title: Clustering Enabled Few-Shot Load Forecasting

abstract:
  While the advanced machine learning algorithms are effective in load
forecasting, they often suffer from low data utilization, and hence their
superior performance relies on massive datasets. This motivates us to design
machine learning algorithms with improved data utilization. Specifically, we
consider the load forecasting for a new user in the system by observing only
few shots (data points) of its energy consumption. This task is challenging
since the limited samples are insufficient to exploit the temporal
characteristics, essential for load forecasting. Nonetheless, we notice that
there are not too many temporal characteristics for residential loads due to
the limited kinds of human lifestyle. Hence, we propose to utilize the
historical load profile data from existing users to conduct effective
clustering, which mitigates the challenges brought by the limited samples.
Specifically, we first design a feature extraction clustering method for
categorizing historical data. Then, inheriting the prior knowledge from the
clustering results, we propose a two-phase Long Short Term Memory (LSTM) model
to conduct load forecasting for new users. The proposed method outperforms the
traditional LSTM model, especially when the training sample size fails to cover
a whole period (i.e., 24 hours in our task). Extensive case studies on two
real-world datasets and one synthetic dataset verify the effectiveness and
efficiency of our method.


title: Meta Knowledge Distillation

abstract:
  Recent studies pointed out that knowledge distillation (KD) suffers from two
degradation problems, the teacher-student gap and the incompatibility with
strong data augmentations, making it not applicable to training
state-of-the-art models, which are trained with advanced augmentations.
However, we observe that a key factor, i.e., the temperatures in the softmax
functions for generating probabilities of both the teacher and student models,
was mostly overlooked in previous methods. With properly tuned temperatures,
such degradation problems of KD can be much mitigated. However, instead of
relying on a naive grid search, which shows poor transferability, we propose
Meta Knowledge Distillation (MKD) to meta-learn the distillation with learnable
meta temperature parameters. The meta parameters are adaptively adjusted during
training according to the gradients of the learning objective. We validate that
MKD is robust to different dataset scales, different teacher/student
architectures, and different types of data augmentation. With MKD, we achieve
the best performance with popular ViT architectures among compared methods that
use only ImageNet-1K as training data, ranging from tiny to large models. With
ViT-L, we achieve 86.5% with 600 epochs of training, 0.6% better than MAE that
trains for 1,650 epochs.


title:
A Survey of Approaches for Event Sequence Analysis and Visualization
  using the ESeVis Framework

abstract:
  Event sequence data is increasingly available. Many business operations are
supported by information systems that record transactions, events, state
changes, message exchanges, and so forth. This observation is equally valid for
various industries, including production, logistics, healthcare, financial
services, education, to name but a few. The variety of application areas
explains that techniques for event sequence data analysis have been developed
rather independently in different fields of computer science. Most prominent
are contributions from information visualization and from process mining. So
far, the contributions from these two fields have neither been compared nor
have they been mapped to an integrated framework. In this paper, we develop the
Event Sequence Visualization framework (ESeVis) that gives due credit to the
traditions of both fields. Our mapping study provides an integrated perspective
on both fields and identifies potential for synergies for future research.


title:
Astrometric star-cluster membership probability: application to the case
  of M 37 with Gaia EDR3

abstract:
  In this work, starting from the well-accepted relations in literature, we
introduce a new formalism to compute the astrometric membership probabilities
for sources in star clusters, and we provide an application to the case of the
open cluster M 37. The novelty of our approach is a refined -- and
magnitude-dependent -- modelling of the parallax distribution of the field
stars. We employ the here-derived list of members to estimate the cluster's
mean systemic astrometric parameters, which are based on the most recent Gaia's
catalog (EDR3).


title: Photochemistry in hot H2-dominated exoplanet atmospheres

abstract:
  Photochemistry has the potential to substantially impact the atmospheric
composition of exoplanets with consequences on the radiative transfer, thermal
structure and dynamics of the atmospheres, particularly in UV-rich stellar
environments. Here, we present the results of a first laboratory experimental
simulation of photochemistry in carbon-rich exoplanet atmospheres at elevated
temperatures. Evolution of gas-phase molecular composition was quantitatively
monitored with infrared spectroscopy and mass spectrometry. We found that H2/CO
gas compositions can change significantly from thermal equilibria compositions
when irradiated with Lyman-alpha photons at temperatures ranging from 600 K to
1500 K. Carbon dioxide and water were found to be the main products caused by
photolysis, while formation of methane was also observed to a lesser extent. We
find that photochemistry efficiency is strongly correlated with increasing
temperature. Our finding that water is efficiently produced by photochemistry
in a super Solar C/O=1 environment, representing C enhancement relative to
solar values C/O ratio = 0.54, has significant implications for the
interpretation of many exoplanet transmission spectra. We also find the
formation of an organic solid condensate at 1500 K and under Lyman-alpha
UV-radiation, confirming the possibility of forming photochemical hazes in
hot-Jupiter exoplanet atmospheres with an enhanced C/O ratio compared to Solar.


title: A case for transparency in principal-agent relationships

abstract:
  When is transparency optimal in principal-agent relationships? We consider
the following setting. The principal has private, payoff-relevant information
about the potential of the project. She can share this information with the
agent and can commit to any information structure. Positive news motivate the
agent, while bad news depress effort. Under rather mild and natural
restrictions on the utility functions of the parties, we obtain interpretable
and easily verifiable sufficient conditions for the optimality of full
disclosure. We also show that full disclosure is optimal under some modeling
assumptions commonly used in applied principal-agent papers.


title:
A new and homogeneous metallicity scale for Galactic classical Cepheids
  II. The abundance of iron and alpha elements

abstract:
  Classical Cepheids are the most popular distance indicators and tracers of
young stellar populations. The key advantage is that they are bright and they
can be easily identified in Local Group and Local Volume galaxies. Their
evolutionary and pulsation properties depend on their chemical abundances. The
main aim of this investigation is to perform a new and accurate abundance
analysis of two tens of calibrating Galactic Cepheids using high spectral
resolution (R$\sim$40,000-115,000) and high S/N spectra ($\sim$400) covering
the entire pulsation cycle. We focus our attention on possible systematics
affecting the estimate of atmospheric parameters and elemental abundances along
the pulsation cycle. We cleaned the line list by using atomic transition
parameters based on laboratory measurements and by removing lines that are
either blended or display abundance variations along the pulsation cycle. The
spectroscopic approach that we developed brings forward small dispersions in
the variation of the atmospheric parameters ($\sigma$($T_{\rm eff}$)$\sim$50 K,
$\sigma$($\log{g}$)$\sim$0.2 dex, and $\sigma$($\xi$)$\sim$0.2 km/s) and in the
abundance of both iron ($\lesssim$ 0.05 dex) and alpha elements ($\lesssim$0.10
dex) over the entire pulsation cycle. We also provide new and accurate
effective temperature templates by splitting the calibrating Cepheids into four
different period bins, ranging from short to long periods. For each period bin,
we performed an analytical fit with Fourier series providing $\theta =
5040/{T_{\rm eff}}$ as a function of the pulsation phase. The current findings
are a good viaticum to trace the chemical enrichment of the Galactic thin disk
by using classical Cepheids and a fundamental stepping stone for further
investigations into the more metal-poor regime typical of Magellanic Cepheids.


title:
Turn Tree into Graph: Automatic Code Review via Simplified AST Driven
  Graph Convolutional Network

abstract:
  Automatic code review (ACR), which can relieve the costs of manual
inspection, is an indispensable and essential task in software engineering. To
deal with ACR, existing work is to serialize the abstract syntax tree (AST).
However, making sense of the whole AST with sequence encoding approach is a
daunting task, mostly due to some redundant nodes in AST hinder the
transmission of node information. Not to mention that the serialized
representation is inadequate to grasp the information of tree structure in AST.
In this paper, we first present a new large-scale Apache Automatic Code Review
(AACR) dataset for ACR task since there is still no publicly available dataset
in this task. The release of this dataset would push forward the research in
this field. Based on it, we propose a novel Simplified AST based Graph
Convolutional Network (SimAST-GCN) to deal with ACR task. Concretely, to
improve the efficiency of node information dissemination, we first simplify the
AST of code by deleting the redundant nodes that do not contain connection
attributes, and thus deriving a Simplified AST. Then, we construct a relation
graph for each code based on the Simplified AST to properly embody the
relations among code fragments of the tree structure into the graph.
Subsequently, in the light of the merit of graph structure, we explore a graph
convolution networks architecture that follows an attention mechanism to
leverage the crucial implications of code fragments to derive code
representations. Finally, we exploit a simple but effective subtraction
operation in the representations between the original and revised code,
enabling the revised difference to be preferably learned for deciding the
results of ACR. Experimental results on the AACR dataset illustrate that our
proposed model outperforms the state-of-the-art methods.


title:
Slip and Stress From Low Strain-Rate Nonequilibrium Molecular Dynamics:
  The Transient-Time Correlation Function Technique

abstract:
  We derive the transient-time correlation function (TTCF) expression for the
computation of phase variables of inhomogenous confined atomistic fluids
undergoing boundary-driven planar shear (Couette) flow at constant pressure.
Using nonequilibrium molecular dynamics simulations, we then apply the TTCF
formalism to the computation of the shear stress and the slip velocity for
atomistic fluids at realistic low shear rates, in systems under constant
pressure and constant volume. We show that, compared to direct averaging of
multiple trajectories, the TTCF method dramatically improves the accuracy of
the results at low shear rates, and that it is suitable to investigate the
tribology and rheology of atomistically detailed confined fluids at realistic
flow rates.


title:
NORM: An FPGA-based Non-volatile Memory Emulation Framework for
  Intermittent Computing

abstract:
  Intermittent computing systems operate by relying only on harvested energy
accumulated in their tiny energy reservoirs, typically capacitors. An
intermittent device dies due to a power failure when there is no energy in its
capacitor and boots again when the harvested energy is sufficient to power its
hardware components. Power failures prevent the forward progress of computation
due to the frequent loss of computational state. To remedy this problem,
intermittent computing systems comprise built-in fast non-volatile memories
with high write endurance to store information that persists despite frequent
power failures. However, the lack of design tools makes fast-prototyping these
systems difficult. Even though FPGAs are common platforms for fast prototyping
and behavioral verification of continuously-powered architectures, they do not
target prototyping intermittent computing systems. This article introduces a
new FPGA-based framework, named NORM (Non-volatile memORy eMulator), to emulate
and verify the behavior of any intermittent computing system that exploits fast
non-volatile memories. Our evaluation showed that NORM can be used to emulate
and validate FeRAM-based transiently-powered hardware architectures
successfully.


title:
Simulating the Space Weather in the AU Mic System: Stellar Winds and
  Extreme Coronal Mass Ejections

abstract:
  Two close-in planets have been recently found around the M-dwarf flare star
AU Microscopii (AU Mic). These Neptune-sized planets (AU Mic b and c) seem to
be located very close to the so-called "evaporation valley" in the exoplanet
population, making this system an important target for studying atmospheric
loss on exoplanets. This process, while mainly driven by the high-energy
stellar radiation, will be strongly mediated by the space environment
surrounding the planets. Here we present an investigation on this last area,
performing 3D numerical modeling of the quiescent stellar wind from AU Mic, as
well as time-dependent simulations describing the evolution of a highly
energetic Coronal Mass Ejection (CME) event in this system. Observational
constraints on the stellar magnetic field and properties of the eruption are
incorporated in our models. We carry out qualitative and quantitative
characterizations of the stellar wind, the emerging CMEs, as well as the
expected steady and transient conditions along the orbit of both exoplanets.
Our results predict an extreme space weather for AU Mic and its planets. This
includes sub-Alfv\'enic regions for the large majority of the exoplanet orbits,
very high dynamic and magnetic pressure values in quiescence (varying within
$10^{2} - 10^{5}$ times the dynamic pressure experienced by the Earth), and an
even harsher environment during the passage of any escaping CME associated with
the frequent flaring observed in AU Mic. These space weather conditions alone
pose an immense challenge for the survival of the exoplanetary atmospheres (if
any) in this system.


title:
Observer dependence of photon bunching: The influence of the
  relativistic redshift on Hong-Ou-Mandel interference

abstract:
  We study the influence of the relativistic redshift on Hong-Ou-Mandel (HOM)
interference, and present a genuine quantum test of general relativity. We use
Glauber's theory of quantum coherence to predict the coincidence probability of
realistic broadband photons in HOM-experiments in a non-relativistic setting.
We extend the quantum field theoretical framework previously developed to
describe the deformation of the spectral profile of single photons in curved
spacetimes to a multi-photon framework, which is exact for inertial observers
in a flat spacetime and an approximation when observers are located in a curved
spacetime. We find that, in case of frequency entangled photons, a mutual
redshift between the sender and the receiver can change the coincidence
statistics from photon bunching to photon anti-bunching, and vice versa. This
implies that the (anti-) symmetry of the photonic spectral wave function is an
observer dependent notion, and that this can be probed via HOM-experiments in a
relativistic setting.


title:
Energy Efficiency in Rate-Splitting Multiple Access with Mixed
  Criticality

abstract:
  Future sixth generation (6G) wireless communication networks face the need to
similarly meet unprecedented quality of service (QoS) demands while also
providing a larger energy efficiency (EE) to minimize their carbon footprint.
Moreover, due to the diverseness of network participants, mixed criticality QoS
levels are assigned to the users of such networks. In this work, with a focus
on a cloud-radio access network (C-RAN), the fulfillment of desired QoS and
minimized transmit power use is optimized jointly within a rate-splitting
paradigm. Thereby, the optimization problem is non-convex. Hence, a
low-complexity algorithm is proposed based on fractional programming. Numerical
results validate that there is a trade-off between the QoS fulfillment and
power minimization. Moreover, the energy efficiency of the proposed
rate-splitting algorithm is larger than in comparative schemes, especially with
mixed criticality.


title: TimeREISE: Time-series Randomized Evolving Input Sample Explanation

abstract:
  Deep neural networks are one of the most successful classifiers across
different domains. However, due to their limitations concerning
interpretability their use is limited in safety critical context. The research
field of explainable artificial intelligence addresses this problem. However,
most of the interpretability methods are aligned to the image modality by
design. The paper introduces TimeREISE a model agnostic attribution method
specifically aligned to success in the context of time series classification.
The method shows superior performance compared to existing approaches
concerning different well-established measurements. TimeREISE is applicable to
any time series classification network, its runtime does not scale in a linear
manner concerning the input shape and it does not rely on prior data knowledge.


title:
Search for invisible Higgs-boson decays in events with vector-boson
  fusion signatures using 139 $\text{fb}^{-1}$ of proton-proton data recorded
  by the ATLAS experiment

abstract:
  A direct search for Higgs bosons produced via vector-boson fusion and
subsequently decaying into invisible particles is reported. The analysis uses
139 $\text{fb}^{-1}$ of $pp$ collision data at a centre-of-mass energy of
$\sqrt{s}$=13 $\text{TeV}$ recorded by the ATLAS detector at the LHC. The
observed numbers of events are found to be in agreement with the background
expectation from Standard Model processes. For a scalar Higgs boson with a mass
of 125 $\text{GeV}$ and a Standard Model production cross section, an observed
upper limit of $0.145$ is placed on the branching fraction of its decay into
invisible particles at 95% confidence level, with an expected limit of $0.103$.
These results are interpreted in the context of models where the Higgs boson
acts as a portal to dark matter, and limits are set on the scattering cross
section of weakly interacting massive particles and nucleons. Invisible decays
of additional scalar bosons with masses from 50 $\text{GeV}$ to 2 $\text{TeV}$
are also studied, and the derived upper limits on the cross section times
branching fraction decrease with increasing mass from 1.0 $\text{pb}$ for a
scalar boson mass of 50 $\text{GeV}$ to 0.1 $\text{pb}$ at a mass of 2
$\text{TeV}$.


title:
Unified smoke and fire detection in an evolutionary framework with
  self-supervised progressive data augment

abstract:
  Few researches have studied simultaneous detection of smoke and flame
accompanying fires due to their different physical natures that lead to
uncertain fluid patterns. In this study, we collect a large image data set to
re-label them as a multi-label image classification problem so as to identify
smoke and flame simultaneously. In order to solve the generalization ability of
the detection model on account of the movable fluid objects with uncertain
shapes like fire and smoke, and their not compactible natures as well as the
complex backgrounds with high variations, we propose a data augment method by
random image stitch to deploy resizing, deforming, position variation, and
background altering so as to enlarge the view of the learner. Moreover, we
propose a self-learning data augment method by using the class activation map
to extract the highly trustable region as new data source of positive examples
to further enhance the data augment. By the mutual reinforcement between the
data augment and the detection model that are performed iteratively, both
modules make progress in an evolutionary manner. Experiments show that the
proposed method can effectively improve the generalization performance of the
model for concurrent smoke and fire detection.


title:
Robust Nonparametric Distribution Forecast with Backtest-based Bootstrap
  and Adaptive Residual Selection

abstract:
  Distribution forecast can quantify forecast uncertainty and provide various
forecast scenarios with their corresponding estimated probabilities. Accurate
distribution forecast is crucial for planning - for example when making
production capacity or inventory allocation decisions. We propose a practical
and robust distribution forecast framework that relies on backtest-based
bootstrap and adaptive residual selection. The proposed approach is robust to
the choice of the underlying forecasting model, accounts for uncertainty around
the input covariates, and relaxes the independence between residuals and
covariates assumption. It reduces the Absolute Coverage Error by more than 63%
compared to the classic bootstrap approaches and by 2% - 32% compared to a
variety of State-of-the-Art deep learning approaches on in-house product sales
data and M4-hourly competition data.


title:
Collective mode excitations and simulated $L$-edge resonant-inelastic
  x-ray scattering spectra in antiferromagnetic Ca$_2$RuO$_4$

abstract:
  We investigate the low-energy excitation spectra in the antiferromagnetic
phase of Ca$_2$RuO$_4$ using the three-orbital Hubbard model. We calculate the
dynamical susceptibilities in the low-energy region by the random phase
approximation and find that the anisotropic dispersion of the transverse mode
is in good agreement with the spectra recently reported by inelastic neutron
scattering experiments. Using the fast collision approximation, we simulate the
resonant inelastic x-ray scattering (RIXS) spectra of the Ru $L_3$-edge from
the dynamical susceptibilities. We show that the dispersion of the transverse
mode is clearly observed in the calculated RIXS spectra and that the
polarization dependence of the incident x-rays enables one to distinguish
between the excitations of the in-plane transverse mode and out-of-plane
transverse mode.


title:
Extension and validation of the pendulum model for longitudinal solar
  prominence oscillations

abstract:
  Longitudinal oscillations in prominences are common phenomena on the Sun.
These oscillations can be used to infer the geometry and intensity of the
filament magnetic field. Previous theoretical studies of longitudinal
oscillations made two simplifying assumptions: uniform gravity and
semi-circular dips on the supporting flux tubes. However, the gravity is not
uniform and realistic dips are not semi-circular. To understand the effects of
including the nonuniform solar gravity on longitudinal oscillations, and
explore the validity of the pendulum model with different flux-tube geometries.
We first derive the equation describing the motion of the plasma along the flux
tube including the effects of nonuniform gravity, yielding corrections to the
original pendulum model. We also compute the full numerical solutions for the
normal modes, and compare them with the new pendulum approximation. We have
found that the nonuniform gravity introduces a significant modification in the
pendulum model. We have also found a cut-off period, i.e. the longitudinal
oscillations cannot have a period longer than 167 minutes. In addition,
considering different tube geometries, the period depends almost exclusively on
the radius of curvature at the bottom of the dip. We conclude that nonuniform
gravity significantly modifies the pendulum model. These corrections are
important for prominence seismology, because the inferred values of the radius
of curvature and minimum magnetic-field strength differ substantially from
those of the old model. However, we find that the corrected pendulum model is
quite robust and is still valid for non-circular dips.


title:
Spatio-temporal analysis of electromagnetic field coherence in complex
  media

abstract:
  We study the coherence in time and space of electromagnetic fields propagated
through complex media. Whether for localization, imaging or telecommunication,
the development of dedicated numerical techniques is generally based on the
exploitation of simplified models considering either coherent or diffuse
fields. The optimization of such applications in conditions of partial
coherence can therefore be particularly challenging, requiring the development
of hybrid algorithms adaptable to prior knowledge on the processed fields. The
objective of this work is to provide numerical techniques for decomposing an
electromagnetic field into subspaces that can then be filtered according to
their level of spatial and temporal coherence. In contrast to the studies
carried out on space-space transfer matrices notably used for the calculation
of Wigner-Smith operators, these decompositions are carried out on space-time
matrices in order to facilitate the study of temporal dispersion. The theory is
developed for illustrative purposes using experimental results from a leaky
resonant system but seem to be applicable to any scattering and reverberating
media capable of transforming localized and coherent excitations into complex
and diffuse distributions. To conclude this work, the proposed technique is
exploited to improve image reconstruction in a millimeter-wave computational
imaging demonstration. In the studied context and from a more general
perspective, we propose a technique to select the most suitable subspaces for
each application operating under conditions of partial coherence, whether these
correspond in the most extreme cases to ballistic paths or to diffuse fields.


title:
EdgeFormer: A Parameter-Efficient Transformer for On-Device Seq2seq
  Generation

abstract:
  We propose EdgeFormer -- a parameter-efficient Transformer of the
encoder-decoder architecture for on-device seq2seq generation, which is
customized under strict computation and memory constraints. EdgeFormer proposes
two novel principles for cost-effective parameterization and further enhance
the model with efficient layer adaptation. We conduct extensive experiments on
two practical on-device seq2seq tasks: Machine Translation and Grammatical
Error Correction, and show that EdgeFormer can effectively outperform previous
parameter-efficient Transformer baselines and achieve very competitive results
with knowledge distillation under both the computation and memory constraints.
Moreover, we release the pretrained EdgeFormer -- the first publicly available
pretrained model that can be easily fine-tuned for English seq2seq tasks with
strong results, largely facilitating on-device seq2seq generation in practice.


title:
On a Variance Reduction Correction of the Temporal Difference for Policy
  Evaluation in the Stochastic Continuous Setting

abstract:
  This paper deals with solving continuous time, state and action optimization
problems in stochastic settings, using reinforcement learning algorithms, and
considers the policy evaluation process. We prove that standard learning
algorithms based on the discretized temporal difference are doomed to fail when
the time discretization tends to zero, because of the stochastic part. We
propose a variance-reduction correction of the temporal difference, leading to
new learning algorithms that are stable with respect to vanishing time steps.
This allows us to give theoretical guarantees of convergence of our algorithms
to the solutions of continuous stochastic optimization problems.


title: Identity testing for radical expressions

abstract:
  We study the \emph{Radical Identity Testing} problem (RIT): Given an
algebraic circuit representing a multivariate polynomial $f(x_1, \dots, x_k)$
and nonnegative integers $a_1, \dots, a_k$ and $d_1, \dots,$ $d_k$, written in
binary, test whether the polynomial vanishes at the \emph{real radicals}
$\sqrt[d_1]{a_1}, \dots,\sqrt[d_k]{a_k}$, i.e., test whether
$f(\sqrt[d_1]{a_1}, \dots, \sqrt[d_k]{a_k}) = 0$. We place the problem in
{coNP} assuming the Generalised Riemann Hypothesis (GRH), improving on the
straightforward PSPACE upper bound obtained by reduction to the existential
theory of reals. Next we consider a restricted version, called $2$-RIT, where
the radicals are square roots of prime numbers, written in binary. It was known
since the work of Chen and Kao that $2$-RIT is at least as hard as the
polynomial identity testing problem, however no better upper bound than PSPACE
was known prior to our work. We show that $2$-RIT is in coRP assuming GRH and
in coNP unconditionally. Our proof relies on theorems from algebraic and
analytic number theory, such as the Chebotarev density theorem and quadratic
reciprocity.


title: Revisiting Parameter-Efficient Tuning: Are We Really There Yet?

abstract:
  Parameter-efficient tuning (PETuning) methods have been deemed by many as the
new paradigm for using pretrained language models (PLMs). By tuning just a
fraction amount of parameters comparing to full model finetuning, PETuning
methods claim to have achieved performance on par with or even better than
finetuning. In this work, we take a step back and re-examine these PETuning
methods by conducting the first comprehensive investigation into the training
and evaluation of PETuning methods. We found the problematic validation and
testing practice in current studies, when accompanied by the instability nature
of PETuning methods, has led to unreliable conclusions. When being compared
under a truly fair evaluation protocol, PETuning cannot yield consistently
competitive performance while finetuning remains to be the best-performing
method in medium- and high-resource settings. We delve deeper into the cause of
the instability and observed that model size does not explain the phenomenon
but training iteration positively correlates with the stability.


title:
Self-Organizing Maps for Exploration of Partially Observed Data and
  Imputation of Missing Values

abstract:
  The self-organizing map is an unsupervised neural network which is widely
used for data visualisation and clustering in the field of chemometrics. The
classical Kohonen algorithm that computes self-organizing maps is suitable only
for complete data without any missing values. However, in many applications,
partially observed data are the norm. In this paper, we propose an extension of
self-organizing maps to incomplete data via a new criterion that also defines
estimators of the missing values. In addition, an adaptation of the Kohonen
algorithm, named missSOM, is provided to compute these self-organizing maps and
impute missing values. An efficient implementation is provided. Numerical
experiments on simulated data and a chemical dataset illustrate the short
computing time of missSOM and assess its performance regarding various criteria
and in comparison to the state of the art.


title:
On stability of classes of solutions to partial differential relations
  constructed by quasiconvex functions and null Lagrangians with respect to
  precompact famalies in $C_{loc}$

abstract:
  We prove theorems on stability of classes of solutions to partial
differential relations constructed by quasiconvex functions and null
Lagrangians with respect to precompact famalies in $C_{loc}$.


title: GAN Estimation of Lipschitz Optimal Transport Maps

abstract:
  This paper introduces the first statistically consistent estimator of the
optimal transport map between two probability distributions, based on neural
networks. Building on theoretical and practical advances in the field of
Lipschitz neural networks, we define a Lipschitz-constrained generative
adversarial network penalized by the quadratic transportation cost. Then, we
demonstrate that, under regularity assumptions, the obtained generator
converges uniformly to the optimal transport map as the sample size increases
to infinity. Furthermore, we show through a number of numerical experiments
that the learnt mapping has promising performances. In contrast to previous
work tackling either statistical guarantees or practicality, we provide an
expressive and feasible estimator which paves way for optimal transport
applications where the asymptotic behaviour must be certified.


title: Guessing with Little Data

abstract:
  Reconstructing a hypothetical recurrence equation from the first terms of an
infinite sequence is a classical and well-known technique in experimental
mathematics. We propose a variation of this technique which can succeed with
fewer input terms.


title:
Magnetic support for neutrino-driven explosion of 3D non-rotating
  core-collapse supernova models

abstract:
  The impact of the magnetic field on the postbounce supernova dynamics of
non-rotating stellar cores is studied by performing three-dimensional
magnetohydrodynamics simulations with spectral neutrino transport. We compare
the explodability between initially strongly and weakly magnetized models of
$20$ and $27$ $M_{\odot}$ pre-supernova progenitors. We find that although the
efficiency for the conversion of the neutrino heating into the turbulent energy
including magnetic fields in the gain region is not significantly different
between the strong and weak field models, the amplified magnetic field due to
the neutrino-driven convection on large hot bubbles just behind a stalled shock
leads to the faster and more energetic explosion in the strongly magnetized
models. In addition, by comparing the difference between 2nd- and 5th-order
spatial accuracy of the simulation in the strong field model for $27$
$M_{\odot}$ progenitor, we also find that the higher-order accuracy in space is
positive for the explosion because it enhances the growth of neutrino-driven
convection in the gain region. A new possibility of the origin of the magnetic
field of the proto-neutron star (PNS) is proposed based on our results of
core-collapse supernova simulations for the non-rotating model. The magnetic
field is accumulated and amplified to the magnetar level, that is,
$\mathcal{O}(10^{14})$ G, in the convectively stable shell in the vicinity of
the PNS surface.


title: On loss functions and evaluation metrics for music source separation

abstract:
  We investigate which loss functions provide better separations via
benchmarking an extensive set of those for music source separation. To that
end, we first survey the most representative audio source separation losses we
identified, to later consistently benchmark them in a controlled experimental
setup. We also explore using such losses as evaluation metrics, via
cross-correlating them with the results of a subjective test. Based on the
observation that the standard signal-to-distortion ratio metric can be
misleading in some scenarios, we study alternative evaluation metrics based on
the considered losses.


title: Semigroups of composition operators on Hardy spaces of Dirichlet series

abstract:
  We consider continuous semigroups of analytic functions $\{\Phi_t\}_{t\geq0}$
in the so-called Gordon-Hedenmalm class $\mathcal{G}$, that is, the family of
analytic functions $\Phi:\mathbb C_+\to \mathbb C_+$ giving rise to bounded
composition operators in the Hardy space of Dirichlet series $\mathcal{H}^2$.
We show that there is a one-to-one correspondence between continuous semigroups
$\{\Phi_{t}\}_{t\geq0}$ in the class $\mathcal G$ and strongly continuous
semigroups of composition operators $\{T_t\}_{t\geq0}$, where
$T_t(f)=f\circ\Phi_t$, $f\in\mathcal{H}^2$. We extend these results for the
range $p\in[1,\infty)$. For the case $p=\infty$, we prove that there is no
non-trivial strongly continuous semigroup of composition operators in
$\mathcal{H}^\infty$. We characterize the infinitesimal generators of
continuous semigroups in the class $\mathcal G$ as those Dirichlet series
sending $\mathbb C_{+}$ into its closure. Some dynamical properties of the
semigroups are obtained from a description of the Koenigs map of the semigroup.


title: Interaction of water with nitrogen-doped graphene

abstract:
  We have studied the interaction of water and graphene doped with nitrogen in
different configurations, namely, graphitic and pyridinic nitrogen, by means of
the van der Waals density functional. We found that the local nitrogen
configuration plays a key role in determining the stable water configuration,
while the dispersion force is responsible for the water adsorption. With the
graphitic nitrogen, water prefers to orient with its oxygen toward the surface,
whereas for the pyridinic nitrogen it prefers to orient with its hydrogens
toward the surface, because nitrogen is positively and negatively charged for
the former and the latter, respectively. Our results have great implications
for the modeling of the interface between water and nitrogen-doped graphitic
systems.


title: Large-System Insensitivity of Zero-Waiting Load Balancing Algorithms

abstract:
  This paper studies the sensitivity (or insensitivity) of a class of load
balancing algorithms that achieve asymptotic zero-waiting in the
sub-Halfin-Whitt regime, named LB-zero. Most existing results on zero-waiting
load balancing algorithms assume the service time distribution is exponential.
This paper establishes the {\em large-system insensitivity} of LB-zero for jobs
whose service time follows a Coxian distribution with a finite number of
phases. This result suggests that LB-zero achieves asymptotic zero-waiting for
a large class of service time distributions, which is confirmed in our
simulations. To prove this result, this paper develops a new technique, called
"Iterative State-Space Peeling" (or ISSP for short). ISSP first identifies an
iterative relation between the upper and lower bounds on the queue states and
then proves that the system lives near the fixed point of the iterative bounds
with a high probability. Based on ISSP, the steady-state distribution of the
system is further analyzed by applying Stein's method in the neighborhood of
the fixed point. ISSP, like state-space collapse in heavy-traffic analysis, is
a general approach that may be used to study other complex stochastic systems.


title: Tailoring higher-order exceptional points toward enhanced sensitivity

abstract:
  Non-Hermitian degeneracies, in the form of exceptional points (EPs), are
unique characteristics of typical $\PT$-symmetric systems. At EPs, two or more
eigenvalues and their corresponding eigenvectors coalesce. While second-order
EPs are mostly explored, recently, higher-order EPs are garnering extensive
attention in various multidisciplinary fields, particularly owing to their
possible applications for extremely high-sensitive devices. Here, we present a
simple yet effective scheme to enhance the device sensitivity by slightly
deviating the gain-neutral-loss linear configuration to a triangular one,
resulting in an abrupt phase transition from third-order to second-order EPs.
Our analysis demonstrates that the EPs can be tailored by judicious tuning of
the coupling parameters of the system, resulting in enhanced sensitivity to a
small perturbation. The tunable coupling also leads to a sharp change in the
sensitivity slope, enabling the perturbation to be measured precisely as a
function of coupling. This two-way detection of the perturbation opens up a
rich landscape toward ultra-sensitive measurements, which could be applicable
to a wide range of non-Hermitian ternary platforms.


title:
Long-term behaviour of asymptotically autonomous Hamiltonian systems
  with multiplicative noise

abstract:
  The influence of multiplicative stochastic perturbations on the class of
asymptotically Hamiltonian systems on the plane is investigated. It is assumed
that disturbances do not preserve the equilibrium of the corresponding limiting
system and their intensity decays in time with power-law asymptotics. The paper
discusses the long-term asymptotic behaviour of solutions and its dependence on
the structure and parameters of perturbations. In particular, it is shown that
perturbed trajectories can tend to the equilibrium of the limiting system or
new stochastically stable states can arise. The proposed analysis is based on a
combination of the averaging method and the construction of stochastic Lyapunov
functions. The results obtained are applied to the problem of capture into
parametric autoresonance in the presence of noise.


title:
Generic transporters for the linear time dependent quantum Harmonic
  oscillator on $\mathbb R$

abstract:
  In this paper we consider the linear, time dependent quantum Harmonic
Schr\"odinger equation $i \partial_t u= \frac{1}{2} ( - \partial_x^2 + x^2) u +
V(t, x, D)u$, $x \in \mathbb R$, where $V(t,x,D)$ is classical
pseudodifferential operator of order 0, selfadjoint, and $2\pi$ periodic in
time. We give sufficient conditions on the principal symbol of $V(t,x,D)$
ensuring the existence of weakly turbulent solutions displaying infinite time
growth of Sobolev norms. These conditions are generic in the Frechet space of
symbols. This shows that generic, classical pseudodifferential, $2\pi$-periodic
perturbations provoke unstable dynamics. The proof builds on the results of
[35] and it is based on pseudodifferential normal form and local energy decay
estimates. These last are proved exploiting Mourre's positive commutator
theory.


title: Phenomenology of $t\bar{t}j + X$ production at the LHC

abstract:
  We present phenomenological results for $t\bar{t}j + X$ production at the
Large Hadron Collider, of interest for designing forthcoming experimental
analyses of this process. We focus on those cases where the $t\bar{t}j + X$
process is considered as a signal. We discuss present theoretical uncertainties
and the dependence on relevant input parameters entering the computation. For
the ${\cal R}$ distribution, which depends on the invariant mass of the
$t\bar{t}j$-system, we present reference predictions in the on-shell,
$\overline{\mbox{MS}}$ and MSR top-quark mass renormalization schemes, applying
the latter scheme to this process for the first time. Our conclusions are
particularly interesting for those analyses aiming at extracting the top-quark
mass from cross-section measurements.


title: Extreme Value Theory for Hurwitz Complex Continued Fractions

abstract:
  The Hurwitz complex continued fraction is a generalization of the nearest
integer continued fraction. In this paper we prove various results concerning
extremes of the modulus of Hurwitz complex continued fraction digits. This
includes a Poisson law and an extreme value law. The results are based on cusp
estimates of the invariant measure about which information is still limited. In
the process, we get several results concerning extremes of nearest integer
continued fractions as well.


title:
Automated surface feature selection using SALSA2D: An illustration using
  Elephant Mortality data in Etosha National Park

abstract:
  This analysis is motivated by the MIKE dataset in Etosha National Park (ENP).
We use this dataset to show the development of an automated selection method
for regression models to replace the model averaging used in the original CReSS
paper. This method shows clear numerical and practical benefits over model
averaging, and it's application to the elephant carcass data are of immediate
and practical value to a range of stakeholders.
  We have developed SALSA 2D in a GLM/GAM regression framework but this paper
shows the flexibility of this approach by applying it to presence only data and
use a downweighted Poisson regression. Using SALSA2D for model selection
provided a more realistic local/clustered intensity surface compared with the
model average approach.
  The full analysis results showed high carcass intensity close to water holes
and roads and in areas of the park with average rainfall. Some high risk areas
were identified and these revelations are important for effective park
management, particularly mitigation of poaching. It is impossible to patrol
such a large area at random and these high intensity areas (particularly those
accessed by a subset of roads and near some waterholes) can be targeted for
more monitoring efforts than others.


title: Coherence of oscillations in the weak-noise limit

abstract:
  In a noisy environment, oscillations loose their coherence which can be
characterized by a quality factor. We determine this quality factor for
oscillations arising from a driven Fokker-Planck dynamics along a periodic
one-dimensional potential analytically in the weak noise limit. With this
expression, we can prove for this continuum model the analog of an upper bound
that has been conjectured for the coherence of oscillations in discrete Markov
network models. We show that our approach can also be adapted to motion along a
noisy two-dimensional limit cycle. Specifically, we apply our scheme to the
noisy Stuart-Landau oscillator and the thermodynamically consistent Brusselator
as a simple model for a chemical clock. Our approach thus complements the
fairly sophisticated extant general framework based on techniques from
Hamilton-Jacobi theory with which we compare our results numerically.


title:
Anomalous Bloch oscillation and electrical switching of edge
  magnetization in bilayer graphene nanoribbon

abstract:
  Graphene features topological edge bands that connect the pair of Dirac
points through either sectors of the 1D Brillouin zone depending on edge
configurations (zigzag or bearded). Because of their flat dispersion,
spontaneous edge magnetisation can arise from Coulomb interaction in graphene
nanoribbons, which has caught remarkable interest. We find an anomalous Bloch
oscillation in such edge bands, in which the flat dispersion freezes electron
motion along the field direction, while the topological connection of the bands
through the bulk leads to electron oscillation in the transverse direction
between edges of different configurations on opposite sides/layers of a bilayer
ribbon. Our Hubbard-model mean-field calculation shows that this phenomenon can
be exploited for electrical switching of edge magnetisation configurations.


title:
Querying Inconsistent Prioritized Data with ORBITS: Algorithms,
  Implementation, and Experiments

abstract:
  We investigate practical algorithms for inconsistency-tolerant query
answering over prioritized knowledge bases, which consist of a logical theory,
a set of facts, and a priority relation between conflicting facts. We consider
three well-known semantics (AR, IAR and brave) based upon two notions of
optimal repairs (Pareto and completion). Deciding whether a query answer holds
under these semantics is (co)NP-complete in data complexity for a large class
of logical theories, and SAT-based procedures have been devised for
repair-based semantics when there is no priority relation, or the relation has
a special structure. The present paper introduces the first SAT encodings for
Pareto- and completion-optimal repairs w.r.t. general priority relations and
proposes several ways of employing existing and new encodings to compute
answers under (optimal) repair-based semantics, by exploiting different
reasoning modes of SAT solvers. The comprehensive experimental evaluation of
our implementation compares both (i) the impact of adopting semantics based on
different kinds of repairs, and (ii) the relative performances of alternative
procedures for the same semantics.


title: m-Nearly k-Universal Words -- Investigating Simon Congruence

abstract:
  Determining the index of the Simon congruence is a long outstanding open
problem. Two words $u$ and $v$ are called Simon congruent if they have the same
set of scattered factors, which are parts of the word in the correct order but
not necessarily consecutive, e.g., $\mathtt{oath}$ is a scattered factor of
$\mathtt{logarithm}$. Following the idea of scattered factor $k$-universality,
we investigate $m$-nearly $k$-universality, i.e., words where $m$ scattered
factors of length $k$ are absent, w.r.t. Simon congruence. We present a full
characterisation as well as the index of the congruence for $m=1$. For $m\neq
1$, we show some results if in addition $w$ is $(k-1)$-universal as well as
some further insights for different $m$.


title: A Direct Road to Entropy and the Second Law of Thermodynamics

abstract:
  The entropy of classical thermodynamics is uniquely determined by the
relation of adiabatical accessibilty between equilibrium states of
thermodynamical systems. This review outlines the logical path leading to this
results and the challenges that have to be faced on the way.


title:
ADAM Challenge: Detecting Age-related Macular Degeneration from Fundus
  Images

abstract:
  Age-related macular degeneration (AMD) is the leading cause of visual
impairment among elderly in the world. Early detection of AMD is of great
importance as the vision loss caused by AMD is irreversible and permanent.
Color fundus photography is the most cost-effective imaging modality to screen
for retinal disorders. \textcolor{red}{Recently, some algorithms based on deep
learning had been developed for fundus image analysis and automatic AMD
detection. However, a comprehensive annotated dataset and a standard evaluation
benchmark are still missing.} To deal with this issue, we set up the Automatic
Detection challenge on Age-related Macular degeneration (ADAM) for the first
time, held as a satellite event of the ISBI 2020 conference. The ADAM challenge
consisted of four tasks which cover the main topics in detecting AMD from
fundus images, including classification of AMD, detection and segmentation of
optic disc, localization of fovea, and detection and segmentation of lesions.
The ADAM challenge has released a comprehensive dataset of 1200 fundus images
with the category labels of AMD, the pixel-wise segmentation masks of the full
optic disc and lesions (drusen, exudate, hemorrhage, scar, and other), as well
as the location coordinates of the macular fovea. A uniform evaluation
framework has been built to make a fair comparison of different models. During
the ADAM challenge, 610 results were submitted for online evaluation, and
finally, 11 teams participated in the onsite challenge. This paper introduces
the challenge, dataset, and evaluation methods, as well as summarizes the
methods and analyzes the results of the participating teams of each task. In
particular, we observed that ensembling strategy and clinical prior knowledge
can better improve the performances of the deep learning models.


title: Fresnel reflections in inverse freeform lens design

abstract:
  In this paper we propose a method to design a freeform lens including the
effect of Fresnel reflections on the transmitted intensity. This method is
elaborated for a lens with one freeform surface shaping a far-field target from
a point source or collimated input beam. It combines the optical mapping with
the energy balance incorporating the loss due to Fresnel reflections, which
leads to a generalized Monge-Amp\`ere equation. We adapt a least-squares solver
from previous research to solve the model numerically. This is then tested with
a theoretical example and a test case related to road lighting.


title:
Irreducible Integrable Modules for the full Toroidal Lie Algebras
  co-ordinated by Rational Quantum Torus

abstract:
  Let $\mathbb{C}_q$ be a non-commutative Laurent polynomial ring associated
with a $(n+1)\times (n+1)$ rational quantum matrix $q$. Let
$\mathfrak{sl}_d(\mathbb{C}_q)\oplus HC_1(\mathbb{C}_q)$ be the universal
central extension of Lie subalgebra $\mathfrak{sl}_d(\mathbb{C}_q)$ of
$\mathfrak{gl}_d(\mathbb{C}_q)$. Now let us take the Lie algebra
$\tau=\mathfrak{gl}_d(\mathbb{C}_q)\oplus HC_1(\mathbb{C}_q)$. Let
$Der(\mathbb{C}_q)$ be the Lie algebra of all derivations of $\mathbb{C}_q$.
Now we consider the Lie algebra $\tilde{\tau}=\tau\rtimes Der(\mathbb{C}_q)$,
called as full toroidal Lie algebra co-ordinated by rational quantum tori. In
this paper we get a classification of irreducible integrable modules with
finite dimensional weight spaces for $\tilde{\tau}$ with nonzero central action
on the modules.


title:
Towards a Better Understanding of Online Influence: Differences in
  Twitter CommunicationBetween Companies and Influencers

abstract:
  In the last decade, Social Media platforms such as Twitter have gained
importance in the various marketing strategies of companies. This work aims to
examine the presence of influential content on a textual level, by
investigating characteristics of tweets in the context of social impact theory,
and its dimension immediacy. To this end, we analysed influential Twitter
communication data during Black Friday 2018 with methods from social media
analytics such as sentiment analysis and degree centrality. Results show
significant differences in communication style between companies and
influencers. Companies published longer textual content and created more tweets
with a positive sentiment and more first-person pronouns than influencers.
These findings shall serve as a basis for a future experimental study to
examine the impact of text presence on consumer cognition and the willingness
to purchase.


title: Out-Of-Distribution Generalization on Graphs: A Survey

abstract:
  Graph machine learning has been extensively studied in both academia and
industry. Although booming with a vast number of emerging methods and
techniques, most of the literature is built on the I.I.D. hypothesis, i.e.,
testing and training graph data are independent and identically distributed.
However, this I.I.D. hypothesis can hardly be satisfied in many real-world
graph scenarios where the model performance substantially degrades when there
exist distribution shifts between testing and training graph data. To solve
this critical problem, out-of-distribution (OOD) generalization on graphs,
which goes beyond the I.I.D. hypothesis, has made great progress and attracted
ever-increasing attention from the research community. In this paper, we
comprehensively survey OOD generalization on graphs and present a detailed
review of recent advances in this area. First, we provide a formal problem
definition of OOD generalization on graphs. Second, we categorize existing
methods into three classes from conceptually different perspectives, i.e.,
data, model, and learning strategy, based on their positions in the graph
machine learning pipeline, followed by detailed discussions for each category.
We also review the theories related to OOD generalization on graphs and
introduce the commonly used graph datasets for thorough evaluations. Last but
not least, we share our insights on future research directions. This paper is
the first systematic and comprehensive review of OOD generalization on graphs,
to the best of our knowledge.


title: On the Self Shuffle Language

abstract:
  The shuffle product \(u\shuffle v\) of two words \(u\) and \(v\) is the set
of all words which can be obtained by interleaving \(u\) and \(v\). Motivated
by the paper \emph{The Shuffle Product: New Research Directions} by Restivo
(2015) we investigate a special case of the shuffle product. In this work we
consider the shuffle of a word with itself called the \emph{self shuffle} or
\emph{shuffle square}, showing first that the self shuffle language and the
shuffle of the language are in general different sets. We prove that the
language of all words arising as a self shuffle of some word is context
sensitive but not context free. Furthermore, we show that the self shuffle \(w
\shuffle w\) uniquely determines \(w\).


title:
High-order accurate entropy stable adaptive moving mesh finite
  difference schemes for (multi-component) compressible Euler equations with
  the stiffened equation of state

abstract:
  This paper extends the high-order entropy stable (ES) adaptive moving mesh
finite difference schemes developed in [14] to the two- and three-dimensional
(multi-component) compressible Euler equations with the stiffened equation of
state. The two-point entropy conservative (EC) flux is first constructed in the
curvilinear coordinates. The high-order semi-discrete EC schemes are given with
the aid of the two-point EC flux and the high-order discretization of the
geometric conservation laws, and then the high-order semi-discrete ES schemes
satisfying the entropy inequality are derived by adding the high-order
dissipation term based on the multi-resolution weighted essentially
non-oscillatory (WENO) reconstruction for the scaled entropy variables to the
EC schemes. The explicit strong-stability-preserving Runge-Kutta methods are
used for the time discretization and the mesh points are adaptively
redistributed by iteratively solving the mesh redistribution equations with an
appropriately chosen monitor function. Several 2D and 3D numerical tests are
conducted on the parallel computer system with the MPI programming to validate
the accuracy and the ability to capture effectively the localized structures of
the proposed schemes.


title: Eco-engineering controls vegetation trends in southwest China karst

abstract:
  The karst area in Yunnan-Guangxi-Guizhou region in southwest China is known
for widespread rocky desertification but several studies report a greening
trend since the year 2000. While the start of the greening trend seems to match
with the implementation of ecological conservation projects, no statistical
evidence on a relationship between vegetation greening and eco-engineering
exists. Moreover, dominant factors influencing the spatial patterns of
vegetation trends have rarely been investigated. Here we use six comprehensive
factors representing the natural conditions and human activities of the study
area, and several statistical models consistently show that eco-engineering
explains large parts of the positive vegetation trends in the karst areas,
while negative vegetation trends in non-karst areas of Yunnan were related with
a decrease in rainfall. We further show that the interaction of eco-engineering
with other factors leads to a heterogeneous pattern of different vegetation
trends. Knowing and understanding these patterns is crucial when planning
ecological restoration, especially in diverse landscapes like China karst and
the methods can be reused in other restoration areas.


title: ADIMA: Abuse Detection In Multilingual Audio

abstract:
  Abusive content detection in spoken text can be addressed by performing
Automatic Speech Recognition (ASR) and leveraging advancements in natural
language processing. However, ASR models introduce latency and often perform
sub-optimally for profane words as they are underrepresented in training
corpora and not spoken clearly or completely. Exploration of this problem
entirely in the audio domain has largely been limited by the lack of audio
datasets. Building on these challenges, we propose ADIMA, a novel,
linguistically diverse, ethically sourced, expert annotated and well-balanced
multilingual profanity detection audio dataset comprising of 11,775 audio
samples in 10 Indic languages spanning 65 hours and spoken by 6,446 unique
users. Through quantitative experiments across monolingual and cross-lingual
zero-shot settings, we take the first step in democratizing audio based content
moderation in Indic languages and set forth our dataset to pave future work.


title: Improved analysis of randomized SVD for top-eigenvector approximation

abstract:
  Computing the top eigenvectors of a matrix is a problem of fundamental
interest to various fields. While the majority of the literature has focused on
analyzing the reconstruction error of low-rank matrices associated with the
retrieved eigenvectors, in many applications one is interested in finding one
vector with high Rayleigh quotient. In this paper we study the problem of
approximating the top-eigenvector. Given a symmetric matrix $\mathbf{A}$ with
largest eigenvalue $\lambda_1$, our goal is to find a vector \hu that
approximates the leading eigenvector $\mathbf{u}_1$ with high accuracy, as
measured by the ratio
$R(\hat{\mathbf{u}})=\lambda_1^{-1}{\hat{\mathbf{u}}^T\mathbf{A}\hat{\mathbf{u}}}/{\hat{\mathbf{u}}^T\hat{\mathbf{u}}}$.
We present a novel analysis of the randomized SVD algorithm of
\citet{halko2011finding} and derive tight bounds in many cases of interest.
Notably, this is the first work that provides non-trivial bounds of
$R(\hat{\mathbf{u}})$ for randomized SVD with any number of iterations. Our
theoretical analysis is complemented with a thorough experimental study that
confirms the efficiency and accuracy of the method.


title:
Planckian jitter: enhancing the color quality of self-supervised visual
  representations

abstract:
  Several recent works on self-supervised learning are trained by mapping
different augmentations of the same image to the same feature representation.
The set of used data augmentations is of crucial importance for the quality of
the learned feature representation. We analyze how the traditionally used color
jitter negatively impacts the quality of the color features in the learned
feature representation. To address this problem, we replace this module with
physics-based color augmentation, called Planckian jitter, which creates
realistic variations in chromaticity, producing a model robust to llumination
changes that can be commonly observed in real life, while maintaining the
ability to discriminate the image content based on color information. We
further improve the performance by introducing a latent space combination of
color-sensitive and non-color-sensitive features. These are found to be
complementary and the combination leads to large absolute performance gains
over the default data augmentation on color classification tasks, including on
Flowers-102 (+15%), Cub200 (+11%), VegFru (+15%), and T1K+ (+12%). Finally, we
present a color sensitivity analysis to document the impact of different
training methods on the model neurons and we show that the performance of the
learned features is robust with respect to illuminant variations.


title:
Privacy-preserving Similarity Calculation of Speaker Features Using
  Fully Homomorphic Encryption

abstract:
  Recent advances in machine learning techniques are enabling Automated Speech
Recognition (ASR) more accurate and practical. The evidence of this can be seen
in the rising number of smart devices with voice processing capabilities. More
and more devices around us are in-built with ASR technology. This poses serious
privacy threats as speech contains unique biometric characteristics and
personal data. However, the privacy concern can be mitigated if the voice
features are processed in the encrypted domain. Within this context, this paper
proposes an algorithm to redesign the back-end of the speaker verification
system using fully homomorphic encryption techniques. The solution exploits the
Cheon-Kim-Kim-Song (CKKS) fully homomorphic encryption scheme to obtain a
real-time and non-interactive solution. The proposed solution contains a novel
approach based on Newton Raphson method to overcome the limitation of CKKS
scheme (i.e., calculating an inverse square-root of an encrypted number). This
provides an efficient solution with less multiplicative depths for a negligible
loss in accuracy. The proposed algorithm is validated using a well-known speech
dataset. The proposed algorithm performs encrypted-domain verification in
real-time (with less than 1.3 seconds delay) for a 2.8\% equal-error-rate loss
compared to plain-domain verification.


title: Branching Reinforcement Learning

abstract:
  In this paper, we propose a novel Branching Reinforcement Learning (Branching
RL) model, and investigate both Regret Minimization (RM) and Reward-Free
Exploration (RFE) metrics for this model. Unlike standard RL where the
trajectory of each episode is a single $H$-step path, branching RL allows an
agent to take multiple base actions in a state such that transitions branch out
to multiple successor states correspondingly, and thus it generates a
tree-structured trajectory. This model finds important applications in
hierarchical recommendation systems and online advertising. For branching RL,
we establish new Bellman equations and key lemmas, i.e., branching value
difference lemma and branching law of total variance, and also bound the total
variance by only $O(H^2)$ under an exponentially-large trajectory. For RM and
RFE metrics, we propose computationally efficient algorithms BranchVI and
BranchRFE, respectively, and derive nearly matching upper and lower bounds. Our
results are only polynomial in problem parameters despite exponentially-large
trajectories.


title:
New steps in $C^0$ symplectic and contact geometry of smooth
  submanifolds

abstract:
  We provide a $C^0$ counterexample to the Lagrangian Arnold conjecture in the
cotangent bundle of a closed manifold. Additionally, we prove a quantitative
$h$-principle for subcritical isotropic embeddings in contact manifolds, and
provide an explicit construction of a contact homeomorphism which takes a
subcritical isotropic curve to a transverse one. On the rigid side, we give
another proof of the Dimitroglou Rizell and Sullivan theorem \cite{RS22} which
states that Legendrian knots are preserved by contact homeomorphisms, provided
their image is smooth. Moreover, our method gives related examples of rigidity
in higher dimensions as well.


title: Consistent and non-consistent deformations of gravitational theories

abstract:
  We study the internally abelianized version of a range of gravitational
theories, written in connection tetrad form, and study the possible interaction
terms that can be added to them in a consistent way. We do this for 2+1
dimensional and 3+1 dimensional models. In the latter case we show that the
Cartan-Palatini and Holst actions are not consistent deformations of their
abelianized versions. We also show that the Husain-Kucha\v{r} and Euclidean
self-dual actions are consistent deformations of their abelianized
counterparts. This suggests that if the latter can be quantized, it could be
possible to devise a perturbative scheme leading to the quantization of
Euclidean general relativity along the lines put forward by Smolin in the early
nineties.


title: DeepTx: Deep Learning Beamforming with Channel Prediction

abstract:
  Machine learning algorithms have recently been considered for many tasks in
the field of wireless communications. Previously, we have proposed the use of a
deep fully convolutional neural network (CNN) for receiver processing and shown
it to provide considerable performance gains. In this study, we focus on
machine learning algorithms for the transmitter. In particular, we consider
beamforming and propose a CNN which, for a given uplink channel estimate as
input, outputs downlink channel information to be used for beamforming. The CNN
is trained in a supervised manner considering both uplink and downlink
transmissions with a loss function that is based on UE receiver performance.
The main task of the neural network is to predict the channel evolution between
uplink and downlink slots, but it can also learn to handle inefficiencies and
errors in the whole chain, including the actual beamforming phase. The provided
numerical experiments demonstrate the improved beamforming performance.


title:
Quantum many-body spin rings coupled to ancillary spins: The sunburst
  quantum Ising model

abstract:
  We study the ground-state properties of a quantum "sunburst model", composed
of a quantum Ising spin-ring in a transverse field, symmetrically coupled to a
set of ancillary isolated qubits, to maintain a residual translation invariance
and also a $\mathbb{Z}_2$ symmetry. The large-size limit is taken in two
different ways: either by keeping the distance between any two neighboring
ancillary qubits fixed, or by fixing their number while increasing the ring
size. Substantially different regimes emerge, depending on the various
Hamiltonian parameters: for small energy scale $\delta$ of the ancillary
subsystem and small ring-qubits interaction $\kappa$, we observe rapid and
nonanalytic changes in proximity of the quantum transitions of the Ising ring,
both first-order and continuous, which can be carefully controlled by
exploiting renormalization-group and finite-size scaling frameworks. Smoother
behaviors are instead observed when keeping $\delta>0$ fixed and in the Ising
disordered phase.


title: An elementary example of Sard's Theorem sharpness

abstract:
  In this note we define a $C^1$ function $F:[0,M]^2\to [0,2]$ that satisfies
that its set of critical values has positive measure. This function provides an
example, easier than those that usually appear in the literature, of how the
order of differentiability required in Sard's Theorem cannot be improved


title: Lyapunov function for non-equilibrium transport processes

abstract:
  Irreversibility is a critical property of non-equilibrium transport
processes. An opinion has long been insisted that the entropy production rate
is a Lyapunov function for all kinds of processes, that is, the principle of
minimum entropy production. However, such principle is based on some strong
assumptions that are rarely valid in practice. Here, the common features of
parabolic-like transport processes are discussed. A theorem is then put forward
that the dot products of fluxes and corresponding forces serve as Lyapunov
function for parabolic-like transport processes. Such fluxes and forces are
defined by their actual constitutive relations (e.g., the Fourier's law, the
Fick's law, etc.). Then, some typical transport processes are analyzed.
Particularly for heat conduction, both the theoretical and numerical analyses
demonstrate that its Lyapunov function is the entransy dissipation rather that
the entropy production, when the Fourier's law is valid. The present work could
be helpful for further understanding on the irreversibility and the
mathematical interpretation of non-equilibrium processes.


title: A fast radio burst with sub-millisecond quasi-periodic structure

abstract:
  Fast radio bursts (FRBs) are extragalactic radio transients of extraordinary
luminosity. Studying the diverse temporal and spectral behaviour recently
observed in a number of FRBs may help determine the nature of the entire class.
For example, a fast spinning or highly magnetised neutron star might generate
the rotation-powered acceleration required to explain the bright emission.
Periodic, sub-second components, suggesting such rotation, were recently
reported in one FRB, and potentially in two more. Here we report the discovery
of FRB 20201020A with Apertif, an FRB showing five components regularly spaced
by 0.415 ms. This sub-millisecond structure in FRB 20201020A carries important
clues about the progenitor of this FRB specifically, and potentially about that
of FRBs in general. We thus contrast its features to the predictions of the
main FRB source models. We perform a timing analysis of the FRB 20201020A
components to determine the significance of the periodicity. We compare these
against the timing properties of the previously reported CHIME FRBs with
sub-second quasi-periodic components, and against two Apertif bursts from
repeating FRB 20180916B that show complex time-frequency structure. We find the
periodicity of FRB 20201020A to be marginally significant at 2.5$\sigma$. Its
repeating subcomponents cannot be explained as a pulsar rotation since the
required spin rate of over 2 kHz exceeds the limits set by typical neutron star
equations of state and observations. The fast periodicity is also in conflict
with a compact object merger scenario. These quasi-periodic components could,
however, be caused by equidistant emitting regions in the magnetosphere of a
magnetar. The sub-millisecond spacing of the components in FRB 20201020A, the
smallest observed so far in a one-off FRB, may rule out both neutron-star
rotation and binary mergers as the direct source of quasi-periodic FRBs.


title:
On higher order passivity preserving schemes for nonlinear Maxwell's
  equations

abstract:
  We present two strategies for designing passivity preserving higher order
discretization methods for Maxwell's equations in nonlinear Kerr-type media.
Both approaches are based on variational approximation schemes in space and
time. This allows to rigorously prove energy conservation or dissipation, and
thus passivity, on the fully discrete level. For linear media, the proposed
methods coincide with certain combinations of mixed finite element and implicit
Runge-Kutta schemes. The order optimal convergence rates, which can thus be
expected for linear problems, are also observed for nonlinear problems in the
numerical tests.


title: Deep Koopman Operator with Control for Nonlinear Systems

abstract:
  Recently Koopman operator has become a promising data-driven tool to
facilitate real-time control for unknown nonlinear systems. It maps nonlinear
systems into equivalent linear systems in embedding space, ready for real-time
linear control methods. However, designing an appropriate Koopman embedding
function remains a challenging task. Furthermore, most Koopman-based algorithms
only consider nonlinear systems with linear control input, resulting in lousy
prediction and control performance when the system is fully nonlinear with the
control input. In this work, we propose an end-to-end deep learning framework
to learn the Koopman embedding function and Koopman Operator together to
alleviate such difficulties. We first parameterize the embedding function and
Koopman Operator with the neural network and train them end-to-end with the
K-steps loss function. We then design an auxiliary control network to encode
the nonlinear state-dependent control term to model the nonlinearity in control
input. For linear control, this encoded term is considered the new control
variable instead, ensuring the linearity of the embedding space. Then we deploy
Linear Quadratic Regulator (LQR) on the linear embedding space to derive the
optimal control policy and decode the actual control input from the control
net. Experimental results demonstrate that our approach outperforms other
existing methods, reducing the prediction error by order-of-magnitude and
achieving superior control performance in several nonlinear dynamic systems
like damping pendulum, CartPole, and 7 Dof robotic manipulator.


title: Should You Mask 15% in Masked Language Modeling?

abstract:
  Masked language models conventionally use a masking rate of 15% due to the
belief that more masking would provide insufficient context to learn good
representations, and less masking would make training too expensive.
Surprisingly, we find that masking up to 40% of input tokens can outperform the
15% baseline, and even masking 80% can preserve most of the performance, as
measured by fine-tuning on downstream tasks. Increasing the masking rates has
two distinct effects, which we investigate through careful ablations: (1) A
larger proportion of input tokens are corrupted, reducing the context size and
creating a harder task, and (2) models perform more predictions, which benefits
training. We observe that larger models in particular favor higher masking
rates, as they have more capacity to perform the harder task. We also connect
our findings to sophisticated masking schemes such as span masking and PMI
masking, as well as BERT's curious 80-10-10 corruption strategy, and find that
simple uniform masking with [MASK] replacements can be competitive at higher
masking rates. Our results contribute to a better understanding of masked
language modeling and point to new avenues for efficient pre-training.


title: Recoloring Unit Interval Graphs with Logarithmic Recourse Budget

abstract:
  In this paper we study the problem of coloring a unit interval graph which
changes dynamically. In our model the unit intervals are added or removed one
at the time, and have to be colored immediately, so that no two overlapping
intervals share the same color. After each update only a limited number of
intervals is allowed to be recolored. The limit on the number of recolorings
per update is called the recourse budget. In this paper we show, that if the
graph remains $k$-colorable at all times, and the updates consist of insertions
only, then we can achieve the amortized recourse budget of $O(k^7 \log n)$
while maintaining a proper coloring with $k$ colors. This is an exponential
improvement over the result in [Bosek et al., Recoloring Interval Graphs with
Limited Recourse Budget. SWAT 2020] in terms of both $k$ and $n$. We complement
this result by showing the lower bound of $\Omega(n)$ on the amortized recourse
budget in the fully dynamic setting. Our incremental algorithm can be
efficiently implemented.
  As a byproduct of independent interest we include a new result on coloring
proper circular arc graphs. Let $L$ be the maximum number of arcs intersecting
in one point for some set of unit circular arcs $\mathcal{A}$. We show that if
there is a set $\mathcal{A}'$ of non-intersecting unit arcs of size $L^2-1$
such that $\mathcal{A} \cup \mathcal{A}'$ does not contain $L+1$ arcs
intersecting in one point, then it is possible to color $\mathcal{A}$ with $L$
colors. This complements the work on unit circular arc coloring, which
specifies sufficient conditions needed to color $\mathcal{A}$ with $L+1$ colors
or more.


title: Sparse Markov Models for High-dimensional Inference

abstract:
  Finite order Markov models are theoretically well-studied models for
dependent discrete data. Despite their generality, application in empirical
work when the order is large is rare. Practitioners avoid using higher order
Markov models because (1) the number of parameters grow exponentially with the
order and (2) the interpretation is often difficult. Mixture of transition
distribution models (MTD) were introduced to overcome both limitations. MTD
represent higher order Markov models as a convex mixture of single step Markov
chains, reducing the number of parameters and increasing the interpretability.
Nevertheless, in practice, estimation of MTD models with large orders are still
limited because of curse of dimensionality and high algorithm complexity. Here,
we prove that if only few lags are relevant we can consistently and efficiently
recover the lags and estimate the transition probabilities of high-dimensional
MTD models. The key innovation is a recursive procedure for the selection of
the relevant lags of the model. Our results are based on (1) a new structural
result of the MTD and (2) an improved martingale concentration inequality. We
illustrate our method using simulations.


title: Near-Shortest Path Routing in Hybrid Communication Networks

abstract:
  Hybrid networks, i.e., networks that leverage different means of
communication, become ever more widespread. To allow theoretical study of such
networks, [Augustine et al., SODA'20] introduced the $\mathsf{HYBRID}$ model,
which is based on the concept of synchronous message passing and uses two
fundamentally different principles of communication: a local mode, which allows
every node to exchange one message per round with each neighbor in a local
communication graph; and a global mode where any pair of nodes can exchange
messages, but only few such exchanges can take place per round.
  A sizable portion of the previous research for the $\mathsf{HYBRID}$ model
revolves around basic communication primitives and computing distances or
shortest paths in networks. In this paper, we extend this study to a related
fundamental problem of computing compact routing schemes for near-shortest
paths in the local communication graph. We demonstrate that, for the case where
the local communication graph is a unit-disc graph with $n$ nodes that is
realized in the plane and has no radio holes, we can deterministically compute
a routing scheme that has constant stretch and uses labels and local routing
tables of size $O(\log n)$ bits in only $O(\log n)$ rounds.


title: Cooling of nanomechanical vibrations by Andreev injection

abstract:
  A nanoelectromechanical weak link composed of a carbon nanotube suspended
between two normal electrodes in a gap between two superconducting leads is
considered. The nanotube is treated as a movable single-level quantum dot in
which the position-dependent superconducting order parameter is induced due to
the Cooper pair tunneling. We show that electron tunneling processes
significantly affect the state of the mechanical subsystem. We found that at a
given direction of the applied voltage between the electrodes, the stationary
state of the mechanical subsystem has a Boltzmann form with an effective
temperature depended on the parameters of the device. As this takes place, the
effective temperature can reach significantly small values (cooling effect). We
also demonstrate that nanotube fluctuations strongly affect the dc current
through the system. The latter can be used to probe the predicted effects in an
experiment.


title:
360 Depth Estimation in the Wild -- The Depth360 Dataset and the SegFuse
  Network

abstract:
  Single-view depth estimation from omnidirectional images has gained
popularity with its wide range of applications such as autonomous driving and
scene reconstruction. Although data-driven learning-based methods demonstrate
significant potential in this field, scarce training data and ineffective 360
estimation algorithms are still two key limitations hindering accurate
estimation across diverse domains. In this work, we first establish a
large-scale dataset with varied settings called Depth360 to tackle the training
data problem. This is achieved by exploring the use of a plenteous source of
data, 360 videos from the internet, using a test-time training method that
leverages unique information in each omnidirectional sequence. With novel
geometric and temporal constraints, our method generates consistent and
convincing depth samples to facilitate single-view estimation. We then propose
an end-to-end two-branch multi-task learning network, SegFuse, that mimics the
human eye to effectively learn from the dataset and estimate high-quality depth
maps from diverse monocular RGB images. With a peripheral branch that uses
equirectangular projection for depth estimation and a foveal branch that uses
cubemap projection for semantic segmentation, our method predicts consistent
global depth while maintaining sharp details at local regions. Experimental
results show favorable performance against the state-of-the-art methods.


title:
Towards Identifying Social Bias in Dialog Systems: Frame, Datasets, and
  Benchmarks

abstract:
  The research of open-domain dialog systems has been greatly prospered by
neural models trained on large-scale corpora, however, such corpora often
introduce various safety problems (e.g., offensive languages, biases, and toxic
behaviors) that significantly hinder the deployment of dialog systems in
practice. Among all these unsafe issues, addressing social bias is more complex
as its negative impact on marginalized populations is usually expressed
implicitly, thus requiring normative reasoning and rigorous analysis. In this
paper, we focus our investigation on social bias detection of dialog safety
problems. We first propose a novel Dial-Bias Frame for analyzing the social
bias in conversations pragmatically, which considers more comprehensive
bias-related analyses rather than simple dichotomy annotations. Based on the
proposed framework, we further introduce CDail-Bias Dataset that, to our
knowledge, is the first well-annotated Chinese social bias dialog dataset. In
addition, we establish several dialog bias detection benchmarks at different
label granularities and input types (utterance-level and context-level). We
show that the proposed in-depth analyses together with these benchmarks in our
Dial-Bias Frame are necessary and essential to bias detection tasks and can
benefit building safe dialog systems in practice.


title: On Locally Conformally K\" ahler metrics on Oeljeklaus-Toma Manifolds

abstract:
  We show that Oeljeklaus-Toma manifolds $X(K, U)$ where $K$ is a number field
of signature $(s, t)$ such that $s\geq 1,$ $t\geq 2$ and $s\geq 2t$ admit no
lck metric. Combined with the earlier results by K. Oeljeklaus - M. Toma and A.
Dubickas this completely solves the problem of existence of LCK metrics on
Oeljeklaus-Toma manifolds.


title:
Elastic pion-proton and pion-pion scattering at high energies in
  holographic QCD

abstract:
  The total and differential cross sections of high energy pion-proton and
pion-pion scattering are investigated in a holographic QCD model, focusing on
the Regge regime in which the Pomeron exchange gives the dominant contribution
to those cross sections. The Pomeron is described by the Reggeized spin-2
particle propagator, and the hadron-Pomeron couplings are given by
gravitational form factors of the hadrons, which are obtained from the
bottom-up AdS/QCD models. Since the parameters, which characterize the Pomeron
trajectory, have been determined in a preceding study of the proton-proton
scattering, the pion-proton cross sections can be expressed with a single
adjustable parameter. Once this parameter is determined by the experimental
data of the pion-proton total cross section, its differential cross section and
the pion-pion cross sections can be calculated without any additional
parameter. Although the currently available data are limited, it is found that
our calculation is consistent with those. Our predictions are explicitly shown,
and the present model can be tested at the future experimental facilities.


title:
Stationary probability measures on projective spaces for block-Lyapunov
  dominated systems

abstract:
  Given a finite-dimensional real vector space $V$, a probability measure $\mu$
on $\operatorname{PGL}(V)$ and a $\mu$-invariant subspace $W$, under a
block-Lyapunov contraction assumption, we prove existence and uniqueness of
lifts to $P(V)\setminus P(W)$ of stationary probability measures on the
quotient $P(V/W)$. In the other direction, i.e. under block-Lyapunov expansion,
we prove that stationary measures on $P(V/W)$ have lifts if any only if the
group generated by the support of $\mu$ stabilizes a subspace $W'$ not
contained in $W$ and exhibiting a faster growth than on $W \cap W'$. These
refine the description of stationary probability measures on projective spaces
as given by Furstenberg, Kifer and Hennion, and under the same assumptions,
extend corresponding results by Aoun, Benoist, Bru\`{e}re, Guivarc'h, and
others.


title:
Magnetism of the 2D honeycomb layered Na2Ni2TeO6 compound driven by
  intermediate Na-layer crystal-structure

abstract:
  The microscopic spin-spin correlations in the 2D layered spin-1 honeycomb
lattice compound Na2Ni2TeO6 have been investigated by neutron diffraction and
inelastic neutron scattering. The honeycomb lattice of spin-1 Ni2+ ions, within
the crystallographic ab planes, are well separated along the c axis by an
intermediate Na layer whose crystal structure contains chiral nuclear density
distributions of Na ions. The chirality of the alternating Na layers is
opposite. Such alternating chirality of the Na layer dictates the magnetic
periodicity along the c axis where an up-up-down-down spin arrangement of the
in-plane zigzag AFM structure is found. Besides, the above described
commensurate (CM) zigzag AFM order state is found to coexist with an
incommensurate (ICM) AFM state below the TN ~ 27.5 K. The ICM state is found to
appear at much higher temperature ~ 50 K and persists down to lowest measured
temperature of 1.7 K. Our reverse Monte Carlo (RMC) analysis divulges a two
dimensional (2D) magnetic correlations (within the ab plane) of the ICM AFM
state over the entire temperature range 1.7-50 K. Further, the spin-Hamiltonian
has been determined by carrying out inelastic neutron scattering experiments
and subsequent linear spin-wave theory analysis which reveals the presence of
competing inplane exchange interactions up to 3 rd nearest neighbours
consistent with the zigzag AFM ground state, and weak interplanar interaction
as well as a weak single-ion-anisotropy. The values of the exchange constants
yield that Na2Ni2TeO6 is situated well inside the zigzag AFM phase (spans over
a wide ranges of J2/J1 and J3/J1 values) in the theoretical phase diagram. The
present study, thus, provides a detailed microscopic understanding of the
magnetic correlations and divulges the intertwining magneto-structural
correlations.


title:
Revisiting the single-saddle model for the $\beta$-relaxation of
  supercooled liquids

abstract:
  The dynamics of glass-forming liquids display several outstanding features,
such as two-step relaxation and dynamic heterogeneities, which are difficult to
predict quantitatively from first principles. In this work, we revisit a simple
theoretical model of the $\beta$-relaxation, i.e., the first step of the
relaxation dynamics. The model, first introduced by Cavagna et al., describes
the dynamics of the system in the neighborhood of a saddle point of the
potential energy surface. We extend the model to account for density-density
correlation functions and for the 4-point dynamic susceptibility. We obtain
analytical results for a simple schematic model, making contact with related
results for p-spin models and with the predictions of inhomogeneous
mode-coupling theory. Building on recent computational advances, we also
explicitly compare the model predictions against overdamped Langevin dynamics
simulations of a glass-forming liquid close to the mode-coupling crossover. The
agreement is quantitative at the level of single-particle dynamic properties
only up to the early $\beta$-regime. Due to its inherent harmonic
approximation, however, the model is unable to predict the dynamics on the time
scale relevant for structural relaxation. Nonetheless, our analysis suggests
that the agreement with the simulations may be largely improved if the modes'
spatial localization is properly taken into account.


title: Mechanization of LAGC Semantics in Isabelle

abstract:
  Formal programming language semantics are imperative when trying to verify
properties of programs in an automated manner. Using a new approach, Din et al.
strengthen the ability of reasoning about concurrent programs by proposing a
modular trace semantics, which can flexibly adapt to the most prominent
imperative programming language paradigms. These semantics decouple the
evaluation in the local environments from the evaluation in the global
environment by generating abstract, symbolic traces for the individual, local
systems. The traces are then composed and concretized, resulting in global
traces for the global system. Hence, these semantics are called Locally
Abstract, Globally Concrete (LAGC).
  In this work, we present a formalization of the LAGC semantics in the popular
theorem proving environment Isabelle/HOL. The given model is based on the prior
work on the theory of LAGC semantics by Din et al. and includes formalizations
of the basic theorems, the LAGC semantics for the While Language (WL), as well
as the LAGC semantics for an extended version of the While Language (WLEXT). We
furthermore use our Isabelle model in order to provide formal proofs for
several advanced properties of the LAGC semantics, which have not been analyzed
in the original paper.
  Whilst the main goal of the work was to formalize the LAGC semantics in a
mathematically rigorous manner, we also achieve a high level of proof
automatization and manage to contribute an efficient code-generation for the
computation of program traces. As the formalization of the semantics is highly
modular, the given theories could in the future be extended with even more
sophisticated programming language paradigms.


title: The quantale of order-preserving maps

abstract:
  In this paper, two new composition operations are defined among the
order-preserving maps. They can act on order-preserving maps like the usual
composition operation. They are coincide with the usual composition operation
when the order-preserving maps are sup-preserving maps or meet-preserving maps.
The usual composition operation can't endow the set $L^{L}$ of order preserving
maps on a complete lattices $L$ with quantale or co-quantale structures.
Luckily, two new operations can endow $L^{L}$ with a quantale and a co-quantale
structures respectively, whenever $L$ is a completely distributive lattice.


title: Data-Driven Control of Event- and Self-Triggered Discrete-Time Systems

abstract:
  The present paper considers the model-based and data-driven control of
unknown linear time-invariant discrete-time systems under event-triggering and
self-triggering transmission schemes. To this end, we begin by presenting a
dynamic event-triggering scheme (ETS) based on periodic sampling, and a
discrete-time looped-functional (DLF) approach, through which a model-based
stability condition is derived. Combining the model-based condition with a
recent data-based system representation, a data-driven stability criterion in
the form of linear matrix inequalities (LMIs) is established, which offers a
way of co-designing the ETS matrix and the controller. To further alleviate the
sampling burden of ETS due to its continuous/periodic detection, a
self-triggering scheme (STS) is developed. Leveraging pre-collected input-state
data, an algorithm for predicting the next transmission instant is given, while
achieving system stability. A co-design method of the ETS matrix and the
controller under the proposed data-driven STS is given. Finally, numerical
simulations showcase the efficacy of ETS and STS in reducing data transmissions
as well as of the proposed co-design methods.


title:
Electric Field Influenced Coordinate Jump of the Guiding Center and
  Magnetotransport

abstract:
  We derived an electrical current formula in the presence of a strong out of
plane magnetic field and an in plane electric field, and within two dimensional
disordered systems. This current is originated from the guiding center
coordinate jump. At a strong magnetic field regime, the current can be pictured
as the migration of the center coordinates. During the electron impurity
scattering, the guiding centers suddenly shift their coordinates. Because of
the electric field, the coordinate shift accumulatively contributes to a
longitudinal current. During the scattering, the value of cyclotron radius
changes, which compensates for the change of the electric potential energy
during the coordinate jump. The diversion of cyclotron radius is the classical
manifestation of electric field dependent broadening and shifting of the Landau
levels.


title:
Toward Development of Machine Learned Techniques for Production of
  Compact Kinetic Models

abstract:
  Chemical kinetic models are an essential component in the development and
optimisation of combustion devices through their coupling to multi-dimensional
simulations such as computational fluid dynamics (CFD). Low-dimensional kinetic
models which retain good fidelity to the reality are needed, the production of
which requires considerable human-time cost and expert knowledge. Here, we
present a novel automated compute intensification methodology to produce
overly-reduced and optimised (compact) chemical kinetic models. This algorithm,
termed Machine Learned Optimisation of Chemical Kinetics (MLOCK),
systematically perturbs each of the four sub-models of a chemical kinetic model
to discover what combinations of terms results in a good model. A virtual
reaction network comprised of n species is first obtained using conventional
mechanism reduction. To counteract the imposed decrease in model performance,
the weights (virtual reaction rate constants) of important connections (virtual
reactions) between each node (species) of the virtual reaction network are
numerically optimised to replicate selected calculations across four sequential
phases. The first version of MLOCK, (MLOCK1.0) simultaneously perturbs all
three virtual Arrhenius reaction rate constant parameters for important
connections and assesses the suitability of the new parameters through
objective error functions, which quantify the error in each compact model
candidate's calculation of the optimisation targets, which may be comprised of
detailed model calculations and/or experimental data. MLOCK1.0 is demonstrated
by creating compact models for the archetypal case of methane air combustion.
It is shown that the NUGMECH1.0 detailed model comprised of 2,789 species is
reliably compacted to 15 species (nodes), whilst retaining an overall fidelity
of ~87% to the detailed model calculations, outperforming the prior
state-of-art.

categories: physics.chem-ph cs.DC cs.LG physics.comp-ph stat.ML


title:
Decoding the bifurcated red-giant branch as a tracer of multiple stellar
  populations in the young Large Magellanic Cloud cluster NGC 2173

abstract:
  Multiple stellar populations (MPs) representing star-to-star light-element
abundance variations are common in nearly all ancient Galactic globular
clusters. Here we provide the strongest evidence yet that the populous, ~ 1.7
Gyr-old Large Magellanic Cloud cluster NGC 2173 also exhibits light-element
abundance variations. Thus, our results suggest that NGC 2173 is the youngest
cluster for which MPs have been confirmed to date. Our conclusion is based on
the distinct bifurcation at the tip of its red-giant branch in high-quality
color--magnitude diagrams generated from Hubble Space Telescope imaging
observations. Our results are further supported by a detailed analysis of
'pseudo-$UBI$' maps, which reveal clear evidence of a bimodality in the
cluster's red-giant-branch color distribution. Young clusters in the Magellanic
Clouds can provide critical insights into galaxy evolution histories. Our
discovery of MPs in NGC 2173 suggests that ancient Galactic globular clusters
and young massive clusters might share a common formation process.


title:
Continuously Learning to Detect People on the Fly: A Bio-inspired Visual
  System for Drones

abstract:
  This paper demonstrates for the first time that a biologically-plausible
spiking neural network (SNN) equipped with Spike-Timing-Dependent Plasticity
(STDP) can continuously learn to detect walking people on the fly using
retina-inspired, event-based cameras. Our pipeline works as follows. First, a
short sequence of event data ($<2$ minutes), capturing a walking human by a
flying drone, is forwarded to a convolutional SNNSTDP system which also
receives teacher spiking signals from a readout (forming a semi-supervised
system). Then, STDP adaptation is stopped and the learned system is assessed on
testing sequences. We conduct several experiments to study the effect of key
parameters in our system and to compare it against conventionally-trained CNNs.
We show that our system reaches a higher peak $F_1$ score (+19%) compared to
CNNs with event-based camera frames, while enabling on-line adaptation.