title:
Entropy production in non-Markovian collision models: Information
  backflow vs system-environment correlations

abstract:
  We investigate the irreversible entropy production of a qubit in contact with
an environment modelled by a microscopic collision model both in Markovian and
non-Markovian regimes. Our main goal is to contribute to the discussions on the
relationship between non-Markovian dynamics and negative entropy production
rates. We employ two different types of collision models that do or do not keep
the correlations established between the system and the incoming environmental
particle, while both of them pertain to their non-Markovian nature through
information backflow from the environment to the system. We observe that as the
former model, where the correlations between the system and environment is
preserved, gives rise to negative entropy production rates in the transient
dynamics, the latter one always maintains positive rates, even though the
convergence to the steady-state value is slower as compared to the
corresponding Markovian dynamics. Our results suggest that the mechanism
underpinning the negative entropy production rates is not solely
non-Markovianity through information backflow, but rather the contribution to
it through established system-environment correlations.


title: SILVR: A Synthetic Immersive Large-Volume Plenoptic Dataset

abstract:
  In six-degrees-of-freedom light-field (LF) experiences, the viewer's freedom
is limited by the extent to which the plenoptic function was sampled. Existing
LF datasets represent only small portions of the plenoptic function, such that
they either cover a small volume, or they have limited field of view.
Therefore, we propose a new LF image dataset "SILVR" that allows for
six-degrees-of-freedom navigation in much larger volumes while maintaining full
panoramic field of view. We rendered three different virtual scenes in various
configurations, where the number of views ranges from 642 to 2226. One of these
scenes (called Zen Garden) is a novel scene, and is made publicly available. We
chose to position the virtual cameras closely together in large cuboid and
spherical organisations ($2.2m^3$ to $48m^3$), equipped with 180{\deg} fish-eye
lenses. Every view is rendered to a color image and depth map of 2048px
$\times$ 2048px. Additionally, we present the software used to automate the
multi-view rendering process, as well as a lens-reprojection tool that converts
between images with panoramic or fish-eye projection to a standard rectilinear
(i.e., perspective) projection. Finally, we demonstrate how the proposed
dataset and software can be used to evaluate LF coding/rendering techniques(in
this case for training NeRFs with instant-ngp). As such, we provide the first
publicly-available LF dataset for large volumes of light with full panoramic
field of view


title:
An Empirical Study on the Relationship Between the Number of Coordinated
  Views and Visual Analysis

abstract:
  Coordinated Multiple views (CMVs) are a visualization technique that
simultaneously presents multiple visualizations in separate but linked views.
There are many studies that report the advantages (e.g., usefulness for finding
hidden relationships) and disadvantages (e.g., cognitive load) of CMVs. But
little empirical work exists on the impact of the number of views on visual
anlaysis results and processes, which results in uncertainty in the
relationship between the view number and visual anlaysis. In this work, we aim
at investigating the relationship between the number of coordinated views and
users analytic processes and results. To achieve the goal, we implemented a CMV
tool for visual anlaysis. We also provided visualization duplication in the
tool to help users easily create a desired number of visualization views
on-the-fly. We conducted a between-subject study with 44 participants, where we
asked participants to solve five analytic problems using the visual tool.
Through quantitative and qualitative analysis, we discovered the positive
correlation between the number of views and analytic results. We also found
that visualization duplication encourages users to create more views and to
take various analysis strategies. Based on the results, we provide implications
and limitations of our study.


title:
Tunable and giant valley-selective Hall effect in gapped bilayer
  graphene

abstract:
  Berry curvature is analogous to magnetic field but in momentum space and is
commonly present in materials with non-trivial quantum geometry. It endows
Bloch electrons with transverse anomalous velocities to produce Hall-like
currents even in the absence of a magnetic field. We report the direct
observation of in situ tunable valley-selective Hall effect (VSHE), where
inversion symmetry, and thus the geometric phase of electrons, is controllable
by an out-of-plane electric field. We use high-quality bilayer graphene with an
intrinsic and tunable bandgap, illuminated by circularly polarized mid-infrared
light and confirm that the observed Hall voltage arises from an
optically-induced valley population. Compared with molybdenum disulfide, we
find orders of magnitude larger VSHE, attributed to the inverse scaling of the
Berry curvature with bandgap. By monitoring the valley-selective Hall
conductivity, we study Berry curvature's evolution with bandgap. This in situ
manipulation of VSHE paves the way for topological and quantum geometric
opto-electronic devices, such as more robust switches and detectors.


title:
Modeling Review History for Reviewer Recommendation:A Hypergraph
  Approach

abstract:
  Modern code review is a critical and indispensable practice in a pull-request
development paradigm that prevails in Open Source Software (OSS) development.
Finding a suitable reviewer in projects with massive participants thus becomes
an increasingly challenging task. Many reviewer recommendation approaches
(recommenders) have been developed to support this task which apply a similar
strategy, i.e. modeling the review history first then followed by
predicting/recommending a reviewer based on the model. Apparently, the better
the model reflects the reality in review history, the higher recommender's
performance we may expect. However, one typical scenario in a pull-request
development paradigm, i.e. one Pull-Request (PR) (such as a revision or
addition submitted by a contributor) may have multiple reviewers and they may
impact each other through publicly posted comments, has not been modeled well
in existing recommenders. We adopted the hypergraph technique to model this
high-order relationship (i.e. one PR with multiple reviewers herein) and
developed a new recommender, namely HGRec, which is evaluated by 12 OSS
projects with more than 87K PRs, 680K comments in terms of accuracy and
recommendation distribution. The results indicate that HGRec outperforms the
state-of-the-art recommenders on recommendation accuracy. Besides, among the
top three accurate recommenders, HGRec is more likely to recommend a diversity
of reviewers, which can help to relieve the core reviewers' workload congestion
issue. Moreover, since HGRec is based on hypergraph, which is a natural and
interpretable representation to model review history, it is easy to accommodate
more types of entities and realistic relationships in modern code review
scenarios. As the first attempt, this study reveals the potentials of
hypergraph on advancing the pragmatic solutions for code reviewer
recommendation.


title: Formality and strongly unique enhancements

abstract:
  Inspired by the intrinsic formality of graded algebras, we prove a necessary
and sufficient condition for strongly uniqueness of DG-enhancements. This
approach offers a generalization to linearity over any commutative ring. In
particular, we obtain several new examples of triangulated categories with a
strongly unique DG-enhancement.


title:
Study of $N(1520)$ and $N(1535)$ structures via $\gamma^*p\to N^*$
  transitions

abstract:
  The helicity amplitudes of the $N(1520)$ and $N(1535)$ resonances in the
$\gamma^*p\to N^*$ electromagnetic transition are studied in the constituent
quark model using the impulse approximation, with the proton and resonances
assumed to be in three-quark configurations. The comparison of theoretical
results and experimental data on the helicity amplitudes $A_{1/2}$, $A_{3/2}$,
and $S_{1/2}$ indicates that the $N(1520)$ and $N(1535)$ resonances are
primarily composed of three-quark $L=1$ states but may contain additional
components. However, it is improbable that contributions from meson clouds will
be dominant at low $Q^2$.


title: Challenges in `seeing' through particulate materials

abstract:
  In this perspective article, we discuss the challenges of imaging granular
particles in three dimensions. Starting with a brief motivation for
investigating particulate materials, we provide an overview of selected
experimental approaches developed for studying static or dynamic granular
systems. We then list some of the challenges and solutions associated with
X-ray tomography, one of the standard methods to study static packings.
Subsequently, we discuss new techniques such as `smart' tracer and radar
tracking for granular dynamics. We close by giving our personal view on the
outstanding problems and potential solutions in the future.


title:
Integral representation and $\Gamma$-convergence for free-discontinuity
  problems with $p(\cdot)$-growth

abstract:
  An integral representation result for free-discontinuity energies defined on
the space $GSBV^{p(\cdot)}$ of generalized special functions of bounded
variation with variable exponent is proved, under the assumption of
log-H\"older continuity for the variable exponent $p(x)$. Our analysis is based
on a variable exponent version of the global method for relaxation devised in
Bouchitt\`e, Fonseca, Leoni and Mascarenhas '98 for a constant exponent. We
prove $\Gamma$-convergence of sequences of energies of the same type, we
identify the limit integrands in terms of asymptotic cell formulas and prove a
non-interaction property between bulk and surface contributions.


title:
Echoes in star quasinormal modes using an alternative finite difference
  method

abstract:
  In this work, the echoes in quasinormal modes of axial gravitational
perturbations in the compact star are investigated. We propose an alternative
scheme of the finite difference method implemented in two coordinate systems,
where the initial conditions are placed on the axis of the tortoise coordinate
with appropriate boundary conditions that fully respect the causality. The
scheme is then used to study the temporal profiles of the quasinormal
oscillations in the Schwarzschild black hole and the uniform density stars.
When presented in a two-dimensional evolution profile, the resulting ringdown
waveforms in the black hole metric are split into the reflected and transmitted
waves as the initial perturbations evolve and collide with the peak of the
effective potential. On the other hand, for compact stars, quasinormal
oscillations might be characterized by echoes. Consistent with the causality
arguments, the phenomenon is produced by the gravitational waves bouncing
between the divergent potential at the star's center and the peak of the
effective potential. The implications of the present study are also discussed.


title:
Gaussian mixture model on nodes of Bayesian network given maximal
  parental cliques

abstract:
  This paper uses Gaussian mixture model instead of linear Gaussian model to
fit the distribution of every node in Bayesian network. We will explain why and
how we use Gaussian mixture models in Bayesian network. Meanwhile we propose a
new method, called double iteration algorithm, to optimize the mixture model,
the double iteration algorithm combines the expectation maximization algorithm
and gradient descent algorithm, and it performs perfectly on the Bayesian
network with mixture models. In experiments we test the Gaussian mixture model
and the optimization algorithm on different graphs which is generated by
different structure learning algorithm on real data sets, and give the details
of every experiment.


title: Evaluating Commit Message Generation: To BLEU Or Not To BLEU?

abstract:
  Commit messages play an important role in several software engineering tasks
such as program comprehension and understanding program evolution. However,
programmers neglect to write good commit messages. Hence, several Commit
Message Generation (CMG) tools have been proposed. We observe that the recent
state of the art CMG tools use simple and easy to compute automated evaluation
metrics such as BLEU4 or its variants. The advances in the field of Machine
Translation (MT) indicate several weaknesses of BLEU4 and its variants. They
also propose several other metrics for evaluating Natural Language Generation
(NLG) tools. In this work, we discuss the suitability of various MT metrics for
the CMG task. Based on the insights from our experiments, we propose a new
variant specifically for evaluating the CMG task. We re-evaluate the state of
the art CMG tools on our new metric. We believe that our work fixes an
important gap that exists in the understanding of evaluation metrics for CMG
research.


title: Statistics for Heteroscedastic Time Series Extremes

abstract:
  Einmahl, de Haan and Zhou (2016, Journal of the Royal Statistical Society:
Series B, 78(1), 31-51) recently introduced a stochastic model that allows for
heteroscedasticity of extremes. The model is extended to the situation where
the observations are serially dependent, which is crucial for many practical
applications. We prove a local limit theorem for a kernel estimator for the
scedasis function, and a functional limit theorem for an estimator for the
integrated scedasis function. We further prove consistency of a bootstrap
scheme that allows to test for the null hypothesis that the extremes are
homoscedastic. Finally, we propose an estimator for the extremal index
governing the dynamics of the extremes and prove its consistency. All results
are illustrated by Monte Carlo simulations. An important intermediate result
concerns the sequential tail empirical process under serial dependence.


title: Theoretical analysis of edit distance algorithms: an applied perspective

abstract:
  Given its status as a classic problem and its importance to both
theoreticians and practitioners, edit distance provides an excellent lens
through which to understand how the theoretical analysis of algorithms impacts
practical implementations. From an applied perspective, the goals of
theoretical analysis are to predict the empirical performance of an algorithm
and to serve as a yardstick to design novel algorithms that perform well in
practice. In this paper, we systematically survey the types of theoretical
analysis techniques that have been applied to edit distance and evaluate the
extent to which each one has achieved these two goals. These techniques include
traditional worst-case analysis, worst-case analysis parametrized by edit
distance or entropy or compressibility, average-case analysis, semi-random
models, and advice-based models. We find that the track record is mixed. On one
hand, two algorithms widely used in practice have been born out of theoretical
analysis and their empirical performance is captured well by theoretical
predictions. On the other hand, all the algorithms developed using theoretical
analysis as a yardstick since then have not had any practical relevance. We
conclude by discussing the remaining open problems and how they can be tackled.


title: Approximate Riemannian manifolds by polyhedra

abstract:
  This is a study on approximating a Riemannian manifold by polyhedra. Our
scope is understanding Tullio Regge's [52] article in the restricted Riemannian
frame. We give a proof of the Regge theorem along lines close to its original
intuition: one can approximate a compact domain of a Riemannian manifold by
polyhedra in such a way that the integral of the scalar curvature is
approximated by a corresponding polyhedral curvature.


title: A Rydberg hydrogen beam for studies of stimulated deexcitation

abstract:
  We present a Rydberg hydrogen beamline developed to commission techniques of
stimulated deexcitation for application in antihydrogen experiments at CERN's
Antiproton Decelerator. The stimulation of spontaneous decay is a key
technology to enhance the number of ground-state anti-atoms available in a beam
toward precision spectroscopy and gravity measurements.


title: Quality of Service in Quantum Networks

abstract:
  In the coming years, quantum networks will allow quantum applications to
thrive thanks to the new opportunities offered by end-to-end entanglement of
qubits on remote hosts via quantum repeaters. On a geographical scale, this
will lead to the dawn of the Quantum Internet. While a full-blown deployment is
yet to come, the research community is already working on a variety of
individual enabling technologies and solutions. In this paper, with the
guidance of extensive simulations, we take a broader view and investigate the
problems of Quality of Service (QoS) and provisioning in the context of quantum
networks, which are very different from their counterparts in classical data
networks due to some of their fundamental properties. Our work leads the way
towards a new class of studies that will allow the research community to better
understand the challenges of quantum networks and their potential commercial
exploitation.


title: A Comprehensive Study of Accelerating IPv6 Deployment

abstract:
  Since the lack of IPv6 network development, China is currently accelerating
IPv6 deployment. In this scenario, traffic and network structure show a huge
shift. However, due to the long-term prosperity, we are ignorant of the
problems behind such outbreak of traffic and performance improvement events in
accelerating deployment. IPv6 development in some regions will still face
similar challenges in the future. To contribute to solving this problem, in
this paper, we produce a new measurement framework and implement a 5-month
passive measurement on the IPv6 network during the accelerating deployment in
China. We combine 6 global-scale datasets to form the normal status of IPv6
network, which is against to the accelerating status formed by the passive
traffic. Moreover, we compare with the traffic during World IPv6 Day 2011 and
Launch 2012 to discuss the common nature of accelerating deployment. Finally,
the results indicate that the IPv6 accelerating deployment is often accompanied
by an unbalanced network status. It exposes unresolved security issues
including the challenge of user privacy and inappropriate access methods.
According to the investigation, we point the future IPv6 development after
accelerating deployment.


title: Inductive Freeness of Ziegler's Canonical Multiderivations

abstract:
  Let $\mathcal A$ be a free hyperplane arrangement. In 1989, Ziegler showed
that the restriction $\mathcal A"$ of $\mathcal A$ to any hyperplane endowed
with the natural multiplicity $\kappa$ is then a free multiarrangement
$(\mathcal A",\kappa)$. The aim of this paper is to prove an analogue of
Ziegler's theorem for the stronger notion of inductive freeness: if $\mathcal
A$ is inductively free, then so is the multiarrangement $(\mathcal A",\kappa)$.
In a related result we derive that if a deletion $\mathcal A'$ of $\mathcal A$
is free and the corresponding restriction $\mathcal A"$ is inductively free,
then so is $(\mathcal A",\kappa)$ -- irrespective of the freeness of $\mathcal
A$. In addition, we show counterparts of the latter kind for additive and
recursive freeness.


title:
Search for the doubly heavy baryon $\it{\Xi}_{bc}^{+}$ decaying to
  $J/\it{\psi} \it{\Xi}_{c}^{+}$

abstract:
  A first search for the $\it{\Xi}_{bc}^{+}\to J/\it{\psi}\it{\Xi}_{c}^{+}$
decay is performed by the LHCb experiment with a data sample of proton-proton
collisions, corresponding to an integrated luminosity of $9\,\mathrm{fb}^{-1}$
recorded at centre-of-mass energies of 7, 8, and $13\mathrm{\,Te\kern -0.1em
V}$. Two peaking structures are seen with a local (global) significance of
$4.3\,(2.8)$ and $4.1\,(2.4)$ standard deviations at masses of
$6571\,\mathrm{Me\kern -0.1em V\!/}c^2$ and $6694\,\mathrm{Me\kern -0.1em
V\!/}c^2$, respectively. Upper limits are set on the $\it{\Xi}_{bc}^{+}$ baryon
production cross-section times the branching fraction relative to that of the
$B_{c}^{+}\to J/\it{\psi} D_{s}^{+}$ decay at centre-of-mass energies of 8 and
$13\mathrm{\,Te\kern -0.1em V}$, in the $\it{\Xi}_{bc}^{+}$ and in the
$B_{c}^{+}$ rapidity and transverse-momentum ranges from 2.0 to 4.5 and 0 to
$20\,\mathrm{Ge\kern -0.1em V\!/}c$, respectively. Upper limits are presented
as a function of the $\it{\Xi}_{bc}^{+}$ mass and lifetime.


title:
A Spectroscopic Measurement of High Velocity Spray Plasma from an
  M-class Flare and Coronal Mass Ejection

abstract:
  Coronal mass ejection spray plasma associated with the M1.5-class flare of 16
February 2011 is found to exhibit a Doppler blue-shift of 850 km/s - the
largest value yet reported from ultraviolet (UV) or extreme ultraviolet (EUV)
spectroscopy of the solar disk and inner corona. The observation is unusual in
that the emission line (Fe XII 193.51 A) is not observed directly, but the
Doppler shift is so large that the blue-shifted component appears in a
wavelength window at 192.82 A, intended to observe lines of O V, Fe XI and Ca
XVII. The Fe XII 195.12 A emission line is used as a proxy for the rest
component of 193.51 A. The observation highlights the risks of using narrow
wavelength windows for spectrometer observations when observing highly-dynamic
solar phenomena. The consequences of large Doppler shifts for ultraviolet solar
spectrometers, including the upcoming Multi-slit Solar Explorer (MUSE) mission,
are discussed.


title: Variability-aware Memristive Crossbars -- A Tutorial

abstract:
  Memristor crossbar architecture is one of the most popular circuit
configurations due to its wide range of practical applications. The crossbar
architecture can emulate the weighted summation operation, called multiply and
accumulate operation (MAC). The errors to MAC computing get introduced due to a
range of crossbar variability. We broadly group the variability in three
categories: (1) device-to-device variations, (2) programming nonlinearity, and
(3) those from peripheral circuits. This tutorial provides insights into the
variability and compensation approaches that can be adopted to reduce its
impact when designing for practical applications with crossbars.


title:
Digging into Primary Financial Market: Challenges and Opportunities of
  Adopting Blockchain

abstract:
  Since the emergence of blockchain technology, its application in the
financial market has always been an area of focus and exploration by all
parties. With the characteristics of anonymity, trust, tamper-proof, etc.,
blockchain technology can effectively solve some problems faced by the
financial market, such as trust issues and information asymmetry issues. To
deeply understand the application scenarios of blockchain in the financial
market, the issue of securities issuance and trading in the primary market is a
problem that must be studied clearly. We conducted an empirical study to
investigate the main difficulties faced by primary market participants in their
business practices and the potential challenges of the deepening application of
blockchain technology in the primary market. We adopted a hybrid method
combining interviews (qualitative methods) and surveys (quantitative methods)
to conduct this research in two stages. In the first stage, we interview 15
major primary market participants with different backgrounds and expertise. In
the second phase, we conducted a verification survey of 54 primary market
practitioners to confirm various insights from the interviews, including
challenges and desired improvements. Our interviews and survey results revealed
several significant challenges facing blockchain applications in the primary
market: complex due diligence, mismatch, and difficult monitoring. On this
basis, we believe that our future research can focus on some aspects of these
challenges.


title: Singular limits for stochastic equations

abstract:
  We study singular limits of stochastic evolution equations in the interplay
of disappearing strength of the noise and increasing roughness of the noise, so
that the noise in the limit would be too rough to define a solution to the
limiting equations. Simultaneously, the limit is singular in the sense that the
leading order differential operator may vanish. Although the noise is
disappearing in the limit, additional deterministic terms appear due to
renormalization effects.
  We give an abstract framework for the main error estimates, that first reduce
to bounds on a residual and in a second step to bounds on the stochastic
convolution. Moreover, we apply it to a singularly regularized Allen-Cahn
equation and the Cahn-Hilliard/Allen-Cahn homotopy.


title:
Ferroelectricity and topological vortices from molecular ordering in
  metal-organic frameworks

abstract:
  Metal-organic frameworks comprehend a wide class of hybrid organic-inorganic
materials with general structure A$_m$BX$_n$, with $A$ and $X$ being organic
molecules and B a metal cation. This often results in enhanced structural
flexibility and new functionalities. Hybrid perovskites ABX$_3$ are a
well-known example.} In an Iron-based perovskites, (DMA)Fe^{II-III}(COOH)_3,
dimethylammonium (DMA) molecules are organized in a hexagonal structure. They
are orientationally disordered at high temperatures, but order at around
$T=100$~K in a peculiar toroidal pattern. Recent experimental and theoretical
study suggest the appearance of ferroelectric polarization in this phase,
although the measured polarization is small, and the mechanism of
ferroelectricity is still debated. We formulate a Landau-type theory that
clarifies the connection between the electric polarization, molecular pattern,
and distortive modes of the inorganic lattice. We find a remarkable mechanism
of improper ferroelectricity, analogue to the trimerization process in
inorganic hexagonal ferrites and manganites, but here driven by the ordering of
organic molecules in a metal-organic framework. Our study reveals an extremely
rich phase diagram with the prediction of topological domain walls, where the
ferroelectricity arise from tripling the unit cells due to molecular ordering.
Wide domain walls with inner structure are predicted.


title: Dependent Optics

abstract:
  A wide variety of bidirectional data accessors, ranging from mixed optics to
functor lenses, can be formalized within a unique framework--dependent optics.
Starting from two indexed categories, which encode what maps are allowed in the
forward and backward directions, we define a category of dependent optics and
establish under what assumptions it has coproducts. Different choices of
indexed categories correspond to different families of optics: we discuss
dependent lenses and prisms, as well as closed dependent optics. We introduce
the notion of Tambara representation and use it to classify contravariant
functors from the category of optics, thus generalizing the profunctor encoding
of optics to the dependent case.


title:
Misinformed by Visualization: What Do We Learn From Misinformative
  Visualizations?

abstract:
  Data visualization is powerful in persuading an audience. However, when it is
done poorly or maliciously, a visualization may become misleading or even
deceiving. Visualizations give further strength to the dissemination of
misinformation on the Internet. The visualization research community has long
been aware of visualizations that misinform the audience, mostly associated
with the terms "lie" and "deceptive." Still, these discussions have focused
only on a handful of cases. To better understand the landscape of misleading
visualizations, we open-coded over one thousand real-world visualizations that
have been reported as misleading. From these examples, we discovered 74 types
of issues and formed a taxonomy of misleading elements in visualizations. We
found four directions that the research community can follow to widen the
discussion on misleading visualizations: (1) informal fallacies in
visualizations, (2) exploiting conventions and data literacy, (3) deceptive
tricks in uncommon charts, and (4) understanding the designers' dilemma. This
work lays the groundwork for these research directions, especially in
understanding, detecting, and preventing them.


title:
Isogeometric boundary element method for acoustic scattering by a
  submarine

abstract:
  Isogeometric analysis with the boundary element method (IGABEM) has recently
gained interest. In this paper, the approximability of IGABEM on 3D acoustic
scattering problems will be investigated and a new improved BeTSSi submarine
will be presented as a benchmark example. Both Galerkin and collocation are
considered in combination with several boundary integral equations (BIE). In
addition to the conventional BIE, regularized versions of this BIE will be
considered. Moreover, the hyper-singular BIE and the Burton--Miller formulation
are also considered. A new adaptive integration routine is presented, and the
numerical examples show the importance of the integration procedure in the
boundary element method. The numerical examples also include comparison between
standard BEM and IGABEM, which again verifies the higher accuracy obtained from
the increased inter-element continuity of the spline basis functions. One of
the main objectives in this paper is benchmarking acoustic scattering problems,
and the method of manufactured solution will be used frequently in this regard.


title:
Comment on "Scattering of light by a parity-time-symmetric dipole beyond
  the first Born approximation"

abstract:
  In [J. A. Rebou\c{c}as and P. A. Brand\~{a}o, Phys. Rev. A 104, 063514
(2021)] the authors compute the scattering amplitude for a
$\mathcal{P}\mathcal{T}$-symmetric double-delta-function potential in three
dimensions by invoking the far-zone approximation and summing the resulting
Born series. We show that the analysis of this paper suffers from a basic
error. Therefore its results are inconclusive. We give an exact closed-form
expression for the scattering amplitude of this potential.


title:
High fidelity state preparation, quantum control, and readout of an
  isotopically enriched silicon spin qubit

abstract:
  Quantum systems must be prepared, controlled, and measured with high fidelity
in order to perform complex quantum algorithms. Control fidelities have greatly
improved in silicon spin qubits, but state preparation and readout fidelities
have generally been poor. By operating with low electron temperatures and
employing high-bandwidth cryogenic amplifiers, we demonstrate single qubit
readout visibilities >99%, exceeding the threshold for quantum error
correction. In the same device, we achieve average single qubit control
fidelities >99.95%. Our results show that silicon spin qubits can be operated
with high overall operation fidelity.


title: Granular dynamics in auger sampling

abstract:
  From geotechnical applications to space exploration, auger drilling is often
used as a standard tool for soil sample collection, instrument installation,
and others. Focusing on granular flow associated with the rotary drilling
process, we investigate the performance of auger drilling in terms of sampling
efficiency, defined as the mass ratio of the soil sample collected in the
coring tube to its total volume at a given penetration depth, by means of
experiments, numerical simulations, as well as theoretical analysis. The ratio
of rotation to penetration speed is found to play a crucial role in the
sampling process. A continuum model for the coupled granular flow in both
coring and discharging channels is proposed to elucidate the physical mechanism
behind the sampling process. Supported by a comparison to experimental results,
the continuum model provides a practical way to predict the performance of
auger drilling. Further analysis reveals that the drilling process approaches a
steady state with constant granular flow speeds in both channels. In the steady
state, sampling efficiency decreases linearly with the growth of the rotation
to penetration speed ratio, which can be well captured by the analytical
solution of the model. The analytical solution also suggests that the sampling
efficiency is independent of gravity in the steady state, which has profound
implications for extraterrestrial sample collection in future space missions.


title:
Nonlocal cross-interaction systems on graphs: Energy landscape and
  dynamics

abstract:
  We explore the dynamical behavior and energetic properties of a model of two
species that interact nonlocally on finite graphs. The authors recently
introduced the model in the context of nonquadratic Finslerian gradient flows
on generalized graphs featuring nonlinear mobilities. In a continuous and local
setting, this class of systems exhibits a wide variety of patterns, including
mixing of the two species, partial engulfment, or phase separation. This work
showcases how this rich behavior carries over to the graph structure. We
present analytical and numerical evidence thereof.


title:
Renormalization of multi-delta-function point scatterers in two and
  three dimensions, the coincidence-limit problem, and its resolution

abstract:
  In two and three dimensions, the standard treatment of the scattering problem
for a multi-delta-function potential,
$v(\mathbf{r})=\sum_{n=1}^N\mathfrak{z}_n\delta(\mathbf{r}-\mathbf{a}_n)$,
leads to divergent terms. Regularization of these terms and renormalization of
the coupling constants $\mathfrak{z}_n$ give rise to a finite expression for
the scattering amplitude of this potential, but this expression has an
important short-coming; in the limit where the centers $\mathbf{a}_n$ of the
delta functions coincide, it does not reproduce the formula for the scattering
amplitude of a single-delta-function potential, i.e., it seems to have a wrong
coincidence limit. We provide a critical assessment of the standard treatment
of these potentials and offer a resolution of its coincidence-limit problem.
This reveals some previously unnoticed features of this treatment. For example,
it turns out to be incapable of determining the dependence of the scattering
amplitude on the distances between the centers of the delta functions. This is
in sharp contrast to the treatment of this problem offered by a recently
proposed dynamical formulation of stationary scattering. For cases where the
centers of the delta functions lie on a straight line, this formulation avoids
singularities of the standard approach and yields an expression for the
scattering amplitude which has the correct coincidence limit.


title: The population of compact radio sources in M 17

abstract:
  We present a catalog of radio sources of the M 17 region based on deep X band
radio observations centered at 10 GHz obtained with the Jansky Very Large Array
in the A configuration. We detect a total of 194 radio sources, 12 of them
extended and 182 compact. We find that a significant fraction (at least 40% in
our catalog) have suspected gyrosynchrotron emission associated with stellar
coronal emission. By comparing the radio luminosities of our sources with their
X ray counterparts, when available, we find that they are underluminous in X
rays with respect to the G\"udel Benz relation, but a correlation with a
similar slope is obtained provided that only sources with evident non thermal
nature are selected from the sample compiled for the Orion Nebula Cluster (ONC)
and M 17. The comparison of M 17 with the ONC and NGC 6334D-F leads to a
similar luminosity function for the three regions, at least for the more
luminous sources. However, the radio sources in M 17 are three times more
numerous compared to the other regions at a given luminosity and their spatial
distribution differs from that of Orion. Moreover, an arc-shaped structure of
40$"$ in extent is observed in our map, identified previously as an ionizing
front, with the cometary Hyper Compact source UC1 at its focus. Archival 1 mm
ALMA data reveals compact emission coincident with the radio wavelength peak,
possibly associated with a protostellar disk of the massive star exciting UC1.


title: De-biasing facial detection system using VAE

abstract:
  Bias in AI/ML-based systems is a ubiquitous problem and bias in AI/ML systems
may negatively impact society. There are many reasons behind a system being
biased. The bias can be due to the algorithm we are using for our problem or
may be due to the dataset we are using, having some features over-represented
in it. In the face detection system bias due to the dataset is majorly seen.
Sometimes models learn only features that are over-represented in data and
ignore rare features from data which results in being biased toward those
over-represented features. In real life, these biased systems are dangerous to
society. The proposed approach uses generative models which are best suited for
learning underlying features(latent variables) from the dataset and by using
these learned features models try to reduce the threats which are there due to
bias in the system. With the help of an algorithm, the bias present in the
dataset can be removed. And then we train models on two datasets and compare
the results.


title: Extracting Impact Model Narratives from Social Services' Text

abstract:
  Named entity recognition (NER) is an important task in narration extraction.
Narration, as a system of stories, provides insights into how events and
characters in the stories develop over time. This paper proposes an
architecture for NER on a corpus about social purpose organizations. This is
the first NER task specifically targeted at social service entities. We show
how this approach can be used for the sequencing of services and impacted
clients with information extracted from unstructured text. The methodology
outlines steps for extracting ontological representation of entities such as
needs and satisfiers and generating hypotheses to answer queries about impact
models defined by social purpose organizations. We evaluate the model on a
corpus of social service descriptions with empirically calculated score.


title:
Detailed chemical compositions of planet-hosting stars: II. Exploration
  of the interiors of terrestrial-type exoplanets

abstract:
  A major goal in the discovery and characterisation of exoplanets is to
identify terrestrial-type worlds that are similar to (or otherwise distinct
from) our Earth. Recent results have highlighted the importance of applying
devolatilisation -- i.e. depletion of volatiles -- to the chemical composition
of planet-hosting stars to constrain bulk composition and interiors of
terrestrial-type exoplanets. In this work, we apply such an approach to a
selected sample of 13 planet-hosting Sun-like stars, for which high-precision
photospheric abundances have been determined in the first paper of the series.
With the resultant devolatilised stellar composition (i.e. the model planetary
bulk composition) as well as other constraints including mass and radius, we
model the detailed mineralogy and interior structure of hypothetical,
habitable-zone terrestrial planets ("exo-Earths") around these stars. Model
output shows that most of these exo-Earths are expected to have broadly
Earth-like composition and interior structure, consistent with conclusions
derived independently from analysis of polluted white dwarfs. The exceptions
are the Kepler-10 and Kepler-37 exo-Earths, which we predict are strongly
oxidised and thus would develop metallic cores much smaller than Earth.
Investigating our devolatilisation model at its extremes as well as varying
planetary mass and radius (within the terrestrial regime) reveals potential
diversities in the interiors of terrestrial planets. By considering (i)
high-precision stellar abundances, (ii) devolatilisation, and (iii) planetary
mass and radius holistically, this work represents essential steps to explore
the detailed mineralogy and interior structure of terrestrial-type exoplanets,
which in turn are fundamental for our understanding of planetary dynamics and
long-term evolution.


title: Evidence for Parameteric Decay Instability in the Lower Solar Atmosphere

abstract:
  We find evidence for the first observation of the parametric decay
instability (PDI) in the lower solar atmosphere. Specifically, we find that the
power spectrum of density fluctuations near the solar transition region
resembles the power spectrum of the velocity fluctuations, but with the
frequency axis scaled up by about a factor of two. These results are from an
analysis of the Si IV lines observed by the Interface Region Imaging
Spectrometer (IRIS) in the transition region of a polar coronal hole. We also
find that the density fluctuations have radial velocity of about 75 km/s and
that the velocity fluctuations are much faster with an estimated speed of 250
km/s, as is expected for sound waves and Alfv\'en waves, respectively, in the
transition region. Theoretical calculations show that this frequency
relationship is consistent with those expected from PDI for the plasma
conditions of the observed region. These measurements suggest an interaction
between sound waves and Alfv\'en waves in the transition region that is
evidence for the parametric decay instability.


title: Understanding and Preventing Capacity Loss in Reinforcement Learning

abstract:
  The reinforcement learning (RL) problem is rife with sources of
non-stationarity, making it a notoriously difficult problem domain for the
application of neural networks. We identify a mechanism by which non-stationary
prediction targets can prevent learning progress in deep RL agents:
\textit{capacity loss}, whereby networks trained on a sequence of target values
lose their ability to quickly update their predictions over time. We
demonstrate that capacity loss occurs in a range of RL agents and environments,
and is particularly damaging to performance in sparse-reward tasks. We then
present a simple regularizer, Initial Feature Regularization (InFeR), that
mitigates this phenomenon by regressing a subspace of features towards its
value at initialization, leading to significant performance improvements in
sparse-reward environments such as Montezuma's Revenge. We conclude that
preventing capacity loss is crucial to enable agents to maximally benefit from
the learning signals they obtain throughout the entire training trajectory.


title:
Optimal mass normalizability for Gibbs measure associated with NLS on
  the 2D disc

abstract:
  We prove the normalizability of Gibbs measure associated with radial focusing
nonlinear Schr\"{o}dinger equation (NLS) on the 2-dimensional disc
$\mathbb{D}$, at critical mass threshold. The result completes the study of
optimal mass normalizability on $\mathbb{D}$ by Oh-Sosoe-Tolomeo (2021).


title: Fast Circular Pattern Matching

abstract:
  The Exact Circular Pattern Matching (ECPM) problem consists of reporting
every occurrence of a rotation of a pattern $P$ in a text $T$. In many
real-world applications, specifically in computational biology, circular
rotations are of interest because of their prominence in virus DNA. Thus, given
no restrictions on pre-processing time, how quickly all such circular rotation
occurrences is of interest to many areas of study. We highlight, to the best of
our knowledge, a novel approach to the ECPM problem and present four data
structures that accompany this approach, each with their own time-space
trade-offs, in addition to experimental results to determine the most
computationally feasible data structure.


title: Interatomic potentials: Achievements and challenges

abstract:
  Interactions between atoms can be formally expanded into two-body,
three-body, and higher-order contributions. Unfortunately, this expansion is
slowly converging for most systems of practical interest making it inexpedient
for molecular simulations. This is why effective descriptions are needed for
the accurate simulation of many-atom systems. This article reviews potentials
designed towards this end with a focus on empirical interatomic potentials not
necessitating a-priori knowledge of what pairs of atoms are bonded to each
other, i.e., on potentials meant to describe defects and chemical reactions
from bond breaking and formation to redox reactions. The classes of discussed
potentials include popular two-body potentials, embedded-atom models for
metals, bond-order potentials for covalently bonded systems, polarizable
potentials including charge-transfer approaches for ionic systems and
quantum-Drude oscillator models mimicking higher-order and many-body
dispersion. Particular emphasis is laid on the question what constraints on
materials properties ensue from the functional form of a potential, e.g., in
what way Cauchy relations for elastic tensor elements can be violated and what
this entails for the ratio of defect and cohesive energies. The review is meant
to be pedagogical rather than encyclopedic. This is why we highlight potentials
with functional forms that are sufficiently simple to remain amenable to
analytical treatments, whereby qualitative questions can be answered, such as,
why the ratio of boiling to melting temperature tends to be large for
potentials describing metals but small for pair potentials. However, we abstain
for the most part from discussing specific parametrizations. Our main aim is to
provide a stimulus for how existing approaches can be advanced or meaningfully
combined to extent the scope of simulations based on empirical potentials.


title: Cross-view Brain Decoding

abstract:
  How the brain captures the meaning of linguistic stimuli across multiple
views is still a critical open question in neuroscience. Consider three
different views of the concept apartment: (1) picture (WP) presented with the
target word label, (2) sentence (S) using the target word, and (3) word cloud
(WC) containing the target word along with other semantically related words.
Unlike previous efforts, which focus only on single view analysis, in this
paper, we study the effectiveness of brain decoding in a zero-shot cross-view
learning setup. Further, we propose brain decoding in the novel context of
cross-view-translation tasks like image captioning (IC), image tagging (IT),
keyword extraction (KE), and sentence formation (SF). Using extensive
experiments, we demonstrate that cross-view zero-shot brain decoding is
practical leading to ~0.68 average pairwise accuracy across view pairs. Also,
the decoded representations are sufficiently detailed to enable high accuracy
for cross-view-translation tasks with following pairwise accuracy: IC (78.0),
IT (83.0), KE (83.7) and SF (74.5). Analysis of the contribution of different
brain networks reveals exciting cognitive insights: (1) A high percentage of
visual voxels are involved in image captioning and image tagging tasks, and a
high percentage of language voxels are involved in the sentence formation and
keyword extraction tasks. (2) Zero-shot accuracy of the model trained on S view
and tested on WC view is better than same-view accuracy of the model trained
and tested on WC view.

categories: q-bio.NC cs.AI cs.CL cs.CV cs.LG eess.IV


title: On the Ethical Considerations of Text Simplification

abstract:
  This paper outlines the ethical implications of text simplification within
the framework of assistive systems. We argue that a distinction should be made
between the technologies that perform text simplification and the realisation
of these in assistive technologies. When using the latter as a motivation for
research, it is important that the subsequent ethical implications be carefully
considered. We provide guidelines for the framing of text simplification
independently of assistive systems, as well as suggesting directions for future
research and discussion based on the concerns raised.


title:
Summit of the N=40 Island of Inversion: precision mass measurements and
  ab initio calculations of neutron-rich chromium isotopes

abstract:
  Mass measurements continue to provide invaluable information for elucidating
nuclear structure and scenarios of astrophysical interest. The transition
region between the $Z = 20$ and $28$ proton shell closures is particularly
interesting due to the onset and evolution of nuclear deformation as nuclei
become more neutron rich. This provides a critical testing ground for emerging
ab-initio nuclear structure models. Here, we present high-precision mass
measurements of neutron-rich chromium isotopes using the sensitive
electrostatic Multiple-Reflection Time-Of-Flight Mass Spectrometer (MR-TOF-MS)
at TRIUMF's Ion Trap for Atomic and Nuclear Science (TITAN) facility. Our
high-precision mass measurements of $^{59, 61-63}$Cr confirm previous results,
and the improved precision in measurements of $^{64-65}$Cr refine the mass
surface beyond N=40. With the ab initio in-medium similarity renormalization
group, we examine the trends in collectivity in chromium isotopes and give a
complete picture of the N=40 island of inversion from calcium to nickel.


title:
Evidence that cuprate superconductors form an array of nanoscopic
  Josephson junctions

abstract:
  Recent measurements of charge instabilities in overdoped compounds rekindled
the proposal that cuprates become superconductors by long-range order through
Josephson coupling between nanoscopic charge domains. We use the theory of
phase-ordering dynamics to show that incommensurate charge density waves (CDWs)
are formed in the CuO planes by a series of free-energy wells separated by
steep barriers. Charge oscillations in these domains give rise to a net
hole-hole attraction proportional to the height of these barriers.
Concomitantly, the self-consistent calculations yield localized superconducting
amplitudes in the CDWdomains characterizing a granular superconductor. We show
that a transition by long-range phase order promoted by Josephson coupling
elucidates many well-known features of cuprates like the high magnetic
penetration depth anisotropy and the origin of the pseudogap, among others.
Furthermore, the average Josephson energy reproduces closely the planar
superfluid density temperature dependence of La-based films and the
superconducting giant proximity effects of cuprates, a 20-year-old open
problem.


title:
Predictive Accuracy of a Hybrid Generalized Long Memory Model for Short
  Term Electricity Price Forecasting

abstract:
  Accurate electricity price forecasting is the main management goal for market
participants since it represents the fundamental basis to maximize the profits
for market players. However, electricity is a non-storable commodity and the
electricity prices are affected by some social and natural factors that make
the price forecasting a challenging task. This study investigates the
predictive performance of a new hybrid model based on the Generalized long
memory autoregressive model (k-factor GARMA), the Gegenbauer Generalized
Autoregressive Conditional Heteroscedasticity(G-GARCH) process, Wavelet
decomposition, and Local Linear Wavelet Neural Network (LLWNN) optimized using
two different learning algorithms; the Backpropagation algorithm (BP) and the
Particle Swarm optimization algorithm (PSO). The performance of the proposed
model is evaluated using data from Nord Pool Electricity markets. Moreover, it
is compared with some other parametric and non-parametric models in order to
prove its robustness. The empirical results prove that the proposed method
performs well than other competing techniques.


title:
Majorana neutrino mass constraints in the landscape of nuclear matrix
  elements

abstract:
  We discuss up-to-date constraints on the Majorana neutrino mass
$m_{\beta\beta}$ from neutrinoless double beta decay ($0\nu\beta\beta$)
searches in experiments using different isotopes: KamLAND-Zen and EXO
($^{136}$Xe), GERDA and MAJORANA ($^{76}$Ge) and CUORE ($^{130}$Te). Best fits
and upper bounds on $m_{\beta\beta}$ are explored in the general landscape of
nuclear matrix elements (NME), as well as for specific NME values obtained in
representative nuclear models. By approximating the likelihood of
$0\nu\beta\beta$ signals through quadratic forms, the analysis of separate and
combined isotope data becomes exceedingly simple, and allows to clarify various
aspects of multi-isotope data combinations. In particular, we analyze the
relative impact of different data in setting upper bounds on $m_{\beta\beta}$,
as well as the conditions leading to nonzero $m_{\beta\beta}$ at best fit, for
variable values of the NMEs. Detailed results on $m_{\beta\beta}$ from various
combinations of data are reported in graphical and numerical form. Implications
for future $0\nu\beta\beta$ data analyses and NME calculations are briefly
discussed.


title:
Role of cohesion in the formation of kink wave fronts in vibrofluidized
  granular materials

abstract:
  The formation of kink wave fronts (KWFs) in a quasi-two-dimensional granular
system is investigated numerically with a focus on the role of cohesive
interactions between individual particles. The cohesive particle-particle
interaction is achieved through tuning the velocity-dependent coefficient of
restitution, based on an analytical model introduced recently. A comparison
with experimental results indicates that the threshold for the emergence of
traveling KWFs matches the regime in which the center of mass height of the
granular layers fluctuates with a period that triples the vibration period.
Further comparisons between dry and wet granular dynamics reveal that KWFs are
more pronounced in wet granular layers because of enhanced collective motion
induced by cohesion.


title: Informative Path Planning in Random Fields via Mixed Integer Programming

abstract:
  We present a new mixed integer formulation for the discrete informative path
planning problem in random fields. The objective is to compute a budget
constrained path while collecting measurements whose linear estimate results in
minimum error over a finite set of prediction locations. The problem is known
to be NP-hard. However, we strive to compute optimal solutions by leveraging
advances in mixed integer optimization. Our approach is based on expanding the
search space so we optimize not only over the collected measurement subset, but
also over the class of all linear estimators. This allows us to formulate a
mixed integer quadratic program that is convex in the continuous variables. The
formulations are general and are not restricted to any covariance structure of
the field. In simulations, we demonstrate the effectiveness of our approach
over previous branch and bound algorithms.


title: Graphene on Silicon Hybrid Field-Effect Transistors

abstract:
  The combination of graphene with silicon in hybrid devices has attracted
attention extensively over the last decade. Most of such devices were proposed
for photonics and radiofrequency applications. In this work, we present a
unique technology of graphene-on-silicon heterostructures and their properties
as solution-gated transistors. The graphene-on-Silicon field-effect transistors
(GoSFETs) were fabricated exploiting various conformations of drain-source
regions doping and channel material dimensions. The fabricated devices were
electrically characterized demonstrating hybrid behavior with features specific
to both graphene and silicon. Although GoSFET's transconductance and carrier's
mobility were found to be lower than in conventional silicon and graphene
field-effect transistors (SiFETs and GFETs), it was demonstrated that the
combination of both materials within the hybrid channel contribute uniquely to
the charge carrier transport. A comprehensive physics-based compact modeling
was specifically developed, showing excellent agreement with the experimental
data. The model is employed to rationalize the observed hybrid behavior as the
theoretical results from the electrostatics and the carrier transport under a
drift-diffusion approach show that graphene acts as a shield for the silicon
channel, giving rise to a non-uniform potential distribution along it,
especially at the subthreshold region. This graphene screening effect is shown
to strongly affect the device subthreshold swing when compared against a
conventional SiFET due to a non-negligible diffusion current in this operation
regime.


title: Fetal Brain Tissue Annotation and Segmentation Challenge Results

abstract:
  In-utero fetal MRI is emerging as an important tool in the diagnosis and
analysis of the developing human brain. Automatic segmentation of the
developing fetal brain is a vital step in the quantitative analysis of prenatal
neurodevelopment both in the research and clinical context. However, manual
segmentation of cerebral structures is time-consuming and prone to error and
inter-observer variability. Therefore, we organized the Fetal Tissue Annotation
(FeTA) Challenge in 2021 in order to encourage the development of automatic
segmentation algorithms on an international level. The challenge utilized FeTA
Dataset, an open dataset of fetal brain MRI reconstructions segmented into
seven different tissues (external cerebrospinal fluid, grey matter, white
matter, ventricles, cerebellum, brainstem, deep grey matter). 20 international
teams participated in this challenge, submitting a total of 21 algorithms for
evaluation. In this paper, we provide a detailed analysis of the results from
both a technical and clinical perspective. All participants relied on deep
learning methods, mainly U-Nets, with some variability present in the network
architecture, optimization, and image pre- and post-processing. The majority of
teams used existing medical imaging deep learning frameworks. The main
differences between the submissions were the fine tuning done during training,
and the specific pre- and post-processing steps performed. The challenge
results showed that almost all submissions performed similarly. Four of the top
five teams used ensemble learning methods. However, one team's algorithm
performed significantly superior to the other submissions, and consisted of an
asymmetrical U-Net network architecture. This paper provides a first of its
kind benchmark for future automatic multi-tissue segmentation algorithms for
the developing human brain in utero.


title:
Development of linear functional arithmetic and its applications to the
  solution of interval linear systems with constraints

abstract:
  The work is devoted to the construction of a new interval arithmetic which
would combine algorithmic efficiency and high quality estimation of the ranges
of expressions.


title:
Fast and Robust Femur Segmentation from Computed Tomography Images for
  Patient-Specific Hip Fracture Risk Screening

abstract:
  Osteoporosis is a common bone disease that increases the risk of bone
fracture. Hip-fracture risk screening methods based on finite element analysis
depend on segmented computed tomography (CT) images; however, current femur
segmentation methods require manual delineations of large data sets. Here we
propose a deep neural network for fully automated, accurate, and fast
segmentation of the proximal femur from CT. Evaluation on a set of 1147
proximal femurs with ground truth segmentations demonstrates that our method is
apt for hip-fracture risk screening, bringing us one step closer to a
clinically viable option for screening at-risk patients for hip-fracture
susceptibility.


title: The valuation pairing on an upper cluster algebra

abstract:
  It is known that many (upper) cluster algebras are not unique factorization
domains. We exhibit the local factorization properties with respect to any
given seed $t$: any non-zero element in a full rank upper cluster algebra can
be uniquely written as the product of a cluster monomial in $t$ and another
element not divisible by the cluster variables in $t$. Our approach is based on
introducing the valuation pairing on an upper cluster algebra: it counts the
maximal multiplicity of a cluster variable among the factorizations of any
given element.
  We apply the valuation pairing to obtain many results concerning
factoriality, $d$-vectors, $F$-polynomials and the combinatorics of cluster
Poisson variables. In particular, we obtain that full rank and primitive upper
cluster algebras are factorial; an explanation of $d$-vectors using valuation
pairing; a cluster monomial in non-initial cluster variables is determined by
its $F$-polynomial; the $F$-polynomials of non-initial cluster variables are
irreducible; and the cluster Poisson variables parametrize the exchange pairs
of the corresponding upper cluster algebra.


title: Energy-Efficient Tree-Based EEG Artifact Detection

abstract:
  In the context of epilepsy monitoring, EEG artifacts are often mistaken for
seizures due to their morphological similarity in both amplitude and frequency,
making seizure detection systems susceptible to higher false alarm rates. In
this work we present the implementation of an artifact detection algorithm
based on a minimal number of EEG channels on a parallel ultra-low-power (PULP)
embedded platform. The analyses are based on the TUH EEG Artifact Corpus
dataset and focus on the temporal electrodes. First, we extract optimal feature
models in the frequency domain using an automated machine learning framework,
achieving a 93.95% accuracy, with a 0.838 F1 score for a 4 temporal EEG channel
setup. The achieved accuracy levels surpass state-of-the-art by nearly 20%.
Then, these algorithms are parallelized and optimized for a PULP platform,
achieving a 5.21 times improvement of energy-efficient compared to
state-of-the-art low-power implementations of artifact detection frameworks.
Combining this model with a low-power seizure detection algorithm would allow
for 300h of continuous monitoring on a 300 mAh battery in a wearable form
factor and power budget. These results pave the way for implementing
affordable, wearable, long-term epilepsy monitoring solutions with low
false-positive rates and high sensitivity, meeting both patients' and
caregivers' requirements.


title: Restructuring TCAD System: Teaching Traditional TCAD New Tricks

abstract:
  Traditional TCAD simulation has succeeded in predicting and optimizing the
device performance; however, it still faces a massive challenge - a high
computational cost. There have been many attempts to replace TCAD with deep
learning, but it has not yet been completely replaced. This paper presents a
novel algorithm restructuring the traditional TCAD system. The proposed
algorithm predicts three-dimensional (3-D) TCAD simulation in real-time while
capturing a variance, enables deep learning and TCAD to complement each other,
and fully resolves convergence errors.


title:
A Survey and Perspective on Artificial Intelligence for Security-Aware
  Electronic Design Automation

abstract:
  Artificial intelligence (AI) and machine learning (ML) techniques have been
increasingly used in several fields to improve performance and the level of
automation. In recent years, this use has exponentially increased due to the
advancement of high-performance computing and the ever increasing size of data.
One of such fields is that of hardware design; specifically the design of
digital and analog integrated circuits~(ICs), where AI/ ML techniques have been
extensively used to address ever-increasing design complexity, aggressive
time-to-market, and the growing number of ubiquitous interconnected devices
(IoT). However, the security concerns and issues related to IC design have been
highly overlooked. In this paper, we summarize the state-of-the-art in AL/ML
for circuit design/optimization, security and engineering challenges, research
in security-aware CAD/EDA, and future research directions and needs for using
AI/ML for security-aware circuit design.


title:
The Impact of Inclination-dependent Attenuation on Ultraviolet Star
  Formation Rate Tracers

abstract:
  We examine and quantify how hybrid (e.g., UV+IR) star formation rate (SFR)
estimators and the $A_{\rm FUV}$-$\beta$ relation (i.e., the Meurer et al. 1999
relation) depend on inclination for disk-dominated galaxies using spectral
energy distribution modeling that utilizes the inclination-dependent
attenuation curves described in Doore et al. (2021). We perform this analysis
on a sample of 133 disk-dominated galaxies from the CANDELS fields and 18 disk
galaxies from the SINGS and KINGFISH samples. We find that both the hybrid SFR
estimators and the $A_{\rm FUV}$-$\beta$ relation present clear dependencies on
inclination. To quantify this dependence in hybrid SFR estimators, we derive an
inclination and FUV-NIR color dependent parametric relation for converting
observed UV and IR luminosities into SFRs. For the $A_{\rm FUV}$-$\beta$
relation, we introduce an inclination-dependent component that accounts for the
majority of the inclination dependence with the scatter of the relation
increasing with inclination. We then compare both of these
inclination-dependent relations to similar inclination-independent relations
found in the literature. From this comparison, we find that the UV+IR
correction factor and $A_{\rm FUV}$ for our our hybrid and $A_{\rm
FUV}$-$\beta$ relations, respectively, result in a reduction in the residual
scatter of our sample by approximately a factor of two. Therefore, we
demonstrate that inclination must be considered in hybrid SFR estimators and
the $A_{\rm FUV}$-$\beta$ relation to produce more accurate SFR estimates in
disk-dominated galaxies.


title: Exact 3D scattering solutions for spherical symmetric scatterers

abstract:
  In this paper, exact solutions to the problem of acoustic scattering by
elastic spherical symmetric scatterers are developed. The scatterer may consist
of an arbitrary number of fluid and solid layers, and scattering with single
Neumann conditions (replacing Neumann-to-Neumann conditions) is added. The
solution is obtained by separation of variables, resulting in an infinite
series which must be truncated for numerical evaluation. The implemented
numerical solution is exact in the sense that numerical error is solely due to
round-off errors, which will be shown using the symbolic toolbox in MATLAB. A
system of benchmark problems is proposed for future reference. Numerical
examples are presented, including comparisons with reference solutions,
far-field patterns and near-field plots of the benchmark problems, and
time-dependent solutions obtained by Fourier transformation.


title: Theta groups and projective models of hyperk\"ahler varieties

abstract:
  We define the theta group associated to a simple coherent sheaf $\cal F$ on a
hyperk\"ahler manifold $X$ of Kummer type or OG6 type, provided $g^{*}({\cal
F})$ is isomorphic to $\cal F$ for every automorphism $g$ of $X$ acting
trivially on $H^2(X)$. Note that this condition is satisfied if $\cal F$ is
invertible, if $\cal F$ is one of the rank $4$ stable vector bundles on general
polarized HK fourfolds with certain discrete invariants constructed in
arXiv:2203.03987, or if $\cal F$ is the tangent bundle. We compute the
commutator pairings of theta groups of line bundles and the rank $4$ modular
vector bundles of arXiv:2203.03987 (the commutator pairing of the tangent
bundle is trivial). We have been motivated by the quest for an explicit
description of locally complete families of polarized varieties of Kummer (or
OG6) type.


title:
Improved Worst-Group Robustness via Classifier Retraining on Independent
  Splits

abstract:
  High-capacity deep neural networks (DNNs) trained with Empirical Risk
Minimization (ERM) often suffer from poor worst-group accuracy despite good
on-average performance, where worst-group accuracy measures a model's
robustness towards certain subpopulations of the input space. Spurious
correlations and memorization behaviors of ERM trained DNNs are typically
attributed to this degradation in performance. We develop a method, called
CRIS, that address these issues by performing robust classifier retraining on
independent splits of the dataset. This results in a simple method that
improves upon state-of-the-art methods, such as Group DRO, on standard datasets
while relying on much fewer group labels and little additional hyperparameter
tuning.


title: An orthogonal approach to algebraic weak factorisation systems

abstract:
  We describe an equivalent formulation of algebraic weak factorisation
systems, not involving monads and comonads, but involving double categories of
morphisms equipped with a lifting operation satisfying lifting and
factorisation axioms.


title:
Implications of CDF $W$-mass and $(g-2)_\mu$ on $U(1)_{L_\mu-L_\tau}$
  model

abstract:
  We study the implications of the recent anomalies in the $W$-boson mass and
the anomalous magnetic moment of the muon on $U(1)_{L_\mu-L_\tau}$ model. We
show that the introduction of vector-like fermions which mix with muon can
solve both anomalies.


title:
Geometric picture for SLOCC classification of pure permutation symmetric
  three-qubit states

abstract:
  We show that the pure entangled three-qubit symmetric states which are
inequivalent under stochastic local operations and classcial communication
(SLOCC) exhibit distinct geometric representation in terms of a spheroid
inscribed within the Bloch sphere. We provide detailed analysis of the SLOCC
canonical forms of the reduced two-qubit states extracted from entangled
three-qubit pure symmetric states. Based on the Lorentz canonical forms of
these states we arrive at two different geometrical representations: (i) a
prolate spheroid centered at the origin of the Bloch sphere -- with longest
semiaxis along the z-direction (symmetry axis of the spheroid) equal to 1 -- in
the case of pure permutation symmetric three-qubit states constructed from 3
distinct spinors and (ii) a spheroid centered at (0,0,1/2) inside the Bloch
sphere, with fixed semiaxes lengths (1/sqrt{2}, 1/sqrt{2}, 1/2) when the
three-qubit pure state is constructed via symmetrization of 2 distinct spinors.


title:
Heat transfer problem for the Boltzmann equation in a channel with
  diffusive boundary condition

abstract:
  In this paper, we study the 1D steady Boltzmann flow in a channel. The walls
of the channel are assumed to have vanishing velocity and given temperatures
$\theta_0$ and $\theta_1$. This problem was studied by Esposito et al [13,14]
where they showed that the solution tends to a local Maxwellian with parameters
satisfying the compressible Navier-Stokes equation with no-slip boundary
condition. However, a lot of numerical experiments reveal that the fluid layer
does not entirely stick to the boundary. In the regime where the Knudsen number
is reasonably small, the slip phenomenon is significant near the boundary.
Thus, we revisit this problem by taking into account the slip boundary
conditions. Following the lines of [9], we will first give a formal asymptotic
analysis to see that the flow governed by the Boltzmann equation is accurately
approximated by a superposition of a steady CNS equation with a temperature
jump condition and two Knudsen layers located at end points. Then we will
establish a uniform $L^\infty$ estimate on the remainder and derive the slip
boundary condition for compressible Navier-Stokes equations rigorously.


title: Exploring Descriptions of Movement Through Geovisual Analytics

abstract:
  Sensemaking using automatically extracted information from text is a
challenging problem. In this paper, we address a specific type of information
extraction, namely extracting information related to descriptions of movement.
Aggregating and understanding information related to descriptions of movement
and lack of movement specified in text can lead to an improved understanding
and sensemaking of movement phenomena of various types, e.g., migration of
people and animals, impediments to travel due to COVID-19, etc. We present
GeoMovement, a system that is based on combining machine learning and
rule-based extraction of movement-related information with state-of-the-art
visualization techniques. Along with the depiction of movement, our tool can
extract and present a lack of movement. Very little prior work exists on
automatically extracting descriptions of movement, especially negation and
movement. Apart from addressing these, GeoMovement also provides a novel
integrated framework for combining these extraction modules with visualization.
We include two systematic case studies of GeoMovement that show how humans can
derive meaningful geographic movement information. GeoMovement can complement
precise movement data, e.g., obtained using sensors, or be used by itself when
precise data is unavailable.


title:
Distantly Supervised Named Entity Recognition via Confidence-Based
  Multi-Class Positive and Unlabeled Learning

abstract:
  In this paper, we study the named entity recognition (NER) problem under
distant supervision. Due to the incompleteness of the external dictionaries
and/or knowledge bases, such distantly annotated training data usually suffer
from a high false negative rate. To this end, we formulate the Distantly
Supervised NER (DS-NER) problem via Multi-class Positive and Unlabeled (MPU)
learning and propose a theoretically and practically novel CONFidence-based MPU
(Conf-MPU) approach. To handle the incomplete annotations, Conf-MPU consists of
two steps. First, a confidence score is estimated for each token of being an
entity token. Then, the proposed Conf-MPU risk estimation is applied to train a
multi-class classifier for the NER task. Thorough experiments on two benchmark
datasets labeled by various external knowledge demonstrate the superiority of
the proposed Conf-MPU over existing DS-NER methods.


title: Model Reduction via Dynamic Mode Decomposition

abstract:
  This work proposes a new framework of model reduction for parametric complex
systems. The framework employs a popular model reduction technique dynamic mode
decomposition (DMD), which is capable of combining data-driven learning and
physics ingredients based on the Koopman operator theory. In the offline step
of the proposed framework, DMD constructs a low-rank linear surrogate model for
the high dimensional quantities of interest (QoIs) derived from the (nonlinear)
complex high fidelity models (HFMs) of unknown forms. Then in the online step,
the resulting local reduced order bases (ROBs) and parametric reduced order
models (PROMs) at the training parameter sample points are interpolated to
construct a new PROM with the corresponding ROB for a new set of target/test
parameter values. The interpolations need to be done on the appropriate
manifolds within consistent sets of generalized coordinates. The proposed
framework is illustrated by numerical examples for both linear and nonlinear
problems. In particular, its advantages in computational costs and accuracy are
demonstrated by the comparisons with projection-based proper orthogonal
decomposition (POD)-PROM and Kriging.


title: A Survey on Bias and Fairness in Natural Language Processing

abstract:
  As NLP models become more integrated with the everyday lives of people, it
becomes important to examine the social effect that the usage of these systems
has. While these models understand language and have increased accuracy on
difficult downstream tasks, there is evidence that these models amplify gender,
racial and cultural stereotypes and lead to a vicious cycle in many settings.
In this survey, we analyze the origins of biases, the definitions of fairness,
and how different subfields of NLP mitigate bias. We finally discuss how future
studies can work towards eradicating pernicious biases from NLP algorithms.


title:
Electrical two-qubit gates within a pair of clock-qubit magnetic
  molecules

abstract:
  Enhanced coherence in HoW$_{10}$ molecular spin qubits has been demonstrated
by use of Clock Transitions (CTs). More recently it was shown that, while
operating at the CTs, it was possible to use an electrical field to selectively
address HoW$_{10}$ molecules pointing in a given direction, within a crystal
that contains two kinds of identical but inversion-related molecules. Herein we
theoretically explore the possibility of employing the electric field to effect
entangling two-qubit quantum gates among two neighbouring CT-protected
HoW$_{10}$ qubits within a diluted crystal. We estimate the thermal evolution
of $T_1$, $T_2$, find that CTs are also optimal operating points from the point
of view of phonons, and lay out how to combine a sequence of microwave and
electric field pulses to achieve coherent control within a 2-qubit operating
space that is protected both from spin-bath and from phonon-bath decoherence.
Finally, we found a highly protected 1-qubit subspace resulting from the
interaction between two clock molecules.


title:
COOL, a Context Outlooker, and its Application to Question Answering and
  other Natural Language Processing Tasks

abstract:
  Vision outlookers improve the performance of vision transformers, which
implement a self-attention mechanism by adding outlook attention, a form of
local attention.
  In natural language processing, as has been the case in computer vision and
other domains, transformer-based models constitute the state-of-the-art for
most processing tasks. In this domain, too, many authors have argued and
demonstrated the importance of local context.
  We present and evaluate an outlook attention mechanism, COOL, for natural
language processing. COOL adds, on top of the self-attention layers of a
transformer-based model, outlook attention layers that encode local syntactic
context considering word proximity and consider more pair-wise constraints than
dynamic convolution operations used by existing approaches.
  A comparative empirical performance evaluation of an implementation of COOL
with different transformer-based approaches confirms the opportunity of
improvement over a baseline using the neural language models alone for various
natural language processing tasks, including question answering. The proposed
approach is competitive with state-of-the-art methods.


title: Predicting Clinical Intent from Free Text Electronic Health Records

abstract:
  After a patient consultation, a clinician determines the steps in the
management of the patient. A clinician may for example request to see the
patient again or refer them to a specialist. Whilst most clinicians will record
their intent as "next steps" in the patient's clinical notes, in some cases the
clinician may forget to indicate their intent as an order or request, e.g.
failure to place the follow-up order. This consequently results in patients
becoming lost-to-follow up and may in some cases lead to adverse consequences.
In this paper we train a machine learning model to detect a clinician's intent
to follow up with a patient from the patient's clinical notes. Annotators
systematically identified 22 possible types of clinical intent and annotated
3000 Bariatric clinical notes. The annotation process revealed a class
imbalance in the labeled data and we found that there was only sufficient
labeled data to train 11 out of the 22 intents. We used the data to train a
BERT based multilabel classification model and reported the following average
accuracy metrics for all intents: macro-precision: 0.91, macro-recall: 0.90,
macro-f1: 0.90.


title:
Exploring Continuous Integrate-and-Fire for Adaptive Simultaneous Speech
  Translation

abstract:
  Simultaneous speech translation (SimulST) is a challenging task aiming to
translate streaming speech before the complete input is observed. A SimulST
system generally includes two components: the pre-decision that aggregates the
speech information and the policy that decides to read or write. While recent
works had proposed various strategies to improve the pre-decision, they mainly
adopt the fixed wait-k policy, leaving the adaptive policies rarely explored.
This paper proposes to model the adaptive policy by adapting the Continuous
Integrate-and-Fire (CIF). Compared with monotonic multihead attention (MMA),
our method has the advantage of simpler computation, superior quality at low
latency, and better generalization to long utterances. We conduct experiments
on the MuST-C V2 dataset and show the effectiveness of our approach.


title: Risk-Averse Receding Horizon Motion Planning

abstract:
  This paper studies the problem of risk-averse receding horizon motion
planning for agents with uncertain dynamics, in the presence of stochastic,
dynamic obstacles. We propose a model predictive control (MPC) scheme that
formulates the obstacle avoidance constraint using coherent risk measures. To
handle disturbances, or process noise, in the state dynamics, the state
constraints are tightened in a risk-aware manner to provide a disturbance
feedback policy. We also propose a waypoint following algorithm that uses the
proposed MPC scheme for discrete distributions and prove its risk-sensitive
recursive feasibility while guaranteeing finite-time task completion. We
further investigate some commonly used coherent risk metrics, namely,
conditional value-at-risk (CVaR), entropic value-at-risk (EVaR), and g-entropic
risk measures, and propose a tractable incorporation within MPC. We illustrate
our framework via simulation studies.


title:
Perceiving the World: Question-guided Reinforcement Learning for
  Text-based Games

abstract:
  Text-based games provide an interactive way to study natural language
processing. While deep reinforcement learning has shown effectiveness in
developing the game playing agent, the low sample efficiency and the large
action space remain to be the two major challenges that hinder the DRL from
being applied in the real world. In this paper, we address the challenges by
introducing world-perceiving modules, which automatically decompose tasks and
prune actions by answering questions about the environment. We then propose a
two-phase training framework to decouple language learning from reinforcement
learning, which further improves the sample efficiency. The experimental
results show that the proposed method significantly improves the performance
and sample efficiency. Besides, it shows robustness against compound error and
limited pre-training data.


title: Build a Robust QA System with Transformer-based Mixture of Experts

abstract:
  In this paper, we aim to build a robust question answering system that can
adapt to out-of-domain datasets. A single network may overfit to the
superficial correlation in the training distribution, but with a meaningful
number of expert sub-networks, a gating network that selects a sparse
combination of experts for each input, and careful balance on the importance of
expert sub-networks, the Mixture-of-Experts (MoE) model allows us to train a
multi-task learner that can be generalized to out-of-domain datasets. We also
explore the possibility of bringing the MoE layers up to the middle of the
DistilBERT and replacing the dense feed-forward network with a
sparsely-activated switch FFN layers, similar to the Switch Transformer
architecture, which simplifies the MoE routing algorithm with reduced
communication and computational costs. In addition to model architectures, we
explore techniques of data augmentation including Easy Data Augmentation (EDA)
and back translation, to create more meaningful variance among the small
out-of-domain training data, therefore boosting the performance and robustness
of our models. In this paper, we show that our combination of best architecture
and data augmentation techniques achieves a 53.477 F1 score in the
out-of-domain evaluation, which is a 9.52% performance gain over the baseline.
On the final test set, we reported a higher 59.506 F1 and 41.651 EM. We
successfully demonstrate the effectiveness of Mixture-of-Expert architecture in
a Robust QA task.


title: Radiology Text Analysis System (RadText): Architecture and Evaluation

abstract:
  Analyzing radiology reports is a time-consuming and error-prone task, which
raises the need for an efficient automated radiology report analysis system to
alleviate the workloads of radiologists and encourage precise diagnosis. In
this work, we present RadText, an open-source radiology text analysis system
developed by Python. RadText offers an easy-to-use text analysis pipeline,
including de-identification, section segmentation, sentence split and word
tokenization, named entity recognition, parsing, and negation detection.
RadText features a flexible modular design, provides a hybrid text processing
schema, and supports raw text processing and local processing, which enables
better usability and improved data privacy. RadText adopts BioC as the unified
interface, and also standardizes the input / output into a structured
representation compatible with Observational Medical Outcomes Partnership
(OMOP) Common Data Model (CDM). This allows for a more systematic approach to
observational research across multiple, disparate data sources. We evaluated
RadText on the MIMIC-CXR dataset, with five new disease labels we annotated for
this work. RadText demonstrates highly accurate classification performances,
with an average precision of, a recall of 0.94, and an F-1 score of 0.92. We
have made our code, documentation, examples, and the test set available at
https://github.com/bionlplab/radtext .


title: Hierarchical BERT for Medical Document Understanding

abstract:
  Medical document understanding has gained much attention recently. One
representative task is the International Classification of Disease (ICD)
diagnosis code assignment. Existing work adopts either RNN or CNN as the
backbone network because the vanilla BERT cannot handle well long documents
(>2000 to kens). One issue shared across all these approaches is that they are
over specific to the ICD code assignment task, losing generality to give the
whole document-level and sentence-level embedding. As a result, it is not
straight-forward to direct them to other downstream NLU tasks. Motivated by
these observations, we propose Medical Document BERT (MDBERT) for long medical
document understanding tasks. MDBERT is not only effective in learning
representations at different levels of semantics but efficient in encoding long
documents by leveraging a bottom-up hierarchical architecture. Compared to
vanilla BERT solutions: 1, MDBERT boosts the performance up to relatively 20%
on the MIMIC-III dataset, making it comparable to current SOTA solutions; 2, it
cuts the computational complexity on self-attention modules to less than 1/100.
Other than the ICD code assignment, we conduct a variety of other NLU tasks on
a large commercial dataset named as TrialTrove, to showcase MDBERT's strength
in delivering different levels of semantics.


title:
Extraction of Sleep Information from Clinical Notes of Alzheimer's
  Disease Patients Using Natural Language Processing

abstract:
  Alzheimer's Disease (AD) is the most common form of dementia in the United
States. Sleep is one of the lifestyle-related factors that has been shown
critical for optimal cognitive function in old age. . However, there is a lack
of research studying the association between sleep and AD incidence. A major
bottleneck for conducting such research is that the traditional way to acquire
sleep information is time-consuming, inefficient, non-scalable, and limited to
patients' subjective experience. In this study, we developed a rule-based NLP
algorithm and machine learning models to automate the extraction of
sleep-related concepts, including snoring, napping, sleep problem, bad sleep
quality, daytime sleepiness, night wakings, and sleep duration, from the
clinical notes of patients diagnosed with AD. We trained and validated the
proposed models on the clinical notes retrieved from the University of
Pittsburgh of Medical Center (UPMC). The results show that the rule-based NLP
algorithm consistently achieved the best performance for all sleep concepts.


title: Federated Learning for Distributed Energy-Efficient Resource Allocation

abstract:
  In cellular networks, resource allocation is performed in a centralized way,
which brings huge computation complexity to the base station (BS) and high
transmission overhead. This paper investigates the distributed resource
allocation scheme for cellular networks to maximize the energy efficiency of
the system in the uplink transmission, while guaranteeing the quality of
service (QoS) for cellular users. Particularly, to cope the fast varying
channels in wireless communication environment, we propose a robust federated
reinforcement learning (FRL_suc) framework to enable local users to perform
distributed resource allocation in items of transmit power and channel
assignment by the guidance of the local neural network trained at each user.
Analysis and numerical results show that the proposed FRL_suc framework can
lower the transmission overhead and offload the computation from the central
server to the local users, while outperforming the conventional multi-agent
reinforcement learning algorithm in terms of EE, and is more robust to channel
variations.


title:
Deep Reinforcement Learning for a Two-Echelon Supply Chain with Seasonal
  Demand

abstract:
  This paper leverages recent developments in reinforcement learning and deep
learning to solve the supply chain inventory management problem, a complex
sequential decision-making problem consisting of determining the optimal
quantity of products to produce and ship to different warehouses over a given
time horizon. A mathematical formulation of the stochastic two-echelon supply
chain environment is given, which allows an arbitrary number of warehouses and
product types to be managed. Additionally, an open-source library that
interfaces with deep reinforcement learning algorithms is developed and made
publicly available for solving the inventory management problem. Performances
achieved by state-of-the-art deep reinforcement learning algorithms are
compared through a rich set of numerical experiments on synthetically generated
data. The experimental plan is designed and performed, including different
structures, topologies, demands, capacities, and costs of the supply chain.
Results show that the PPO algorithm adapts very well to different
characteristics of the environment. The VPG algorithm almost always converges
to a local maximum, even if it typically achieves an acceptable performance
level. Finally, A3C is the fastest algorithm, but just like the VPG, it never
achieves the best performance when compared to PPO. In conclusion, numerical
experiments show that deep reinforcement learning performs consistently better
than standard inventory management strategies, such as the static (s,
Q)-policy. Thus, it can be considered a practical and effective option for
solving real-world instances of the stochastic two-echelon supply chain
problem.


title: The conceptual and mathematical foundations of the MC-QTAIM

abstract:
  The concept of atoms in molecules (AIM) is one of the cornerstones of the
structural theory of chemistry however, in contrast to the free atoms a
comprehensive quantum mechanical theory of AIM has never been proposed.
Currently the most satisfactory deduction of this concept is based on the
partitioning methodologies that are trying to recover AIM from the ab initio
wavefunctions (WF). One of these methodologies is the quantum theory of AIM
(QTAIM), which retrieves AIM by an exhaustive partitioning of the one-electron
density into atomic basins in real space. The molecular properties are then
partitioned into the basin and inter-basin contributions as the incarnation of
the AIM properties and their interaction modes. The inputs of the QTAIM
partitioning scheme are the electronic WF computed from the electronic
Schrodinger equation (SE), which is basically a single-component equation
treating electrons as quantum particles and the nuclei as clamped point charges
(CPC). A recently extended form of the QTAIM, called the multi-component QTAIM
(MC-QTAIM), removes this restriction and enables AIM partitioning to be applied
to the MC quantum systems. This is done using MC-WF as inputs that are derived
from the MC-SE in which there are two or more types of quantum particles. This
opens the possibility for the AIM partitioning of molecular systems where
certain nuclei, e.g. because of their non-adiabatic coupling to electrons, must
be treated as quantum particles instead of CPC. The same formalism allows the
partitioning of exotic molecular systems in which there are other elementary
particles like muons or positrons, in addition to electrons and nuclei. The
application of the MC-QTAIM partitioning to such systems reveals that the
positively charged muon may shape its own atomic basin, i.e. an example of
exotic AIM, while positron may act as an agent of bonding, i.e. an example of
exotic bonds.


title:
Nusselt number for steady periodically developed heat transfer in micro-
  and mini-channels with arrays of offset strip fins subject to a uniform heat
  flux

abstract:
  In this work, the Nusselt number is examined for periodically developed heat
transfer in micro- and mini-channels with arrays of offset strip fins, subject
to a constant heat flux. The Nusselt number is defined on the basis of a heat
transfer coefficient which represents the spatially constant macro-scale
temperature difference between the fluid and solid during conjugate heat
transfer. Its values are determined numerically on a single unit cell of the
array for Reynolds numbers between 1 and 600. Two combinations of the Prandtl
number and the thermal conductivity ratio are selected, corresponding to air
and water. It is shown that the Nusselt number correlations from the literature
mainly apply to air in the transitional flow regime in larger conventional
channels if the wall temperature remains uniform. As a result, they do not
correctly capture the observed trends for the Nusselt number in micro- and
mini-channels subject to a constant heat flux. Therefore, new Nusselt number
correlations, obtained through a least-squares fitting of 2282 numerical
simulations, are presented for air and water. The suitability of these
correlations is assessed via the Bayesian approach for parameter estimation and
model validation. The correlations respect the observed asymptotic trends and
limits of the Nusselt number for all the geometrical parameters of the offset
strip fins. In addition, they predict a linear dependence of the Nusselt number
on the Reynolds number, in good agreement with the data from this work.
Nevertheless, a detailed analysis reveals a more complex scaling of the Nusselt
number with the Reynolds number, closely related to the underlying flow
regimes, particularly the weak and strong inertia regimes. Finally, through 62
additional simulations, the influence of the material properties on the Nusselt
number is illustrated and compared to the available literature.


title: Detecting Unintended Memorization in Language-Model-Fused ASR

abstract:
  End-to-end (E2E) models are often being accompanied by language models (LMs)
via shallow fusion for boosting their overall quality as well as recognition of
rare words. At the same time, several prior works show that LMs are susceptible
to unintentionally memorizing rare or unique sequences in the training data. In
this work, we design a framework for detecting memorization of random textual
sequences (which we call canaries) in the LM training data when one has only
black-box (query) access to LM-fused speech recognizer, as opposed to direct
access to the LM. On a production-grade Conformer RNN-T E2E model fused with a
Transformer LM, we show that detecting memorization of singly-occurring
canaries from the LM training data of 300M examples is possible. Motivated to
protect privacy, we also show that such memorization gets significantly reduced
by per-example gradient-clipped LM training without compromising overall
quality.


title:
On the Practical Design of Tube-Enhanced Multi-Stage Nonlinear Model
  Predictive Control

abstract:
  Tube-enhanced multi-stage nonlinear model predictive control is a robust
control scheme that can handle a wide range of uncertainties with reduced
conservatism and manageable computational complexity. In this paper, we
elaborate on the flexibility of the approach from an application point of view.
We discuss the path to making design decisions to implement the novel scheme
systematically. We illustrate the critical steps in the design and
implementation of the scheme for an industrial example.


title:
CM elliptic curves of rank 2 and nonvanishing of generalised Kato
  classes

abstract:
  Let $E_{/\mathbf{Q}}$ be a CM elliptic curve and $p>3$ a prime of good
ordinary reduction for $E$. Assume $L(E,1)=0$ with sign $+1$ (so ${\rm
ord}_{s=1}L(E,s)\geq 2$) and $E$ has a rational point of infinite order. We
prove that if ${\rm Sel}(\mathbf{Q},V_pE)$ is $2$-dimensional, then a certain
generalised Kato class $\kappa_p\in{\rm Sel}(\mathbf{Q},V_pE)$ is nonzero; and
conversely, the nonvanishing of $\kappa_p$ implies that ${\rm
Sel}(\mathbf{Q},V_pE)$ is $2$-dimensional. For non-CM elliptic curves, a
similar result was proved in a joint work with M.-L. Hsieh. The proof in this
paper is completely different, and should extend to other settings. Moreover,
combined with work of Rubin, we can exhibit explicit bases for $2$-dimensional
${\rm Sel}(\mathbf{Q},V_pE)$.


title: Controlling atomic spin-mixing via multiphoton transitions in a cavity

abstract:
  We propose to control spin-mixing dynamics in a gas of spinor atoms, via the
combination of two off-resonant Raman transition pathways, enabled by a common
cavity mode and a bichromatic pump laser. The mixing rate, which is
proportional to the synthesized spin-exchange interaction strength, and the
effective atomic quadratic Zeeman shift (QZS), can both be tuned by changing
the pump laser parameters. Quench and driving dynamics of the atomic collective
spin are shown to be controllable on a faster time scale than in existing
experiments based on inherent spin-exchange collision interactions. The results
we present open a promising avenue for exploring spin-mixing physics of atomic
ensembles accessible in current experiments.