title:
Optimal estimation of conjugate shifts in position and momentum by
  classically correlated probes and measurements

abstract:
  Multi-parameter estimation is necessary for force sensing due to simultaneous
and nontrivial small changes of position and momentum. Designing quantum probes
that allow simultaneous estimation of all parameters is therefore an important
task. The optimal methods for estimation of the conjugate changes of position
and momentum of quantum harmonic oscillator employ probes in entangled or
quantum non-Gaussian states. We show that the same results can be obtained in a
significantly more feasible fashion by employing independent sets of
differently squeezed Gaussian states classically correlated to position or
momentum measurements. This result demonstrates the unexplored power of a
classical correlation between the probe states and measurements directly
applicable to force sensing


title:
Periodic orbits in the 1:2:3 resonant chain and their impact on the
  orbital dynamics of the Kepler-51 planetary system

abstract:
  Space missions have discovered a large number of exoplanets evolving in (or
close to) mean-motion resonances (MMRs) and resonant chains. Often, the
published data exhibit very high uncertainties due to the observational
limitations that introduce chaos into the evolution of the system on especially
shorter or longer timescales. We propose a study of the dynamics of such
systems by exploring particular regions in phase space. We exemplify our method
by studying the long-term orbital stability of the three-planet system
Kepler-51 and either favor or constrain its data. It is a dual process which
breaks down in two steps: the computation of the families of periodic orbits in
the 1:2:3 resonant chain and the visualization of the phase space through maps
of dynamical stability. We present novel results for the general four-body
problem. Stable periodic orbits were found only in the low-eccentricity regime.
We demonstrate three possible scenarios safeguarding Kepler-51, each followed
by constraints. Firstly, there are the 2/1 and 3/2 two-body MMRs, in which
$e_b<0.02$, such that these two-body MMRs last for extended time spans.
Secondly, there is the 1:2:3 three-body Laplace-like resonance, in which
$e_c<0.016$ and $e_d<0.006$ are necessary for such a chain to be viable.
Thirdly, there is the combination comprising the 1/1 secondary resonance inside
the 2/1 MMR for the inner pair of planets and an apsidal difference oscillation
for the outer pair of planets in which the observational eccentricities, $e_b$
and $e_c$, are favored as long as $e_d\approx 0$. With the aim to obtain an
optimum deduction of the orbital elements, this study showcases the need for
dynamical analyses based on periodic orbits performed in parallel to the
fitting processes.


title: Corrigendum: Liftings of Jordan planes

abstract:
  We complete the classification of the pointed Hopf algebras with finite
Gelfand-Kirillov dimension that are liftings of the Jordan plane over a
nilpotent-by-finite group, correcting the statement in arXiv:1512.09271.


title:
OFFO minimization algorithms for second-order optimality and their
  complexity

abstract:
  An Adagrad-inspired class of algorithms for smooth unconstrained optimization
is presented in which the objective function is never evaluated and yet the
gradient norms decrease at least as fast as $\calO(1/\sqrt{k+1})$ while
second-order optimality measures converge to zero at least as fast as
$\calO(1/(k+1)^{1/3})$. This latter rate of convergence is shown to be
essentially sharp and is identical to that known for more standard algorithms
(like trust-region or adaptive-regularization methods) using both function and
derivatives' evaluations. A related "divergent stepsize" method is also
described, whose essentially sharp rate of convergence is slighly inferior. It
is finally discussed how to obtain weaker second-order optimality guarantees at
a (much) reduced computional cost.


title: Exceptional Point Singularities in Multi-Section DFB Lasers

abstract:
  A laser exhibits both controllable gain and loss and, under proper design
conditions, is an ideal non-Hermitian system allowing the direct observation
and engineering of spectral singularities such as exceptional points (EPs). A
dual section distributed feedback (DFB) quantum cascade laser (QCL) is a
prototype of such a system, allowing the controlled coupling of a ladder of
cavity Fabry-Perot (FP) modes to a quarter wave shifted DFB mode. Tuning the
coupling strength and the gain difference between these two set of modes
enables probing the regimes from weak coupling to strong coupling and the
robust observation of exceptional point singularities. At these exceptional
points, the laser exhibits a sequence of lasing and coherent prefect absorption
dynamics1,2 when pumped above transparency. Additionally, the pumping scheme
allows the deliberate lifting of the exceptional point degeneracy. These
results show that dual section QCL is a perfect platform to study exceptional
points because the coupling parameter and system loss can be tuned in a single
device.


title:
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for
  Approximate Bayesian Inference

abstract:
  Full Bayesian posteriors are rarely analytically tractable, which is why
real-world Bayesian inference heavily relies on approximate techniques.
Approximations generally differ from the true posterior and require diagnostic
tools to assess whether the inference can still be trusted. We investigate a
new approach to diagnosing approximate inference: the approximation mismatch is
attributed to a change in the inductive bias by treating the approximations as
exact and reverse-engineering the corresponding prior. We show that the problem
is more complicated than it appears to be at first glance, because the solution
generally depends on the observation. By reframing the problem in terms of
incompatible conditional distributions we arrive at a natural solution: the
Gibbs prior. The resulting diagnostic is based on pseudo-Gibbs sampling, which
is widely applicable and easy to implement. We illustrate how the Gibbs prior
can be used to discover the inductive bias in a controlled Gaussian setting and
for a variety of Bayesian models and approximations.


title: Morphologies of Wolf-Rayet Planetary Nebulae based on IFU Observations

abstract:
  Integral field unit (IFU) spectroscopy of planetary nebulae (PNe) provides a
plethora of information about their morphologies and ionization structures. An
IFU survey of a sample of PNe around hydrogen-deficient stars has been
conducted with the Wide Field Spectrograph (WiFeS) on the ANU 2.3-m telescope.
In this paper, we present the H$\alpha$ kinematic observations of the PN M2-42
with a weak emission-line star ($wels$), and the compact PNe Hen 3-1333 and Hen
2-113 around Wolf-Rayet ([WR]) stars from this WiFeS survey. We see that the
ring and point-symmetric knots previously identified in the velocity [N II]
channels of M2-42 are also surrounded by a thin exterior ionized H$\alpha$
halo, whose polar expansion is apparently faster than the low-ionization knots.
The velocity-resolved H$\alpha$ channel maps of Hen3-1333 and Hen2-113 also
suggest that the faint multipolar lobes may get to a projected outflow velocity
of $\sim 100 \pm 20$ km s$^{-1}$ far from the central stars. Our recent
kinematic studies of the WiFeS/IFU survey of other PNe around [WR] and $wels$
mostly hint at elliptical morphologies, while collimated outflows are present
in many of them. As the WiFeS does not have adequate resolution for compact
($\leq 6$ arcsec) PNe, future high-resolution spatially-resolved observations
are necessary to unveil full details of their morpho-kinematic structures.


title:
Reliably Re-Acting to Partner's Actions with the Social Intrinsic
  Motivation of Transfer Empowerment

abstract:
  We consider multi-agent reinforcement learning (MARL) for cooperative
communication and coordination tasks. MARL agents can be brittle because they
can overfit their training partners' policies. This overfitting can produce
agents that adopt policies that act under the expectation that other agents
will act in a certain way rather than react to their actions. Our objective is
to bias the learning process towards finding reactive strategies towards other
agents' behaviors. Our method, transfer empowerment, measures the potential
influence between agents' actions. Results from three simulated cooperation
scenarios support our hypothesis that transfer empowerment improves MARL
performance. We discuss how transfer empowerment could be a useful principle to
guide multi-agent coordination by ensuring reactiveness to one's partner.


title:
Measurement of double beta decay of $^{150}$Nd to the 0$^+_1$ excited
  state of $^{150}$Sm in NEMO-3

abstract:
  NEMO-3 results for the double beta decay of $^{150}$Nd to the 0 $^+_1$ and
2$^+_1$ excited states of $^{150}$Sm are reported. The data recorded during
5.25 y with 36.6 g of the isotope $^{150}$Nd were used in the analysis. For the
first time the signal of $2\nu\beta\beta$ transition to the 0$^+_1$ excited
state is detected with statistical significance exceeding 5 sigma. The
half-life is measured to be $T_{1/2}^{2\nu\beta\beta}(0^+_1) = \left[ 1.11
^{+0.19}_{-0.14} \,\left(\mbox{stat}\right) ^{+0.17}_{-0.15}\,
\left(\mbox{syst}\right) \right] \times10^{20}\,\mbox{y}$. Limits are set on
$2\nu\beta\beta$ decay to 2$^+_1$ level and on $0\nu\beta\beta$ decay to
0$^+_1$ and 2$^+_1$ levels of $^{150}$Sm.


title:
Analysis and Optimization of Hybrid Caching in mmWave Networks with BS
  Cooperation

abstract:
  In this paper, we investigate a hybrid caching strategy maximizing the
success transmission probability (STP) in a millimeter wave (mmWave)
cache-enabled network. First, we derive theoretical expressions of the STP and
the average system transmission delay by utilizing stochastic geometry, then we
consider the maximization of the STP and the minimization of the average system
transmission delay by optimizing the design parameters. Considering the
optimality structure of the NP-hard problem, the original problem is
transferred into a multi-choice knapsack problem (MCKP). Finally, we
investigate the impact of key network parameters on the STP and the average
system transmission delay. Numerical results demonstrate the superiority of the
proposed caching strategy over the conventional caching strategies in the
mmWave cache-enabled networks.


title: Turbocharging Heuristics for Weak Coloring Numbers

abstract:
  Bounded expansion and nowhere-dense classes of graphs capture the theoretical
tractability for several important algorithmic problems. These classes of
graphs can be characterized by the so-called weak coloring numbers of graphs,
which generalize the well-known graph invariant degeneracy (also called
$k$-core number). Being NP-hard, weak-coloring numbers were previously computed
on real-world graphs mainly via incremental heuristics. We study whether it is
feasible to augment such heuristics with exponential-time subprocedures that
kick in when a desired upper bound on the weak coloring number is breached. We
provide hardness and tractability results on the corresponding computational
subproblems. We implemented several of the resulting algorithms and show them
to be competitive with previous approaches on a previously studied set of
benchmark instances containing 55 graphs with up to 8581 edges. We obtain
improved weak coloring numbers for half of the instances. We also establish a
baseline for lower bounds on the coloring numbers.


title:
Atomic scale skyrmions and large topological Hall effect in a
  breathing-kagome lattice

abstract:
  Motivated by recent experiments in Gd$_3$Ru$_4$Al$_{12}$, we demonstrate the
emergence of atomic scale Skyrmions in interacting spins on a breathing kagome
lattice with competing nearest neighbor ferromagnetic and next nearest neighbor
antiferromagnetic exchange interactions. In the presence of an applied
longitudinal magnetic field, the ground state magnetic order evolves from a
helical phase at low fields to a Skyrmion phase at intermediate fields before
finally entering a polarized phase at high fields. The size of each Skyrmion
spans only two unit cells of the lattice, in contrast to tens to hundreds of
unit cells in most chiral magnets. Furthermore, the Skyrmions are driven not by
chiral interactions but by the interplay between competing exchange
interactions and geometric frustration, just as in Gd$_3$Ru$_4$Al$_{12}$ . When
itinerant electrons are coupled to the localized moments, they exhibit the
usual Skyrmion-driven topological Hall effect (THE) arising from the real space
Berry curvature of the the Skyrmion texture. The small size of the Skyrmions in
this system yield a strong local Berry curvature that results in an enhanced
THE, which is investigated using a strong coupling approximation between the
spins of the itinerant electrons and the localized moments. Our results will be
crucial in understanding the experiments in Gd$_3$Ru$_4$Al$_{12}$ and other
members of the same family of metallic frustrated magnets.


title: Machine Learning to identify ICL and BCG in simulated galaxy clusters

abstract:
  Nowadays, Machine Learning techniques offer fast and efficient solutions for
classification problems that would require intensive computational resources
via traditional methods. We examine the use of supervised Random Forest
algorithms to classify stars in simulated galaxy clusters, after subtracting
the stars belonging to the member galaxies. These dynamically different
components are interpreted as the individual properties of the stars in the
Brightest Cluster Galaxy (BCG) and IntraCluster Light (ICL). We employ matched
stellar catalogues (following, Dolag et al. 2010) of 29 simulated clusters from
the DIANOGA set to train and test the classifier. The input features are
cluster mass, normalized particle cluster-centric distance, and rest-frame
velocity. The model is found to correctly identify most of the star particles,
while the larger errors are exhibited at the BCG outskirt, where the
differences between the physical properties of the two components are less
obvious. We investigate the robustness of the classifier to numerical
resolution, redshift dependence (up to $z=1$), and included astrophysical
models. We claim that our classifier provides consistent results in simulations
at $z<1$, at different resolution levels and with significantly different
subgrid models. The phase-space structure is examined to assess whether the
general properties of the stellar components are recovered: indeed, BCG
outskirts represent the most critical region and are far more prone to
uncertainties in the classification process. In conclusion, this work suggests
the importance of employing Machine Learning to speed up a computationally
expensive classification in numerical simulations.


title:
Dephasing enhanced strong Majorana zero modes in 2D and 3D higher-order
  topological superconductors

abstract:
  The 1D Kitaev model in the topological phase, with open boundary conditions,
hosts strong Majorana zero modes. These are fermion parity-odd operators that
almost commute with the Hamiltonian and manifest in long coherence times for
edge degrees of freedom. We obtain higher-dimensional counterparts of such
Majorana operators by explicitly computing their closed form expressions in
models describing 2D and 3D higher-order superconductors. Due to the existence
of such strong Majorana zero modes, the coherence time of the infinite
temperature autocorrelation function of the corner Majorana operators in these
models diverges with the linear system size. In the presence of a certain class
of orbital-selective dissipative dynamics, the coherence times of half of the
corner Majorana operators is enhanced, while the time correlations
corresponding to the remaining corner Majoranas decay much faster as compared
with the unitary case. We numerically demonstrate robustness of the coherence
times to the presence of disorder.


title: Stabilising all K\"ahler moduli in perturbative LVS

abstract:
  In this work we investigate the moduli stabilisation problem and the
requirements for de Sitter vacua within the framework of type IIB string
theory. Using perturbative effects arising from the various sources such as
$\alpha^\prime$ corrections, logarithmic as well as KK and winding-type
string-loop corrections along with the higher derivative $F^4$-contributions,
we present a moduli stabilisation scheme in which the overall volume is
realised at exponentially large values such that $\langle {\cal V} \rangle
\simeq e^{a/g_s^2}$ in the weak coupling regime, where $a$ is a parameter given
as $a = \frac{\zeta[3]}{2 \zeta[2]} \simeq 0.365381$. We also present a
concrete global construction using a $K3$-fibred CY threefold with $h^{1,1} =3$
which shares many of its properties with those of the standard toroidal case,
and subsequently generates the appropriate corrections needed to fix all the
three K\"ahler moduli at the perturbative level. We further discuss whether de
Sitter vacua can be ensured through appropriate contributions of uplifting
terms in the effective potential.


title: Dispute-free Scalable Open Vote Network using zk-SNARKs

abstract:
  The Open Vote Network is a self-tallying decentralized e-voting protocol
suitable for boardroom elections. Currently, it has two Ethereum-based
implementations: the first, by McCorry et al., has a scalability issue since
all the computations are performed on-chain. The second implementation, by
Seifelnasr et al., solves this issue partially by assigning a part of the heavy
computations to an off-chain untrusted administrator in a verifiable manner. As
a side effect, this second implementation became not dispute-free; there is a
need for a tally dispute phase where an observer interrupts the protocol when
the administrator cheats, i.e., announces a wrong tally result. In this work,
we propose a new smart contract design to tackle the problems in the previous
implementations by (i) preforming all the heavy computations off-chain hence
achieving higher scalability, and (ii) utilizing zero-knowledge Succinct
Non-interactive Argument of Knowledge (zk-SNARK) to verify the correctness of
the off-chain computations, hence maintaining the dispute-free property. To
demonstrate the effectiveness of our design, we develop prototype
implementations on Ethereum and conduct multiple experiments for different
implementation options that show a trade-off between the zk-SNARK proof
generation time and the smart contract gas cost, including an implementation in
which the smart contract consumes a constant amount of gas independent of the
number of voters.


title:
Optimized state transfer in systems of ultrastrongly coupled matter and
  radiation

abstract:
  Ultrastrong coupling may allow faster operations for the development of
quantum technologies at the expenses of increased sensitivity to new kind of
intrinsic errors. We study state transfer in superconducting circuit QED
architectures in the ultrastrong coupling regime. Using optimal control methods
we find a protocol resilient to the main source of errors, coming from the
interplay of the dynamical Casimir effect with cavity losses.


title:
Machine learning using longitudinal prescription and medical claims for
  the detection of nonalcoholic steatohepatitis (NASH)

abstract:
  Objectives To develop and evaluate machine learning models to detect
suspected undiagnosed nonalcoholic steatohepatitis (NASH) patients for
diagnostic screening and clinical management.
  Methods In this retrospective observational noninterventional study using
administrative medical claims data from 1,463,089 patients, gradient-boosted
decision trees were trained to detect likely NASH patients from an at-risk
patient population with a history of obesity, type 2 diabetes mellitus (T2DM),
metabolic disorder, or nonalcoholic fatty liver (NAFL). Models were trained to
detect likely NASH in all at-risk patients or in the subset without a prior
NAFL diagnosis (non-NAFL at-risk patients). Models were trained and validated
using retrospective medical claims data and assessed using area under precision
recall and receiver operating characteristic curves (AUPRCs, AUROCs).
  Results The 6-month incidence of NASH in claims data was 1 per 1,437 at-risk
patients and 1 per 2,127 non-NAFL at-risk patients. The model trained to detect
NASH in all at-risk patients had an AUPRC of 0.0107 (95% CI 0.0104 - 0.011) and
an AUROC of 0.84. At 10% recall, model precision was 4.3%, which is 60x above
NASH incidence. The model trained to detect NASH in non-NAFL patients had an
AUPRC of 0.003 (95% CI 0.0029 - 0.0031) and an AUROC of 0.78. At 10% recall,
model precision was 1%, which is 20x above NASH incidence.
  Conclusion The low incidence of NASH in medical claims data corroborates the
pattern of NASH underdiagnosis in clinical practice. Claims-based machine
learning could facilitate the detection of probable NASH patients for
diagnostic testing and disease management.


title:
Improvements to Gradient Descent Methods for Quantum Tensor Network
  Machine Learning

abstract:
  Tensor networks have demonstrated significant value for machine learning in a
myriad of different applications. However, optimizing tensor networks using
standard gradient descent has proven to be difficult in practice. Tensor
networks suffer from initialization problems resulting in exploding or
vanishing gradients and require extensive hyperparameter tuning. Efforts to
overcome these problems usually depend on specific network architectures, or ad
hoc prescriptions. In this paper we address the problems of initialization and
hyperparameter tuning, making it possible to train tensor networks using
established machine learning techniques. We introduce a `copy node' method that
successfully initializes arbitrary tensor networks, in addition to a gradient
based regularization technique for bond dimensions. We present numerical
results that show that the combination of techniques presented here produces
quantum inspired tensor network models with far fewer parameters, while
improving generalization performance.


title: Multi-CPR: A Multi Domain Chinese Dataset for Passage Retrieval

abstract:
  Passage retrieval is a fundamental task in information retrieval (IR)
research, which has drawn much attention recently. In the English field, the
availability of large-scale annotated dataset (e.g, MS MARCO) and the emergence
of deep pre-trained language models (e.g, BERT) has resulted in a substantial
improvement of existing passage retrieval systems. However, in the Chinese
field, especially for specific domains, passage retrieval systems are still
immature due to quality-annotated dataset being limited by scale. Therefore, in
this paper, we present a novel multi-domain Chinese dataset for passage
retrieval (Multi-CPR). The dataset is collected from three different domains,
including E-commerce, Entertainment video and Medical. Each dataset contains
millions of passages and a certain amount of human annotated query-passage
related pairs. We implement various representative passage retrieval methods as
baselines. We find that the performance of retrieval models trained on dataset
from general domain will inevitably decrease on specific domain. Nevertheless,
a passage retrieval system built on in-domain annotated dataset can achieve
significant improvement, which indeed demonstrates the necessity of domain
labeled data for further optimization. We hope the release of the Multi-CPR
dataset could benchmark Chinese passage retrieval task in specific domain and
also make advances for future studies.


title:
On the properties of the Aron-Berner regularity of bounded tri-linear
  maps

abstract:
  Let $f:X\times Y\times Z\longrightarrow W $ be a bounded tri-linear map on
normed spaces. We say that $f$ is close-to-regular when $f^{t****s}=f^{s****t}$
and $f$ is Aron-Berener regular when all natural extensions are equal. In this
manuscript, we have some results on the Aron-Berner regular maps. We
investigate the relation between Arens regularity of bounded bilinear maps and
Aron-Berner regularity of bounded tri-linear maps. We also give a simple
criterion for the Aron-Berner regularity of tri-linear maps.


title:
Spectral softening in core-collapse supernova remnant expanding inside
  wind-blown bubble

abstract:
  Context. Galactic cosmic rays are widely assumed to arise from diffusive
shock acceleration, specifically at shocks in supernova remnants (SNRs). These
shocks expand in a complex environment, particularly in the core-collapse
scenario as these SNRs evolve inside the wind-blown bubbles created by their
progenitor stars. The cosmic rays (CRs) at core-collapse SNRs may carry
spectral signatures of that complexity. Aims. We study particle acceleration in
the core-collapse SNR of a progenitor with initial mass 60 $M_\odot$ and
realistic stellar evolution. The SNR shock interacts with discontinuities
inside the wind-blown bubble and generates several transmitted and reflected
shocks. We analyse their impact on particle spectra and the resulting emission
from the remnant. Methods. The hydrodynamic equations for the evolution of SNR
inside the pre-supernova circumstellar medium have been solved simultaneously
with the transport equation for cosmic rays in test-particle approximation and
with the induction equation for the magnetohydrodynamics (MHD) in 1-D spherical
symmetry. Results. The evolution of core-collapse SNRs inside complex
wind-blown bubbles modifies the spectra of both the particles and their
emission. We have found softer particle spectra with spectral indices close to
2.5 during shock propagation inside the shocked wind, and this softness
persists at later evolutionary stages. Further, our calculated total production
spectrum released into the interstellar medium demonstrates spectral
consistency at high energy with the galactic CRs injection spectrum, required
in propagation models. The magnetic field structure effectively influences the
emission morphology of SNR as it governs the transportation of particles and
the synchrotron emissivity.


title: Complete dark energy scenario in $f(Q)$ gravity

abstract:
  In theoretical physics, the fundamental nature and evolution mechanism of
dark energy is still an open question. In General Relativity Theory, the
simplest explanation for dark energy is the cosmological constant $\Lambda$.
However, the cosmological constant $\Lambda$ facing a sensitive problem called
fine-tunning problem. In the present work, we follow a different approach where
the gravitational sector is the responsible candidate for the evolution of dark
energy instead of the matter source. The modified symmetric teleparallel
gravity or $f(Q)$ gravity is a recently proposed theory of gravity where the
gravitational interaction ruled by the non-metricity term $Q$. In this
manuscript, we assume a $f(Q)$ model that contains a linear and a non-linear
form of non-metricity scalar, particularly $f(Q)=\alpha Q + \beta Q^n$, where
$\alpha$, $\beta$, and $n$ are free model parameters. Then we find the values
of our model parameters that would be in agreement with the observed value of
cosmographic parameters. We analyze the behavior of different cosmological
parameters like deceleration parameter, density, and the equation of state
parameter with the energy conditions for our cosmological model. We found that
for higher positive values of $n$ specifically $n \geq 1$, dark energy fluid
part evolving due to non-metricity behaves like quintessence type dark energy
while for higher negative values of $n$ specifically $n \leq -1$ it follows
phantom scenario. Further for $n=0$, our cosmological $f(Q)$ model behaves like
$\Lambda$CDM model. Thus, we conclude that the geometrical generalization of GR
can be a viable candidate for the description of origin of the dark energy.


title:
The Franson experiment as an example of spontaneous breaking of
  time-translation symmetry

abstract:
  We describe an explicit statistical model of local hidden variables that
reproduces the predictions of quantum mechanics for the ideal Franson
experiment and sheds light on the physical mechanisms that might be involved in
the actual experiment. The crux of our model is the spontaneous breaking of the
time-translation gauge symmetry by the hidden configurations of the pairs of
photons locked in time and energy involved in the experiment, which acquire a
non-zero geometric phase through certain cyclic transformations.


title: Generalizing eleven-dimensional supergravity

abstract:
  We develop a procedure to reproduce the ten-dimensional generalized
supergravity equations from T-duality covariant equations, that facilitates
generalization to U-duality covariant formulations of eleven-dimensional
supergravity. The latter leads to a modification of the eleven-dimensional
supergravity equations with terms that contain a rank-2 tensor field $J^{mn}$
which is the eleven-dimensional analog of the non-unimodularity Killing vector
$I^m$ in ten dimensions.


title: Adversarial Texture for Fooling Person Detectors in the Physical World

abstract:
  Nowadays, cameras equipped with AI systems can capture and analyze images to
detect people automatically. However, the AI system can make mistakes when
receiving deliberately designed patterns in the real world, i.e., physical
adversarial examples. Prior works have shown that it is possible to print
adversarial patches on clothes to evade DNN-based person detectors. However,
these adversarial examples could have catastrophic drops in the attack success
rate when the viewing angle (i.e., the camera's angle towards the object)
changes. To perform a multi-angle attack, we propose Adversarial Texture
(AdvTexture). AdvTexture can cover clothes with arbitrary shapes so that people
wearing such clothes can hide from person detectors from different viewing
angles. We propose a generative method, named Toroidal-Cropping-based
Expandable Generative Attack (TC-EGA), to craft AdvTexture with repetitive
structures. We printed several pieces of cloth with AdvTexure and then made
T-shirts, skirts, and dresses in the physical world. Experiments showed that
these clothes could fool person detectors in the physical world.


title: A Unified Formulation of Geometry-aware Dynamic Movement Primitives

abstract:
  Learning from demonstration (LfD) is considered as an efficient way to
transfer skills from humans to robots. Traditionally, LfD has been used to
transfer Cartesian and joint positions and forces from human demonstrations.
The traditional approach works well for some robotic tasks, but for many tasks
of interest it is necessary to learn skills such as orientation, impedance,
and/or manipulability that have specific geometric characteristics. An
effective encoding of such skills can be only achieved if the underlying
geometric structure of the skill manifold is considered and the constrains
arising from this structure are fulfilled during both learning and execution.
However, typical learned skill models such as dynamic movement primitives
(DMPs) are limited to Euclidean data and fail in correctly embedding quantities
with geometric constraints. In this paper, we propose a novel and
mathematically principled framework that uses concepts from Riemannian geometry
to allow DMPs to properly embed geometric constrains. The resulting DMP
formulation can deal with data sampled from any Riemannian manifold including,
but not limited to, unit quaternions and symmetric and positive definite
matrices. The proposed approach has been extensively evaluated both on
simulated data and real robot experiments. The performed evaluation
demonstrates that beneficial properties of DMPs, such as convergence to a given
goal and the possibility to change the goal during operation, apply also to the
proposed formulation.


title: Rational exponents near two

abstract:
  A longstanding conjecture of Erd\H{o}s and Simonovits states that for every
rational $r$ between $1$ and $2$ there is a graph $H$ such that the largest
number of edges in an $H$-free graph on $n$ vertices is $\Theta(n^r)$.
Answering a question raised by Jiang, Jiang and Ma, we show that the conjecture
holds for all rationals of the form $2 - a/b$ with $b$ sufficiently large in
terms of $a$.


title: Spatio-temporal Gait Feature with Adaptive Distance Alignment

abstract:
  Gait recognition is an important recognition technology, because it is not
easy to camouflage and does not need cooperation to recognize subjects.
However, there are still serious challenges in gait recognition, that is,
people with similar walking posture are often recognized incorrectly. In this
paper, We try to increase the discrimination of extraced gait features of
different subjects to increase the recognition efficiency of subjects with
similar walking posture. It includes the optimization of network structure and
the refinement of extracted gait features. So our method is proposed, it
consists of Spatio-temporal Feature Extraction (SFE) and Adaptive Distance
Alignment (ADA), which SFE uses Temporal Feature Fusion (TFF) and Fine-grained
Feature Extraction (FFE) to effectively extract the spatio-temporal features
from raw silhouettes, ADA uses a large number of unlabeled gait data in real
life as a benchmark to refine the extracted spatio-temporal features to make
them have low inter-class similarity and high intra-class similarity. Extensive
experiments on mini-OUMVLP and CASIA-B have proved that we have a good result
than some state-of-the-art methods.


title: A Random Access Protocol for RIS-Aided Wireless Communications

abstract:
  Reconfigurable intelligent surfaces (RISs) are arrays of passive elements
that can control the reflection of the incident electromagnetic waves. While
RIS are particularly useful to avoid blockages, the protocol aspects for their
implementation have been largely overlooked. In this paper, we devise a random
access protocol for a RIS-assisted wireless communication setting. Rather than
tailoring RIS reflections to meet the positions of users equipment (UEs), our
protocol relies on a finite set of RIS configurations designed to cover the
area of interest. The protocol is comprised of a downlink training phase
followed by an uplink access phase. During these phases, a base station (BS)
controls the RIS to sweep over its configurations. The UEs then receive
training signals to measure the channel quality with the different RIS
configurations and refine their access policies. Numerical results show that
our protocol increases the average number of successful access attempts;
however, at the expense of increased access delay due to the realization of a
training period. Promising results are further observed in scenarios with a
high access load.


title:
Weak Gravity Conjecture from Conformal Field Theory: A Challenge from
  Hyperscaling Violating and Kerr-Newman-AdS Black Holes

abstract:
  We search for a possible relation between weak gravity conjecture and
conformal field theory in the hyperscaling violating and Kerr-Newman-AdS black
holes. We deal with the critical points of the black hole systems through the
correlation function introduced in conformal field theory. We discuss the weak
gravity conjecture conditions by the imaginary part of the energy obtained from
the critical points and their poles. Under the assumptions $z=1$, $d=1$ and
$\theta\rightarrow0^{-}$, we link weak gravity conjecture to the hyperscaling
violating black holes due to the existence of $r_{H}$ larger and smaller than
one. As the second black hole system, we study the conditions of weak gravity
conjecture for the Kerr-Newman-AdS black hole by rotation and radius
parameters. Then, we show that the conditions of weak gravity conjecture are
satisfied when the charged particle near the hyperscaling violating and
Kerr-Newman black holes is $\frac{1}{a}$ with a ratio $\frac{a}{\ell}\ll 1$.


title: An STDP-Based Supervised Learning Algorithm for Spiking Neural Networks

abstract:
  Compared with rate-based artificial neural networks, Spiking Neural Networks
(SNN) provide a more biological plausible model for the brain. But how they
perform supervised learning remains elusive. Inspired by recent works of Bengio
et al., we propose a supervised learning algorithm based on Spike-Timing
Dependent Plasticity (STDP) for a hierarchical SNN consisting of Leaky
Integrate-and-fire (LIF) neurons. A time window is designed for the presynaptic
neuron and only the spikes in this window take part in the STDP updating
process. The model is trained on the MNIST dataset. The classification accuracy
approach that of a Multilayer Perceptron (MLP) with similar architecture
trained by the standard back-propagation algorithm.


title:
Beam Profiling with Noise Reduction From Computer Vision and Principal
  Component Analysis for the MAGIS-100 Experiment

abstract:
  MAGIS-100 is a long-baseline atom interferometer that operates as a quantum
sensor. It will search for dark matter, probe fundamental quantum science, and
serve as a prototype gravitational wave detector in the 0.3 to 3~Hz frequency
range. The experiment uses light-pulse atom interferometry where pulses of
light create the atom optics equivalents of beamsplitters and mirrors. Laser
beam aberrations are a key source of systematic error for MAGIS-100, and
accurately characterizing the laser beam spatial profile is therefore
essential. In this paper, we describe a new and efficient beam profiling
technique. We use a low-cost CMOS camera affixed to a translating and rotating
optomechanical mount to image the beam, then employ computer vision and
principal component analysis to minimize background noise and produce accurate
beam profiles for a laser incident on a variety of aberration-inducing optical
elements.


title: On a variant of the happy numbers and their generalizations

abstract:
  This paper investigates a variant of the famous "happy numbers" sequence,
given by A351327 on the oeis. First of all we'll define this integer sequence,
and then we'll show some important results about it; in particular we
conjectured that if $k$ is a term of the sequence, then it converges to 1 in a
number of steps less or equal to 3. Furthermore it will be possible to find
some codes written in Wolfram language in order to compute large terms of the
sequence and to support our hypothesis. At the end we'll explore some new
conjectures and generalizations about this kind of integer sequence.


title: A Glyph-driven Topology Enhancement Network for Scene Text Recognition

abstract:
  Attention-based methods by establishing one-dimensional (1D) and
two-dimensional (2D) mechanisms with an encoder-decoder framework have
dominated scene text recognition (STR) tasks due to their capabilities of
building implicit language representations. However, 1D attention-based
mechanisms suffer from alignment drift on latter characters. 2D attention-based
mechanisms only roughly focus on the spatial regions of characters without
excavating detailed topological structures, which reduces the visual
performance. To mitigate the above issues, we propose a novel Glyph-driven
Topology Enhancement Network (GTEN) to improve topological features
representations in visual models for STR. Specifically, an unsupervised method
is first employed to exploit 1D sequence-aligned attention weights. Second, we
construct a supervised segmentation module to capture 2D ordered and pixel-wise
topological information of glyphs without extra character-level annotations.
Third, these resulting outputs fuse enhanced topological features to enrich
semantic feature representations for STR. Experiments demonstrate that GTEN
achieves competitive performance on IIIT5K-Words, Street View Text,
ICDAR-series, SVT Perspective, and CUTE80 datasets.


title:
Switching in the Rain: Predictive Wireless x-haul Network
  Reconfiguration

abstract:
  Wireless x-haul networks rely on microwave and millimeter-wave links between
4G and/or 5G base-stations to support ultra-high data rate and ultra-low
latency. A major challenge associated with these high frequency links is their
susceptibility to weather conditions. In particular, precipitation may cause
severe signal attenuation, which significantly degrades the network
performance. In this paper, we develop a Predictive Network Reconfiguration
(PNR) framework that uses historical data to predict the future condition of
each link and then prepares the network ahead of time for imminent
disturbances. The PNR framework has two components: (i) an Attenuation
Prediction (AP) mechanism; and (ii) a Multi-Step Network Reconfiguration (MSNR)
algorithm. The AP mechanism employs an encoder-decoder Long Short-Term Memory
(LSTM) model to predict the sequence of future attenuation levels of each link.
The MSNR algorithm leverages these predictions to dynamically optimize routing
and admission control decisions aiming to maximize network utilization, while
preserving max-min fairness among the base-stations sharing the network and
preventing transient congestion that may be caused by re-routing. We train,
validate, and evaluate the PNR framework using a dataset containing over 2
million measurements collected from a real-world city-scale backhaul network.
The results show that the framework: (i) predicts attenuation with high
accuracy, with an RMSE of less than 0.4 dB for a prediction horizon of 50
seconds; and (ii) can improve the instantaneous network utilization by more
than 200% when compared to reactive network reconfiguration algorithms that
cannot leverage information about future disturbances.


title: A New $p$-Control Chart with Measurement Error Correction

abstract:
  Control charts are important tools to monitor quality of products. One of
useful applications is to monitor the proportion of non-conforming products.
However, in practical applications, measurement error is ubiquitous and may
occur due to false records or misclassification, which makes the observed
proportion different from the underlying true proportion. It is also well-known
that ignoring measurement error effects provides biases, and is expected that
the resulting control charts may incur wrong detection. In this paper, we study
this important problem and propose a valid method to correct for measurement
error effects and obtain error-eliminated control chart for the proportion of
non-conforming products. In addition, unlike traditional approaches, the
corrected EWMA $p$-control chart provides asymmetric control limits and is
flexible to handle the data with small sample size. Numerical results are
conducted to justify the validity of the corrected EWMA $p$-control chart and
verify the necessity of measurement error correction.


title: FloorGenT: Generative Vector Graphic Model of Floor Plans for Robotics

abstract:
  Floor plans are the basis of reasoning in and communicating about indoor
environments. In this paper, we show that by modelling floor plans as sequences
of line segments seen from a particular point of view, recent advances in
autoregressive sequence modelling can be leveraged to model and predict floor
plans. The line segments are canonicalized and translated to sequence of tokens
and an attention-based neural network is used to fit a one-step distribution
over next tokens. We fit the network to sequences derived from a set of
large-scale floor plans, and demonstrate the capabilities of the model in four
scenarios: novel floor plan generation, completion of partially observed floor
plans, generation of floor plans from simulated sensor data, and finally, the
applicability of a floor plan model in predicting the shortest distance with
partial knowledge of the environment.


title: Depth-resolved Laue microdiffraction with coded-apertures

abstract:
  We introduce a rapid data acquisition and reconstruction method to image the
crystalline structure of materials and associated strain and orientations at
micrometer resolution using Laue diffraction. Our method relies on scanning a
coded-aperture across the diffracted x-ray beams from a broadband illumination,
and a reconstruction algorithm to resolve Laue microdiffraction patterns as a
function of depth along the incident illumination path. This method provides a
rapid access to full diffraction information at sub-micrometer volume elements
in bulk materials. Here we present the theory as well as the experimental
validation of this imaging approach.


title:
Floquet engineering the band structure of materials with optimal control
  theory

abstract:
  We demonstrate that the electronic structure of a material can be deformed
into Floquet pseudo-bands with arbitrarily tailored shapes. We achieve this
goal with a novel combination of quantum optimal control theory and Floquet
engineering. The power and versatility of this framework is demonstrated here
by utilizing the independent-electron tight-binding description of the $\pi$
electronic system of graphene. We show several prototype examples focusing on
the region around the K (Dirac) point of the Brillouin zone: creation of a gap
with opposing flat valence and conduction bands, creation of a gap with
opposing concave symmetric valence and conduction bands -- which would
correspond to a material with an effective negative electron-hole mass --, or
closure of the gap when departing from a modified graphene model with a
non-zero field-free gap. We employ time periodic drives with several frequency
components and polarizations, in contrast to the usual monochromatic fields,
and use control theory to find the amplitudes of each component that optimize
the shape of the bands as desired. In addition, we use quantum control methods
to find realistic switch-on pulses that bring the material into the predefined
stationary Floquet band structure, i.e. into a state in which the desired
Floquet modes of the target bands are fully occupied, so that they should
remain stroboscopically stationary, with long lifetimes, when the weak periodic
drives are started. Finally, we note that although we have focused on solid
state materials, the technique that we propose could be equally used for the
Floquet engineering of ultracold atoms in optical lattices, and to other
non-equilibrium dynamical and correlated systems.


title: Elementary discrete and continuous interplay

abstract:
  We illustrate the interplay between certain discrete and continuous problems,
by presenting a method for the study of the asymptotics of a divergent
sequence, through consideration of the asymptotics of its continuous analogue


title:
Carrier mobilities of Janus transition metal dichalcogenides monolayers
  studied by Born effective charge and first-principles calculation

abstract:
  Two-dimensional (2D) Janus transition metal dichalcogenides (TMDs) are a new
class of materials with unique physical properties. However, the carrier
mobility of most Janus TMDs calculated by deformation potential theory (DPT) is
not reliable due to the unconsidered part of lattice scattering. In this work,
we propose a new method of Born effective charge (BEC) to calculate the carrier
mobility of Janus TMDs by including the important factors that neglected in the
DPT. The BEC could be used in the calculation of both pure and defective Janus
TMDs by employing density functional perturbation theory. We have figured out
the relationship between the carrier mobility and the value of BEC, which is
the lower the absolute BEC, the higher the electron or hole mobility. Using the
new method, we have calculated the carrier mobility of commonly studied Janus
TMDs with and without defect. The method may shed light on the high-throughout
calculation of selecting high carrier mobility 2D materials.


title:
Multi-line assessment of narrow-line regions in $z \sim$ 3 radio
  galaxies

abstract:
  In this paper, we utilize high-quality rest-UV spectra of three radio
galaxies at $z \sim$ 3 observed with VLT/FORS2 to measure the flux of several
emission lines including relatively faint ones, such as NIV]$\lambda$1486,
OIII]$\lambda$1663, and [NeIV]$\lambda$2424. Additionally, we collect fluxes of
faint rest-UV emission lines in 12 $z \sim$ 3 radio galaxies from the
literature. Previously, physical and chemical properties of narrow-line regions
(NLRs) in high-$z$ active galactic nuclei (AGNs) have been investigated mostly
by using only strong rest-UV emission-lines (e.g., NV$\lambda$1240,
CIV$\lambda$1549, HeII$\lambda$1640, and CIII]$\lambda$1909). Such strong-line
diagnostics are based on various assumptions due to the limitation in the
number of available emission-line constraints. In this work, both physical and
chemical properties of NLR clouds in each object are estimated by fitting
detailed photoionization models to the measured emission-line fluxes. We
confirm that the metallicity of NLRs in AGNs at $z \sim$ 3 is solar or
super-solar, without assuming the gas density and ionization parameter thanks
to the constrains from the faint emission lines. This result suggests that
high-$z$ radio galaxies are already chemically matured at $z \sim$ 3.


title:
A Transferable Legged Mobile Manipulation Framework Based on Disturbance
  Predictive Control

abstract:
  Due to their ability to adapt to different terrains, quadruped robots have
drawn much attention in the research field of robot learning. Legged mobile
manipulation, where a quadruped robot is equipped with a robotic arm, can
greatly enhance the performance of the robot in diverse manipulation tasks.
Several prior works have investigated legged mobile manipulation from the
viewpoint of control theory. However, modeling a unified structure for various
robotic arms and quadruped robots is a challenging task. In this paper, we
propose a unified framework disturbance predictive control where a
reinforcement learning scheme with a latent dynamic adapter is embedded into
our proposed low-level controller. Our method can adapt well to various types
of robotic arms with a few random motion samples and the experimental results
demonstrate the effectiveness of our method.


title:
Naturally-meaningful and efficient descriptors: machine learning of
  material properties based on robust one-shot ab initio descriptors

abstract:
  Establishing a data-driven pipeline for the discovery of novel materials
requires the engineering of material features that can be feasibly calculated
and can be applied to predict a material's target properties. Here we propose a
new class of descriptors for describing crystal structures, which we term
Robust One-Shot Ab initio (ROSA) descriptors. ROSA is computationally cheap and
is shown to accurately predict a range of material properties. These simple and
intuitive class of descriptors are generated from the energetics of a material
at a low level of theory using an incomplete ab initio calculation. We
demonstrate how the incorporation of ROSA descriptors in ML-based property
prediction leads to accurate predictions over a wide range of crystals,
amorphized crystals, metal-organic frameworks and molecules. We believe that
the low computational cost and ease of use of these descriptors will
significantly improve ML-based predictions.


title: On the Hausdorff Measure of $\R^n$ with the Euclidean Topology

abstract:
  In this paper we answer a question raised by David H. Fremlin about the
Hausdorff measure of $\mathbb{R}^2$ with respect to a distance inducing the
Euclidean topology. In particular we prove that the Hausdorff $n$-dimensional
measure of $\mathbb{R}^n$ is never $0$ when considering a distance inducing the
Euclidean topology. Finally, we show via counterexamples that the previous
result does not hold in general if we remove the assumption on the topology.


title: Semidefinite programming lower bounds on the squashed entanglement

abstract:
  The squashed entanglement is a widely used entanglement measure that has many
desirable properties. However, as it is based on an optimization over
extensions of arbitrary dimension, one drawback of this measure is the lack of
good algorithms to compute it. Here, we introduce a hierarchy of semidefinite
programming lower bounds on the squashed entanglement.


title: A Note on Integrals Containing the Univariate Lommel Function

abstract:
  This short note investigates a number of index integrals of products of the
Lommel functions $s_{\mu,\nu}(a)$ and uncovers an integral relationship.
between this function and the Tchebyshev polynomials $T_{2n}(x)$.


title: Screentone-Preserved Manga Retargeting

abstract:
  As a popular comic style, manga offers a unique impression by utilizing a
rich set of bitonal patterns, or screentones, for illustration. However,
screentones can easily be contaminated with visual-unpleasant aliasing and/or
blurriness after resampling, which harms its visualization on displays of
diverse resolutions. To address this problem, we propose the first manga
retargeting method that synthesizes a rescaled manga image while retaining the
screentone in each screened region. This is a non-trivial task as accurate
region-wise segmentation remains challenging. Fortunately, the rescaled manga
shares the same region-wise screentone correspondences with the original manga,
which enables us to simplify the screentone synthesis problem as an
anchor-based proposals selection and rearrangement problem. Specifically, we
design a novel manga sampling strategy to generate aliasing-free screentone
proposals, based on hierarchical grid-based anchors that connect the
correspondences between the original and the target rescaled manga.
Furthermore, a Recurrent Proposal Selection Module (RPSM) is proposed to
adaptively integrate these proposals for target screentone synthesis. Besides,
to deal with the translation insensitivity nature of screentones, we propose a
translation-invariant screentone loss to facilitate the training convergence.
Extensive qualitative and quantitative experiments are conducted to verify the
effectiveness of our method, and notably compelling results are achieved
compared to existing alternative techniques.


title:
OverlapTransformer: An Efficient and Rotation-Invariant Transformer
  Network for LiDAR-Based Place Recognition

abstract:
  Place recognition is an important capability for autonomously navigating
vehicles operating in complex environments and under changing conditions. It is
a key component for tasks such as loop closing in SLAM or global localization.
In this paper, we address the problem of place recognition based on 3D LiDAR
scans recorded by an autonomous vehicle. We propose a novel lightweight neural
network exploiting the range image representation of LiDAR sensors to achieve
fast execution with less than 4 ms per frame. Based on that, we design a
yaw-rotation-invariant architecture exploiting a transformer network, which
boosts the place recognition performance of our method. We evaluate our
approach on the KITTI and Ford Campus datasets. The experimental results show
that our method can effectively detect loop closures compared to the
state-of-the-art methods and generalizes well across different environments. To
further evaluate long-term place recognition performance, we provide a novel
challenging Haomo dataset, which contains LiDAR sequences recorded by a mobile
robot in repetitive places across seasons. Both the implementation of our
method and our new Haomo dataset are released here:
https://github.com/haomo-ai/OverlapTransformer


title: Estimation and Model Misspecification: Fake and Missing Features

abstract:
  We consider estimation under model misspecification where there is a model
mismatch between the underlying system, which generates the data, and the model
used during estimation. We propose a model misspecification framework which
enables a joint treatment of the model misspecification types of having fake
and missing features, as well as incorrect covariance assumptions on the
unknowns and the noise. Here, features which are included in the model but are
not present in the underlying system, and features which are not included in
the model but are present in the underlying system, are referred to as fake and
missing features, respectively. Under this framework, we characterize the
estimation performance and reveal trade-offs between the missing and fake
features and the possibly incorrect noise level assumption. In contrast to
existing work focusing on incorrect covariance assumptions or missing features,
fake features is a central component of our framework. Our results show that
fake features can significantly improve the estimation performance, even though
they are not correlated with the features in the underlying system. In
particular, we show that the estimation error can be decreased by including
more fake features in the model, even to the point where the model is
overparametrized, i.e., the model contains more unknowns than observations.


title:
Building and curating conversational corpora for diversity-aware
  language science and technology

abstract:
  We present a pipeline and tools to build a maximally natural data set of
conversational interaction that covers 66 languages and varieties from 32
phyla. We describe the curation and compilation process moving from diverse
language documentation corpora to a unified format and describe an open-source
tool "convo-parse" to help in quality control and assessment of conversational
data. We conclude with two case studies of how diverse data sets can inform
interactional linguistics and speech recognition technology and thus contribute
to broadening the empirical foundations of language sciences and technologies
of the future.


title:
Quasi-bialgebras from set-theoretic type solutions of the Yang-Baxter
  equation

abstract:
  We examine classes of quantum algebras emerging from involutive,
non-degenerate set-theoretic solutions of the Yang-Baxter equation and their
q-analogues. After providing some universal results on quasi-bialgebras and
admissible Drinfeld twists we show that the quantum algebras produced from
set-theoretic solutions and their q-analogues are in fact quasi-triangular
quasi-bialgebras. Specific illustrative examples compatible with our generic
findings are worked out. In the q-deformed case of set-theoretic solutions we
also construct admissible Drinfeld twists similar to the set-theoretic ones,
subject to certain extra constraints dictated by the q-deformation. These
findings greatly generalise recent relevant results on set theoretic solutions
and their q-deformed analogues.


title:
New constraints on axion-gauge field dynamics during inflation from
  Planck and BICEP/Keck data sets

abstract:
  We present new constraints on spectator axion-${\rm U}(1)$ gauge field
interactions during inflation using the latest Planck ($PR4$) and BICEP/Keck
2018 data releases. This model can source tensor perturbations from amplified
gauge field fluctuations, driven by an axion rolling for a few e-folds during
inflation. The gravitational waves sourced in this way have a strongly
scale-dependent (and chiral) spectrum, with potentially visible contributions
to large/intermediate scale $B$-modes of the CMB. We first derive theoretical
bounds on the model imposing validity of the perturbative regime and negligible
backreaction of the gauge field on the background dynamics. Then, we determine
bounds from current CMB observations, adopting a frequentist profile likelihood
approach. We study the behaviour of constraints for typical choices of the
model's parameters, analyzing the impact of different dataset combinations. We
find that observational bounds are competitive with theoretical ones and
together they exclude a significant portion of the model's parameter space. We
argue that the parameter space still remains large and interesting for future
CMB experiments targeting large/intermediate scales $B$-modes.


title:
Multi-cavity gravito-acoustic modes in stars: A general analytical
  resonance condition

abstract:
  Asteroseismology has proven to be a powerful method for probing stellar
interiors. Analytical descriptions of the global oscillation modes, in
combination with pulsation codes, have provided valuable help in processing and
interpreting the large amount of seismic data collected by the CoRoT, $Kepler$,
and TESS missions. These prior results have paved the way to more in-depth
analyses of the oscillation spectra of stars, which requires innovative
theoretical descriptions of stellar oscillations. In this paper, we aim to
analytically express the resonance condition of the spheroidal adiabatic
oscillation modes in a very general way, applicable at different evolutionary
stages. In the present formulation, a star is represented as an acoustic
interferometer composed of a multitude of resonant cavities where waves can
propagate and the short-wavelength JWKB approximation is met. Each cavity is
separated from the adjacent ones by barriers, which corresponds to regions
either where waves are evanescent or where the JWKB approximation fails. The
stationary modes are computed using two different physical representations: 1)
the infinite-time reflections picture 2) the linear boundary value problem
picture. Both provide the same resonance condition, which ultimately turns out
to depend on a number of parameters: the reflection and transmission phase lags
introduced by each barrier, the coupling factor associated with each barrier,
and the wave number integral over each resonant cavity. Using such a
formulation, we can retrieve the usual forms derived in previous works (e.g.,
mixed modes with two or three cavities, low- and large-amplitude glitches).
This resonance condition provides a new tool that is useful in predicting the
stellar oscillation spectra and interpret seismic observations at different
evolutionary stages. Practical applications are left to future work.


title: Rota-Baxter Lie $2$-algebras

abstract:
  In this paper, we introduce the notion of Rota-Baxter Lie $2$-algebras, which
is a categorification of Rota-Baxter Lie algebras. We prove that the category
of Rota-Baxter Lie $2$-algebras and the category of $2$-term Rota-Baxter
$L_\infty$-algebras are equivalent. We introduce the notion of a crossed module
of Rota-Baxter Lie algebras and show that there is a one-to-one correspondence
between strict $2$-term Rota-Baxter $L_\infty$-algebras and crossed modules of
Rota-Baxter Lie algebras. We give the construction of crossed modules of Lie
algebras from crossed modules of Rota-Baxter Lie algebras.


title: Weakness Makes Muller Delay Games Hard

abstract:
  We show that solving delay games with winning conditions given by
deterministic and nondeterministic weak Muller automata is 2EXPTIME-complete
respectively 3EXPTIME-complete. Furthermore, doubly and triply exponential
lookahead is necessary and sufficient to win such games. These results are the
first that show that the succinctness of the automata types used to specify the
winning conditions has an influence on the complexity of these problems.


title: Depth-SIMS: Semi-Parametric Image and Depth Synthesis

abstract:
  In this paper we present a compositing image synthesis method that generates
RGB canvases with well aligned segmentation maps and sparse depth maps, coupled
with an in-painting network that transforms the RGB canvases into high quality
RGB images and the sparse depth maps into pixel-wise dense depth maps. We
benchmark our method in terms of structural alignment and image quality,
showing an increase in mIoU over SOTA by 3.7 percentage points and a highly
competitive FID. Furthermore, we analyse the quality of the generated data as
training data for semantic segmentation and depth completion, and show that our
approach is more suited for this purpose than other methods.


title: Geodetic convexity and Kneser graphs

abstract:
  The {\em Kneser graph} $K(2n+k,n)$, for positive integers $n$ and $k$, is the
graph $G=(V,E)$ such that $V=\{S\subseteq\{1,\ldots,2n+k\} : |S|=n\}$ and there
is an edge $uv\in E$ whenever $u\cap v=\emptyset$. Kneser graphs have a nice
combinatorial structure, and many parameters have been determined for them,
such as the diameter, the chromatic number, the independence number, and,
recently, the hull number (in the context of $P_3$-convexity). However, the
determination of geodetic convexity parameters in Kneser graphs still remained
open. In this work, we investigate both the geodetic number and the geodetic
hull number of Kneser graphs. We give upper bounds and determine the exact
value of these parameters for Kneser graphs of diameter two (which form a
nontrivial subfamily). We prove that the geodetic hull number of a Kneser graph
of diameter two is two, except for $K(5,2)$, $K(6,2)$, and $K(8,2)$, which have
geodetic hull number three. We also contribute to the knowledge on Kneser
graphs by presenting a characterization of endpoints of diametral paths in
$K(2n+k,n)$, used as a tool for obtaining some of the main results in this
work.


title:
On convergence of neural network methods for solving elliptic interface
  problems

abstract:
  With the remarkable empirical success of neural networks across diverse
scientific disciplines, rigorous error and convergence analysis are also being
developed and enriched. However, it still seems little theoretical work on
neural networks in solving interface problems. In this paper, we perform a
convergence analysis of neural network-based methods for solving second-order
elliptic interface problems. Specifically, we consider a neural network method
combined with domain decomposition technologies and gradient-enhanced
strategies. It is shown that the neural network sequence obtained by minimizing
a Lipschitz regularized loss function converges to the unique solution to the
interface problem in $H^2$ as the number of samples increases. Numerical
experiments are provided to demonstrate our analysis.


title: On dense intermingling of exact overlaps and the open set condition

abstract:
  We prove that certain families of homogenous affine iterated function systems
in $\mathbb{R}^d$ have the property that the open set condition and the
existence of exact overlaps both occur densely in the space of translation
parameters. These examples demonstrate that in the theorems of Falconer and
Jordan-Pollicott-Simon on the almost sure dimensions of self-affine sets and
measures, the set of exceptional translation parameters can be a dense set. The
proof combines results from the literature on self-affine tilings of
$\mathbb{R}^d$ with an adaptation of a classic argument of Erd\H{o}s on the
singularity of certain Bernoulli convolutions. Our result encompasses a
one-dimensional example due to Kenyon which arises as a special case.


title: Dark current in monolithic extended-SWIR GeSn PIN photodetectors

abstract:
  The monolithic integration of extended short-wave infrared (e-SWIR)
photodetectors (PDs) on silicon is highly sought-after to implement
manufacturable, cost-effective sensing and imaging technologies. With this
perspective, GeSn PIN PDs have been the subject of extensive investigations
because of their bandgap tunability and silicon compatibility. However, due to
growth defects, these PDs suffer a relatively high dark current density as
compared to commercial III-V PDs. Herein, we elucidate the mechanisms governing
the dark current in $2.6 \, \mu$m GeSn PDs at a Sn content of $10$ at.%. It was
found that in the temperature range of $293 \, $K -- $363 \,$K and at low bias,
the diffusion and Shockley-Read-Hall (SRH) leakage mechanisms dominate the dark
current in small diameter ($20 \, \mu$m) devices, while combined SRH and trap
assisted tunneling (TAT) leakage mechanisms are prominent in larger diameter
($160 \, \mu$m) devices. However, at high reverse bias, TAT leakage mechanism
becomes dominant regardless of the operating temperature and device size. The
effective non-radiative carrier lifetime in these devices was found to reach
$\sim 300$ -- $400$ ps at low bias. Owing to TAT leakage current, however, this
lifetime reduces progressively as the bias increases.


title: Coarse-grained dynamics of ac-driven two-state systems

abstract:
  Magnus expansion is used to identify effective Hamiltonians describing the
coarse-grained dynamics of more complex problems. Here, we apply this method to
a two-level system driven by an and AC field. We derive Stark and Bloch-Siegert
shifs of both diagonal and off-diagonal entries of the Hamiltonian as a result
of coarse-graining only.


title: Gaka-chu: a self-employed autonomous robot artist

abstract:
  The physical autonomy of robots is well understood both theoretically and
practically. By contrast, there is almost no research exploring their potential
economic autonomy. In this paper, we present the first economically autonomous
robot -- a robot able to produce marketable goods while having full control
over the use of its generated income. Gaka-chu ("painter" in Japanese) is a
6-axis robot arm that creates artistic paintings of Japanese kanjis from an
autoselected keyword. By using a blockchain-based smart contract, Gaka-chu can
autonomously list a painting it made for sale in an online auction. In this
transaction, the robot interacts with the human bidders as a peer not as a
tool. Using the blockchain-based smart contract, Gaka-chu can then use its
income from selling paintings to replenish its resources by autonomously
ordering materials from an online art shop, and maintain its activity by
remunerating a human assistant for manual tasks such as placing canvases. We
built the Gaka-chu prototype with an Ethereum-based smart contract and ran a
6-month long experiment, during which the robot painted and sold four paintings
one-by-one, simultaneously using its income to purchase art supplies, human
assistance, and paid initial investors. In this work, we present the results of
the experiments conducted and discuss the implications of economically
autonomous robots.


title:
The two hot corinos of the SVS13-A protostellar binary system:
  counterposed siblings

abstract:
  We present ALMA high-angular resolution ($\sim$ 50 au) observations of the
Class I binary system SVS13-A. We report images of SVS13-A in numerous
interstellar complex organic molecules: CH$_{\rm 3}$OH, $^{13}$CH$_{\rm 3}$OH,
CH$_{\rm 3}$CHO, CH$_{\rm 3}$OCH$_{\rm 3}$, and NH$_{\rm 2}$CHO. Two hot
corinos at different velocities are imaged in VLA4A (V$_{sys}$= +7.7 km
s$^{-1}$) and VLA4B (V$_{sys}$= +8.5 km s$^{-1}$). From a non-LTE analysis of
methanol lines we derive a gas density of 3 $\times$ 10$^8$ cm$^{-3}$, and gas
temperatures of 140 K and 170 K for VLA4A and VLA4B, respectively. For the
other species the column densities are derived from a LTE analysis. Formamide,
which is the only N-bearing species detected in our observations, is more
prominent around VLA4A, while dimethyl ether, methanol and acetaldehyde are
associated with both VLA4A and VLA4B. We derive in the two hot corinos
abundance ratios of $\sim$ 1 for CH$_{\rm 3}$OH, $^{13}$CH$_{\rm 3}$OH, and
CH$_{\rm 3}$OCH$_{\rm 3}$, $\sim$ 2 for CH$_{\rm 3}$CHO, and $\sim$ 4 for
NH$_{\rm 2}$CHO. The present dataset supports a chemical segregation between
the different species inside the binary system. The emerging picture is that of
an onion-like structure of the two SVS13-A hot corinos, caused by the different
binding energies of the species, also supported by ad hoc quantum chemistry
calculations. In addition, the comparison between molecular and dust maps
suggests that the interstellar complex organic molecules emission originates
from slow shocks produced by accretion streamers impacting the VLA4A and VLA4B
disks and enriching the gas-phase component.


title: New supersymmetric Janus solutions from N=4 gauged supergravity

abstract:
  We study $N=4$ gauged supergravity with $SO(4)\times SO(4)$ gauge group in
the presence of symplectic deformations and find new classes of Janus solutions
preserving $N=1$ and $N=2$ supersymmetries. The $N=2$ solutions preserve
$SO(2)\times SO(2)\times SO(2)\times SO(2)$ symmetry and interpolate between
$N=4$ supersymmetric $AdS_4$ vacua with $SO(4)\times SO(4)$ symmetry. These
correspond holographically to $N=(2,0)$ two-dimensional conformal defects
within the dual $N=4$ CSM theories with $SO(4)\times SO(4)$ symmetry. The $N=1$
solutions contain two families of Janus configurations, one interpolating
between $N=4$ $AdS_4$ vacua with $SO(4)\times SO(4)$ symmetry and the other
interpolating between $N=4$ $AdS_4$ vacua with $SO(3)\times SO(3) \times SO(3)$
symmetry. These respectively describe $N=(1,0)$ conformal defects in $N=4$ CSM
theories with $SO(4)\times SO(4)$ and $SO(3)\times SO(3) \times SO(3)$
symmetries. The latter give the first example of Janus solutions involving
non-trivial $AdS_4$ vacua in addition to the trivial $SO(4)\times SO(4)$
critical point at the origin of the scalar manifold within the framework of
$N=4$ gauged supergravity.


title:
Some rational homology computations for diffeomorphisms of
  odd-dimensional manifolds

abstract:
  We calculate the rational cohomology of the classifying space of the
diffeomorphism group of the manifolds $U_{g,1}^n:= \#^g(S^n \times
S^{n+1})\setminus \mathrm{int}{D^{2n+1}}$, for large $g$ and $n$, up to
approximately degree $n$. The answer is that it is a free graded commutative
algebra on an appropriate set of Miller--Morita--Mumford classes.
  Our proof goes through the classical three-step procedure: (a) compute the
cohomology of the homotopy automorphisms, (b) use surgery to compare this to
block diffeomorphisms, (c) use pseudoisotopy theory and algebraic $K$-theory to
get at actual diffeomorphism groups.


title:
AugHover-Net: Augmenting Hover-net for Nucleus Segmentation and
  Classification

abstract:
  Nuclei segmentation and classification have been a challenge in digital
pathology due to the specific domain characteristics. First, annotating a
large-scale dataset is quite consuming. It requires specific domain knowledge
and large efforts. Second, some nuclei are clustered together and hard to
segment from each other. Third, the classes are often extremely unbalanced. As
in Lizard, the number of epithelial nuclei is around 67 times larger than the
number of eosinophil nuclei. Fourth, the nuclei often exhibit high inter-class
similarity and intra-class variability. Connective nuclei may look very
different from each other while some of them share a similar shape with the
epithelial ones. Last but not least, pathological patches may have very
different color distributions among different datasets. Thus, a large-scale
generally annotated dataset and a specially-designed algorithm are needed to
solve this problem. The CoNIC challenge aims to promote the automatic
segmentation and classification task and requires researchers to develop
algorithms that perform segmentation, classification, and counting of 6
different types of nuclei with the large-scale annotated dataset: Lizard. Due
to the 60-minute time limit, the algorithm has to be simple and quick. In this
paper, we briefly describe the final method we used in the CoNIC challenge. Our
algorithm is based on Hover-Net and we added several modifications to it to
improve its performance.


title:
The classification of $n$-dimensional nilpotent non-Tortkara
  anticommutative algebras with $(n-4)$-dimensional annihilator

abstract:
  In this paper, we give a complete classification of $n$-dimensional nilpotent
non-Tortkara anticommutative algebras with $(n-4)$-dimensional annihilator over
$\mathbb{C}$.


title:
Scalable multi-agent reinforcement learning for distributed control of
  residential energy flexibility

abstract:
  This paper proposes a novel scalable type of multi-agent reinforcement
learning-based coordination for distributed residential energy. Cooperating
agents learn to control the flexibility offered by electric vehicles, space
heating and flexible loads in a partially observable stochastic environment. In
the standard independent Q-learning approach, the coordination performance of
agents under partial observability drops at scale in stochastic environments.
Here, the novel combination of learning from off-line convex optimisations on
historical data and isolating marginal contributions to total rewards in reward
signals increases stability and performance at scale. Using fixed-size
Q-tables, prosumers are able to assess their marginal impact on total system
objectives without sharing personal data either with each other or with a
central coordinator. Case studies are used to assess the fitness of different
combinations of exploration sources, reward definitions, and multi-agent
learning frameworks. It is demonstrated that the proposed strategies create
value at individual and system levels thanks to reductions in the costs of
energy imports, losses, distribution network congestion, battery depreciation
and greenhouse gas emissions.


title: Non-thermal radio continuum emission from young nearby stars

abstract:
  Young and magnetically active low-mass stars often exhibit non-thermal
coronal radio emission due to the gyration of electrons in their magnetized
chromospheres. This emission is easily detectable at centimeter wavelengths
with the current sensitivity of large radio interferometers like the VLA. With
the aim of identifying nearby stars adequate for future accurate radio
astrometric monitoring using Very Long Baseline Interferometry (VLBI), we have
used the VLA in its B\, configuration to search for radio emission at
\mbox{$\nu \simeq 6$\,GHz} \mbox{($\lambda \simeq 5$\,cm)} toward a sample of
170 nearby ($<$130\,pc) mostly young (5\,--\,500\,Myr) stars of spectral types
between F4 and M2. At our mean $3\sigma$ detection limit of
$\simeq$\,50\,$\mu$Jy, we identify 31 young stars with coronal radio emission
(an 18\% system detection rate) and more than 600 background (most likely
extra-galactic) sources. Among the targeted stars, we find a significant
decline of the detection rate with age from 56$\pm$20\% for stars with ages
$\le10$\,Myr to 10$\pm$3\% for stars with ages 100\,--\,200\,Myr. No star older
than 200\,Myr was detected. The detection rate also declines with $T_{\rm eff}$
from 36$\pm$10\% for stars with $T_{\rm eff}<4000$\,K to 13$\pm$3\% for earlier
spectral types with $T_{\rm eff}>5000$\,K. The binarity fraction among the
radio-bright stars is at least twice as high as among the radio-quiet stars.
The radio-bright nearby young stars identified here provide an interesting
sample for future astrometric studies using VLBI arrays aimed at searching for
hitherto unknown tight binary components or even exoplanets.


title:
Twisted Alexander invariants for the braid group associated with the
  Tong-Yang-Ma representation

abstract:
  In this paper, we compute the twisted Alexander invariant of the braid group
associated with the Tong-Yang-Ma representation.


title:
A Deep Learning Framework for Nuclear Segmentation and Classification in
  Histopathological Images

abstract:
  Nucleus segmentation and classification are the prerequisites in the workflow
of digital pathology processing. However, it is very challenging due to its
high-level heterogeneity and wide variations. This work proposes a deep neural
network to simultaneously achieve nuclear classification and segmentation,
which is designed using a unified framework with three different branches,
including segmentation, HoVer mapping, and classification. The segmentation
branch aims to generate the boundaries of each nucleus. The HoVer branch
calculates the horizontal and vertical distances of nuclear pixels to their
centres of mass. The nuclear classification branch is used to distinguish the
class of pixels inside the nucleus obtained from segmentation.


title:
Seniority and $(\frac{9}{2})^n$ configurations in neutron-rich Nickel
  isotopes

abstract:
  Excited states in low-energy spectra in $^{70-76}$Ni are considered. To this
end, pairing forces in form of surface delta interaction are employed to
account for formation of the ground state multiplet with seniority $\nu = 2$
states. The multiplet splitting is described with mass relations of masses of
neighbouring nuclei. Subsequently, seniority model is used to reproduce or
predict the states $\nu = 3$ in odd-even isotopes and $\nu = 4$ in even-even
isotopes. Correct account of $2_1^+$ state should allow for description of
reversed order of states $J = 4$ with $\nu = 2$ and $\nu = 4$ observed in
experiment. The results obtained are compared with the structure of similar
multiplets in $N=50$ isotones.


title: Water and Sediment Analyse Using Predictive Models

abstract:
  The increasing prevalence of marine pollution during the past few decades
motivated recent research to help ease the situation. Typical water quality
assessment requires continuous monitoring of water and sediments at remote
locations with labour intensive laboratory tests to determine the degree of
pollution. We propose an automated framework where we formalise a predictive
model using Machine Learning to infer the water quality and level of pollution
using collected water and sediments samples. One commonly encountered
difficulty performing statistical analysis with water and sediment is the
limited amount of data samples and incomplete dataset due to the sparsity of
sample collection location. To this end, we performed extensive investigation
on various data imputation methods' performance in water and sediment datasets
with various data missing rates. Empirically, we show that our best model
archives an accuracy of 75% after accounting for 57% of missing data.
Experimentally, we show that our model would assist in assessing water quality
screening based on possibly incomplete real-world data.


title: Multivariate Time Series Forecasting with Latent Graph Inference

abstract:
  This paper introduces a new approach for Multivariate Time Series forecasting
that jointly infers and leverages relations among time series. Its modularity
allows it to be integrated with current univariate methods. Our approach allows
to trade-off accuracy and computational efficiency gradually via offering on
one extreme inference of a potentially fully-connected graph or on another
extreme a bipartite graph. In the potentially fully-connected case we consider
all pair-wise interactions among time-series which yields the best forecasting
accuracy. Conversely, the bipartite case leverages the dependency structure by
inter-communicating the N time series through a small set of K auxiliary nodes
that we introduce. This reduces the time and memory complexity w.r.t. previous
graph inference methods from O(N^2) to O(NK) with a small trade-off in
accuracy. We demonstrate the effectiveness of our model in a variety of
datasets where both of its variants perform better or very competitively to
previous graph inference methods in terms of forecasting accuracy and time
efficiency.


title: Multiplicity algebras for rank 2 bundles on curves of small genus

abstract:
  The main aim of the paper is to demonstrate continuous families of
isomorphism classes of multiplicity algebras associated to very stable rank 2
bundles on a curve of genus 2 and 3. These are algebras defined by Hausel and
the author associated to fixed points of the C* action on the moduli space of
Higgs bundles, and the examples are contrasted with other fixed points where
they are isomorphic to the cohomology of a homogeneous space. The construction
involves some classical geometry relating curves of genus 2,3 and 5.


title:
Zero-One Laws and Almost Sure Valuations of First-Order Logic in
  Semiring Semantics

abstract:
  Semiring semantics evaluates logical statements by values in some commutative
semiring K. Random semiring interpretations, induced by a probability
distribution on K, generalise random structures, and we investigate here the
question of how classical results on first-order logic on random structures,
most importantly the 0-1 laws of Glebskii et al. and Fagin, generalise to
semiring semantics. For positive semirings, the classical 0-1 law implies that
every first-order sentence is, asymptotically, either almost surely evaluated
to 0 by random semiring interpretations, or almost surely takes only values
different from 0. However, by means of a more sophisticated analysis, based on
appropriate extension properties and on algebraic representations of
first-order formulae, we can prove much stronger results.
  For many semirings K, the first-order sentences can be partitioned into
classes F(j) for all semiring values j in K, such that every sentence in F(j)
evaluates almost surely to j under random semiring interpretations. Further,
for finite or infinite lattice semirings, this partition actually collapses to
just three classes F(0), F(1), and F(e), of sentences that, respectively,
almost surely evaluate to 0, 1, and to the smallest non-zero value e. The
problem of computing the almost sure valuation of a first-order sentence on
finite lattice semirings is PSPACE-complete.
  An important semiring where the analysis is somewhat different is the
semiring of natural numbers. Here, both addition and multiplication are
increasing with respect to the natural semiring order and the classes F(j), for
natural numbers j, no longer cover all FO-sentences, but have to be extended by
the class of sentences that almost surely evaluate to unboundedly large values.


title:
Towards Managing Industrial Robot Fleets with Hyperledger Fabric
  Blockchain and ROS 2

abstract:
  Trust is increasingly becoming a key consideration in the design of
autonomous robotic systems. In industrial applications, security and trust in
the system are requirements for widespread adoption. Blockchain technologies
have emerged as a potential solution to address identity management and secure
data aggregation and control. However, the vast majority of works to date
utilize Ethereum and smart contracts that are not scalable or well suited for
industrial applications. This paper presents what is, to the best of our
knowledge, the first integration of ROS 2 with the Hyperledger Fabric
blockchain. With a framework that leverages Fabric smart contracts and ROS 2
through a Go application, we delve into the potential of using blockchain for
controlling robots, and gathering and processing their data. We demonstrate the
applicability of the proposed framework to an inventory management use-case
where different robots are used to detect objects of interest in a given area.
Designed to meet the requirements of distributed robotic systems, we show that
the performance of the robots is not impacted significantly by the blockchain
layer. At the same time, we provide examples for developing other applications
that integrate Fabric smart contracts with ROS 2. Our results pave the way for
further adoption of blockchain technologies in autonomous robotic systems for
building trustable data sharing.


title: On finite groups with given $IC\Phi$-subgroups

abstract:
  A subgroup $H$ of a group $G$ is said to be an $IC\Phi$-subgroup of $G$ if $H
\cap [H,G] \le \Phi(H)$. We analyze the structure of a finite group $G$ under
the assumption that some given subgroups of $G$ are $IC\Phi$-subgroups of $G$.
A new characterization of finite abelian groups and some new criteria for
$2$-nilpotence and nilpotence of finite groups will be obtained. Moreover, we
will obtain two criteria for a finite group to lie in a given solvably
saturated formation containing the class of finite supersolvable groups.


title:
Attention-based Region of Interest (ROI) Detection for Speech Emotion
  Recognition

abstract:
  Automatic emotion recognition for real-life appli-cations is a challenging
task. Human emotion expressions aresubtle, and can be conveyed by a combination
of several emo-tions. In most existing emotion recognition studies, each
audioutterance/video clip is labelled/classified in its entirety.
However,utterance/clip-level labelling and classification can be too coarseto
capture the subtle intra-utterance/clip temporal dynamics. Forexample, an
utterance/video clip usually contains only a fewemotion-salient regions and
many emotionless regions. In thisstudy, we propose to use attention mechanism
in deep recurrentneural networks to detection the Regions-of-Interest (ROI)
thatare more emotionally salient in human emotional speech/video,and further
estimate the temporal emotion dynamics by aggre-gating those emotionally
salient regions-of-interest. We comparethe ROI from audio and video and analyse
them. We comparethe performance of the proposed attention networks with
thestate-of-the-art LSTM models on multi-class classification task
ofrecognizing six basic human emotions, and the proposed attentionmodels
exhibit significantly better performance. Furthermore, theattention weight
distribution can be used to interpret how anutterance can be expressed as a
mixture of possible emotions.


title:
Synthetic Defect Generation for Display Front-of-Screen Quality
  Inspection: A Survey

abstract:
  Display front-of-screen (FOS) quality inspection is essential for the mass
production of displays in the manufacturing process. However, the severe
imbalanced data, especially the limited number of defect samples, has been a
long-standing problem that hinders the successful application of deep learning
algorithms. Synthetic defect data generation can help address this issue. This
paper reviews the state-of-the-art synthetic data generation methods and the
evaluation metrics that can potentially be applied to display FOS quality
inspection tasks.


title: Academic Support Network Reflects Doctoral Experience and Productivity

abstract:
  Current practices of quantifying performance by productivity leads serious
concerns for psychological well-being of doctoral students and influence of
research environment is often neglected in research evaluations.
Acknowledgements in dissertations reflect the student experience and provide an
opportunity to thank the people who support them. We conduct textual analysis
of acknowledgments to build the "academic support network," uncovering five
distinct communities: Academic, Administration, Family, Friends & Colleagues,
and Spiritual; each of which is acknowledged differently by genders and
disciplines. Female students mention fewer people from each community except
for their families and total number of people mentioned in acknowledgements
allows disciplines to be categorized as either individual science or team
science. We also show that number of people mentioned from academic community
is positively correlated with productivity and institutional rankings are found
to be correlated with productivity and size of academic support networks but
show no effect on students' sentiment on acknowledgements. Our results indicate
the importance of academic support networks by explaining how they differ and
how they influence productivity.


title: What Did You Say? Task-Oriented Dialog Datasets Are Not Conversational!?

abstract:
  High-quality datasets for task-oriented dialog are crucial for the
development of virtual assistants. Yet three of the most relevant large scale
dialog datasets suffer from one common flaw: the dialog state update can be
tracked, to a great extent, by a model that only considers the current user
utterance, ignoring the dialog history. In this work, we outline a taxonomy of
conversational and contextual effects, which we use to examine MultiWOZ, SGD
and SMCalFlow, among the most recent and widely used task-oriented dialog
datasets. We analyze the datasets in a model-independent fashion and
corroborate these findings experimentally using a strong text-to-text baseline
(T5). We find that less than 4% of MultiWOZ's turns and 10% of SGD's turns are
conversational, while SMCalFlow is not conversational at all in its current
release: its dialog state tracking task can be reduced to single exchange
semantic parsing. We conclude by outlining desiderata for truly conversational
dialog datasets.


title: Learning Solution Manifolds for Control Problems via Energy Minimization

abstract:
  A variety of control tasks such as inverse kinematics (IK), trajectory
optimization (TO), and model predictive control (MPC) are commonly formulated
as energy minimization problems. Numerical solutions to such problems are
well-established. However, these are often too slow to be used directly in
real-time applications. The alternative is to learn solution manifolds for
control problems in an offline stage. Although this distillation process can be
trivially formulated as a behavioral cloning (BC) problem in an imitation
learning setting, our experiments highlight a number of significant
shortcomings arising due to incompatible local minima, interpolation artifacts,
and insufficient coverage of the state space. In this paper, we propose an
alternative to BC that is efficient and numerically robust. We formulate the
learning of solution manifolds as a minimization of the energy terms of a
control objective integrated over the space of problems of interest. We
minimize this energy integral with a novel method that combines Monte
Carlo-inspired adaptive sampling strategies with the derivatives used to solve
individual instances of the control task. We evaluate the performance of our
formulation on a series of robotic control problems of increasing complexity,
and we highlight its benefits through comparisons against traditional methods
such as behavioral cloning and Dataset aggregation (Dagger).


title: A Tracial Schwarz inequality and a Theorem of Hiai and Petz

abstract:
  Let $\phi$ be a linear map from the $n\times n$ matrices ${\mathcal M}_m$ to
the $m\times m$ matrices ${\mathcal M}_m$. It is known that $\phi$ is
$2$-positive if and only if for all $X\in {\mathcal M}_m$ and all strictly
positive $C\in {\mathcal M}_m$, $\phi(X^*C^{-1}X) \geq
\phi(X)^*\phi(C)^{-1}\phi(X)$. This inequality is not generally true if $\phi$
is merely a Schwarz map. We show here that the corresponding tracial inequality
${\rm Tr}[\phi(X^*C^{-1}X)] \geq {\rm Tr}[\phi(X)^*\phi(C)^{-1}\phi(X)]$ holds
for a wider class of positive maps, including duals of unital Schwarz maps. We
apply this to show that a theorem of Hiai and Petz that was proved for
completely positive unital $\phi$, and easily extends to $2$-positive unital
maps, is actually true whenever $\phi$ is a unital Schwarz map.


title:
Large-$S$ and tensor-network methods for strongly-interacting
  topological insulators

abstract:
  The study of correlation effects in topological phases of matter can benefit
from a multidisciplinary approach that combines techniques drawn from condensed
matter, high-energy physics and quantum information science. In this work, we
exploit these connections to study the strongly-interacting limit of certain
lattice Hubbard models of topological insulators, which map onto four-Fermi
quantum field theories with a Wilson-type discretization, and have been
recently shown to be at reach of cold-atom quantum simulators based on
synthetic spin-orbit coupling. We combine large-S and tensor-network techniques
to explore the possible spontaneous symmetry-breaking phases that appear when
the interactions of the topological insulators are sufficiently large. In
particular, we show that varying the Wilson parameter $r$ of the lattice
discretizations leads to a novel Heisenberg-Ising compass model with critical
lines that flow with the value of $r$.


title: TPC development by the LCTPC collaboration for the ILD detector at ILC

abstract:
  The International Large Detector (ILD) at the International Linear Collider
(ILC) requires significantly improved subdetector systems to comply with the
envisioned performance. Its central tracking detector is a Time Projection
Chamber (TPC) which is equipped with Micropattern Gaseous Detector (MPGD)
readout modules. The LCTPC collaboration was founded in 2007 to design such a
MPGD-based TPC and to build and test demonstrator modules. For this an
experimental setup at DESY, Hamburg, was built, where four different readout
concepts have been studied in many successful test beam campaigns since 2010.
It could be demonstrated that the required transverse and longitudinal space
point resolutions can be reached. Also, a new gating concept was developed that
can effectively eliminate the ion backflow, while marginally degrading the
performance of the detector.


title: Deep Neural Decision Forest for Acoustic Scene Classification

abstract:
  Acoustic scene classification (ASC) aims to classify an audio clip based on
the characteristic of the recording environment. In this regard, deep learning
based approaches have emerged as a useful tool for ASC problems. Conventional
approaches to improving the classification accuracy include integrating
auxiliary methods such as attention mechanism, pre-trained models and ensemble
multiple sub-networks. However, due to the complexity of audio clips captured
from different environments, it is difficult to distinguish their categories
without using any auxiliary methods for existing deep learning models using
only a single classifier. In this paper, we propose a novel approach for ASC
using deep neural decision forest (DNDF). DNDF combines a fixed number of
convolutional layers and a decision forest as the final classifier. The
decision forest consists of a fixed number of decision tree classifiers, which
have been shown to offer better classification performance than a single
classifier in some datasets. In particular, the decision forest differs
substantially from traditional random forests as it is stochastic,
differentiable, and capable of using the back-propagation to update and learn
feature representations in neural network. Experimental results on the
DCASE2019 and ESC-50 datasets demonstrate that our proposed DNDF method
improves the ASC performance in terms of classification accuracy and shows
competitive performance as compared with state-of-the-art baselines.


title:
Multilevel Monte Carlo with Surrogate Models for Resource Adequacy
  Assessment

abstract:
  Monte Carlo simulation is often used for the reliability assessment of power
systems, but it converges slowly when the system is complex. Multilevel Monte
Carlo (MLMC) can be applied to speed up computation without compromises on
model complexity and accuracy that are limiting real-world effectiveness. In
MLMC, models with different complexity and speed are combined, and having
access to fast approximate models is essential for achieving high speedups.
This paper demonstrates how machine-learned surrogate models are able to fulfil
this role without excessive manual tuning of models. Different strategies for
constructing and training surrogate models are discussed. A resource adequacy
case study based on the Great Britain system with storage units is used to
demonstrate the effectiveness of the proposed approach, and the sensitivity to
surrogate model accuracy. The high accuracy and inference speed of
machine-learned surrogates result in very large speedups, compared to using
MLMC with hand-built models.