeV. We finally explain
the absence of CT effects in the recent experiment on proton transparencies in
quasielastic (QE) events on nuclei. We propose to look instead for CT effects
on protons in semi-inclusive DIS (SIDIS) events.


title: Integrable sigma models on Riemann surfaces

abstract:
  We consider quantum aspects of a class of generalized Gross-Neveu models,
which in special cases reduce to sigma models. We show that, in the case of
gauged models, an admissible gauge is $A_\mu=0$, which is a direct analogue of
the conformal gauge in string models. Chiral anomalies are a gauge counterpart
of the Weyl anomaly, and are required to vanish. Topological effects on the
worldsheet lead to an integration over moduli spaces of connections on a
Riemann surface. This is an initial step in studying the effects of worldsheet
geometry and topology in integrable sigma models.


title: Deep learning-assisted imaging through stationary scattering media

abstract:
  Imaging through scattering media is a challenging problem owing to speckle
decorrelations from perturbations in the media itself. For in-line imaging
modalities, which are appealing because they are compact, require no moving
parts, and are robust, negating the effects of such scattering becomes
particularly challenging. Here we explore the effect of stationary scattering
media on light scattering in in-line geometries, including digital holographic
microscopy. We consider various object-scatterer scenarios where the object is
distorted or obscured by additional stationary scatterers, and use an advanced
deep learning (DL) generative methodology, generative adversarial networks
(GANs), to mitigate the effects of the additional scatterers. Using light
scattering simulations and experiments on objects of interest with and without
additional scatterers, we find that conditional GANs can be quickly trained
with minuscule datasets and can also efficiently learn the one-to-one
statistical mapping between the cross-domain input-output image pairs. Training
such a network yields a standalone model, that can be used later to inverse or
negate the effect of scattering, yielding clear object reconstructions for
object retrieval and downstream processing. Moreover, it is well-known that the
coherent point spread function (c-PSF) of a stationary scattering optical
system is a speckle pattern which is spatially shift variant. We show that with
rapid training using only 20 image pairs, it is possible to negate this
undesired scattering to accurately localize diffraction-limited impulses with
high spatial accuracy, therefore transforming the earlier shift variant system
to a linear shift invariant (LSI) system.


title:
EXFOR-NSR PDF database: a system for nuclear knowledge preservation and
  data curation

abstract:
  Current needs of nuclear science and technology include complete,
well-documented, and easily verifiable nuclear data. The complete data records
require supporting nuclear bibliography, presently stored in dedicated
libraries, in addition, to actual data. Experimental nuclear reaction data
(EXFOR) and Nuclear Science References (NSR) databases contain compilations
based on primary (journals) and secondary (conference proceedings, theses,
preprints, etc.) publications, and data received from authors via private
communications. The secondary library materials and private communications
often represent a bottleneck for nuclear data verification, compilation,
evaluation, and dissemination activities. To address this issue, bibliographic
materials were scanned into PDF (Portable Document Format) files and uploaded
in a relational database. The traditional scope of nuclear databases that
includes meta-data and numbers derived from data in specialized formats was
broadened to accommodate the large volumes of original nuclear data
publications. The complete PDF publication files were stored in a relational
database as Binary Large OBjects (BLOB). This unique collection of nuclear data
compilations and supporting publications generate many opportunities for
machine learning applications.


title: High-Dimensional Sparse Bayesian Learning without Covariance Matrices

abstract:
  Sparse Bayesian learning (SBL) is a powerful framework for tackling the
sparse coding problem. However, the most popular inference algorithms for SBL
become too expensive for high-dimensional settings, due to the need to store
and compute a large covariance matrix. We introduce a new inference scheme that
avoids explicit construction of the covariance matrix by solving multiple
linear systems in parallel to obtain the posterior moments for SBL. Our
approach couples a little-known diagonal estimation result from numerical
linear algebra with the conjugate gradient algorithm. On several simulations,
our method scales better than existing approaches in computation time and
memory, especially for structured dictionaries capable of fast matrix-vector
multiplication.


title:
Design development and implementation of the NEAR area and its neutron
  irradiation station at the n_TOF facility at CERN

abstract:
  A new parasitic, mixed-field, neutron-dominated irradiation station has been
recently commissioned at the European Laboratory for Particle Physics (CERN).
The station is installed in the Neutron Time-Of-Flight (n\_TOF) facility,
taking advantage of the secondary radiation produced by the neutron spallation
target. The new station allows radiation damage studies to be performed in
irradiation conditions that are closer to the ones encountered during the
operation of particle accelerators; the irradiation tests carried out in the
station will be complementary to the standard tests on materials, usually
performed with gamma sources. Samples will be exposed to neutron doses in the
MGy range per year, with minimal impact on the n TOF facility operation. The
station has twenty-four irradiation positions, each hosting up to 100 cm3 of
sample material. In view of its proximity to the n\_TOF target, inside
protective shielding, the irradiation station and its operating procedures have
been carefully developed taking into account the safety of personnel and to
avoid any unwanted impact on the operation of the n\_TOF facility and
experiments. Due to the residual radioactivity of the whole area around the
n\_TOF target and of the irradiated samples, access to the irradiation station
is forbidden to human operators even when the n\_TOF facility is not in
operation. Robots are used for the remote installation and retrieval of the
samples, and other optimizations of the handling procedures were developed in
compliance with radiation protection regulations and the aim of minimizing
doses to personnel.


title: Dynamical systems on directed hyper-graphs

abstract:
  Networks and graphs provide a simple but effective model to a vast set of
systems which building blocks interact throughout pairwise interactions. Such
models fail to describe all those systems which building blocks interact at a
higher order. Higher order graphs provide us the right tools for the task. We
analyze the interplay between the structure of a directed hyper-graph and a
dynamical system evolving on it. We connect the dynamical system on the h to a
corresponding random walk on an effective graph. Measures of hyper-dynamics
correspond to similar measures on the effective dynamics. We can use simple and
well tested algorithms on the effective graph.


title:
Does an increase in the cost of imported inputs hurt exports? Evidence
  from firms' network of foreign suppliers

abstract:
  This paper examines the relationship between changes in the cost of imported
inputs and export performance using a novel dataset from Argentina which
identifies domestic firms' network of foreign suppliers. To guide my empirical
strategy, I construct a heterogeneous firm model subject to quality choice and
frictions in the market for foreign supplier. The model predicts that the
impact of an increase in the cost of imported inputs to be increasing in the
adjustments costs of supplier linkages and in the quality of the product
exported. I take the model to the data by constructing firm-specific shocks
using a shift-share analysis which exploits firms' lagged exposure to foreign
suppliers and finely defined import price shifts. Evidence suggests the
presence of significant adjustment cost in firms' foreign supplier linkages and
strong complementarities between imported inputs and export performance,
particularly of high-quality products.


title:
Ab initio study on the atomic and electronic structures of twisted InSe
  bilayer

abstract:
  The electronic properties of the twisted InSe bilayer are studied by
large-scale density functional theory. Spectral Function Unfolding reveals that
the electronic structure of the twisted system can be described in terms of a
combination of features of the bandstructures of the aligned InSe bilayer with
different stacking configurations, enabling predictions of the band gap and the
effective mass for holes. The effective mass for holes in the twisted InSe
bilayer is shown to be influenced primarily by the interlayer distance. The
intralayer and interlayer exciton binding energies are thus calculated based on
a model recently developed by Ruiz-Tijerina et al. We apply similar analysis to
the trilayer heterostructure InSe/hBN/InSe: its electronic structure is shown
to be well-described by the superposition of band structures of two InSe
monolayers with a small coupling through the hBN layer.


title:
Causal discovery for observational sciences using supervised machine
  learning

abstract:
  Causal inference can estimate causal effects, but unless data are collected
experimentally, statistical analyses must rely on pre-specified causal models.
Causal discovery algorithms are empirical methods for constructing such causal
models from data.
  Several asymptotically correct methods already exist, but they generally
struggle on smaller samples. Moreover, most methods focus on very sparse causal
models, which may not always be a realistic representation of real-life data
generating mechanisms. Finally, while causal relationships suggested by the
methods often hold true, their claims about causal non-relatedness have high
error rates. This non-conservative error tradeoff is not ideal for
observational sciences, where the resulting model is directly used to inform
causal inference: A causal model with many missing causal relations entails too
strong assumptions and may lead to biased effect estimates.
  We propose a new causal discovery method that addresses these three
shortcomings: Supervised learning discovery (SLdisco). SLdisco uses supervised
machine learning to obtain a mapping from observational data to equivalence
classes of causal models.
  We evaluate SLdisco in a large simulation study based on Gaussian data and we
consider several choices of model size and sample size. We find that SLdisco is
more conservative, only moderately less informative and less sensitive towards
sample size than existing procedures.
  We furthermore provide a real epidemiological data application. We use random
subsampling to investigate real data performance on small samples and again
find that SLdisco is less sensitive towards sample size and hence seems to
better utilize the information available in small datasets.