title:
An exploration of the performances achievable by combining unsupervised
  background subtraction algorithms

abstract:
  Background subtraction (BGS) is a common choice for performing motion
detection in video. Hundreds of BGS algorithms are released every year, but
combining them to detect motion remains largely unexplored. We found that
combination strategies allow to capitalize on this massive amount of available
BGS algorithms, and offer significant space for performance improvement. In
this paper, we explore sets of performances achievable by 6 strategies
combining, pixelwise, the outputs of 26 unsupervised BGS algorithms, on the
CDnet 2014 dataset, both in the ROC space and in terms of the F1 score. The
chosen strategies are representative for a large panel of strategies, including
both deterministic and non-deterministic ones, voting and learning. In our
experiments, we compare our results with the state-of-the-art combinations
IUTIS-5 and CNN-SFC, and report six conclusions, among which the existence of
an important gap between the performances of the individual algorithms and the
best performances achievable by combining them.


title: Time zero regularity of Ricci flow

abstract:
  We consider the problem of when a smooth Ricci flow, for positive time, that
attains smooth initial data in a weak sense must be smooth down to the initial
time. We develop an example where this fails. We prove a positive result in the
case that the flow satisfies a lower IC1 curvature bound, equivalent to a lower
Ricci bound in three dimensions. As an application, we prove that
Gromov-Hausdorff limits of WPIC1 manifolds are WPIC1.


title:
Beyond Bj{\o}ntegaard: Limits of Video Compression Performance
  Comparisons

abstract:
  For 20 years, the gold standard to evaluate the performance of video codecs
is to calculate average differences between ratedistortion curves, also called
the "Bj{\o}ntegaard Delta". With the help of this tool, the compression
performance of codecs can be compared. In the past years, we could observe that
the calculus was also deployed for other metrics than bitrate and distortion in
terms of peak signal-to-noise ratio, for example other quality metrics such as
video multi-method assessment fusion or hardware-dependent metrics such as the
decoding energy. However, it is unclear whether the Bj{\o}ntegaard Delta is a
valid way to evaluate these metrics. To this end, this paper reviews several
interpolation methods and evaluates their accuracy using different
performancemetrics. As a result, we propose to use a novel approach based on
Akima interpolation, which returns the most accurate results for a large
variety of performance metrics. The approximation accuracy of this new method
is determined to be below a bound of 1.5%.


title: Composing Complex and Hybrid AI Solutions

abstract:
  Progress in several areas of computer science has been enabled by comfortable
and efficient means of experimentation, clear interfaces, and interchangable
components, for example using OpenCV for computer vision or ROS for robotics.
We describe an extension of the Acumos system towards enabling the above
features for general AI applications. Originally, Acumos was created for
telecommunication purposes, mainly for creating linear pipelines of machine
learning components. Our extensions include support for more generic components
with gRPC/Protobuf interfaces, automatic orchestration of graphically assembled
solutions including control loops, sub-component topologies, and event-based
communication,and provisions for assembling solutions which contain user
interfaces and shared storage areas. We provide examples of deployable
solutions and their interfaces. The framework is deployed at
http://aiexp.ai4europe.eu/ and its source code is managed as an open source
Eclipse project.


title:
Sparse Sampling and Completion for Light Transport in VPL-based
  Rendering

abstract:
  The many-light formulation provides a general framework for rendering various
illumination effects using hundreds of thousands of virtual point lights
(VPLs). To efficiently gather the contributions of the VPLs, lightcuts and its
extensions cluster the VPLs, which implicitly approximates the lighting matrix
with some representative blocks similar to vector quantization. In this paper,
we propose a new approximation method based on the previous lightcut method and
a low-rank matrix factorization model. As many researchers pointed out, the
lighting matrix is low rank, which implies that it can be completed from a
small set of known entries.
  We first generate a conservative global light cut with bounded error and
partition the lighting matrix into slices by the coordinate and normal of the
surface points using the method of lightslice. Then we perform two passes of
randomly sampling on each matrix slice. In the first pass, uniformly
distributed random entries are sampled to coarsen the global light cut, further
clustering the similar light for the spatially localized surface points of the
slices. In the second pass, more entries are sampled according to the
possibility distribution function estimated from the first sampling result.
Then each matrix slice is factorized into a product of two smaller low-rank
matrices constrained by the sampled entries, which delivers a completion of the
lighting matrix. The factorized form provides an additional speedup for adding
up the matrix columns which is more GPU friendly. Compared with the previous
lightcut based methods, we approximate the lighting matrix with some signal
specialized bases via factorization. The experimental results shows that we can
achieve significant acceleration than the state of the art many-light methods.


title: Screening Gender Transfer in Neural Machine Translation

abstract:
  This paper aims at identifying the information flow in state-of-the-art
machine translation systems, taking as example the transfer of gender when
translating from French into English. Using a controlled set of examples, we
experiment several ways to investigate how gender information circulates in a
encoder-decoder architecture considering both probing techniques as well as
interventions on the internal representations used in the MT system. Our
results show that gender information can be found in all token representations
built by the encoder and the decoder and lead us to conclude that there are
multiple pathways for gender transfer.


title: Moduli of vector bundles on primitive multiple schemes

abstract:
  A primitive multiple scheme is a Cohen-Macaulay scheme $Y$ such that the
associated reduced scheme $X=Y_{red}$ is smooth, irreducible, and that $Y$ can
be locally embedded in a smooth variety of dimension $\dim(X)+1$. If $n$ is the
multiplicity of $Y$, there is a canonical filtration $X=X_1\subset
X_2\subset\cdots\subset X_n=Y$, such that $X_i$ is a primitive multiple scheme
of multiplicity $i$. The simplest example is the trivial primitive multiple
scheme of multiplicity $n$ associated to a line bundle $L$ on $X$: it is the
$n$-th infinitesimal neighborhood of $X$, embedded in the line bundle $L^*$ by
the zero section.
  The main subject of this paper is the construction and properties of fine
moduli spaces of vector bundles on primitive multiple schemes. Suppose that
$Y=X_n$ is of multiplicity $n$, and can be extended to $X_{n+1}$ of
multiplicity $n+1$, and let $M_n$ a fine moduli space of vector bundles on
$X_n$. With suitable hypotheses, we construct a fine moduli space $M_{n+1}$ for
the vector bundles on $X_{n+1}$ whose restriction to $X_n$ belongs to $M_n$. It
is an affine bundle over the subvariety $N_n\subset M_n$ of bundles that can be
extended to $X_{n+1}$. In general this affine bundle is not banal. This applies
in particular to Picard groups.
  We give also many new examples of primitive multiple schemes $Y$ such that
the dualizing sheaf $\omega_Y$ is trivial (in the case of surfaces they are
called K3-carpets).


title: Towards Learning Causal Representations from Multi-Instance Bags

abstract:
  Although humans can easily identify the object of interest from groups of
examples using group-level labels, most of the existing machine learning
algorithms can only learn from individually labeled examples. Multi-instance
learning (MIL) is a type of weakly supervised learning that deals with objects
represented as groups of instances, and is theoretically capable of predicting
instance labels from group-level supervision. Unfortunately, most existing MIL
algorithms focus on improving the performances of group label predictions and
cannot be used to accurately predict instance labels. In this work, we propose
the TargetedMIL algorithm, which learns semantically meaningful representations
that can be interpreted as causal to the object of interest. Utilizing the
inferred representations, TargetedMIL excels at instance label predictions from
group-level labels. Qualitative and quantitative evaluations on various
datasets demonstrate the effectiveness of TargetedMIL.


title:
NeuralKG: An Open Source Library for Diverse Representation Learning of
  Knowledge Graphs

abstract:
  NeuralKG is an open-source Python-based library for diverse representation
learning of knowledge graphs. It implements three different series of Knowledge
Graph Embedding (KGE) methods, including conventional KGEs, GNN-based KGEs, and
Rule-based KGEs. With a unified framework, NeuralKG successfully reproduces
link prediction results of these methods on benchmarks, freeing users from the
laborious task of reimplementing them, especially for some methods originally
written in non-python programming languages. Besides, NeuralKG is highly
configurable and extensible. It provides various decoupled modules that can be
mixed and adapted to each other. Thus with NeuralKG, developers and researchers
can quickly implement their own designed models and obtain the optimal training
methods to achieve the best performance efficiently. We built an website in
http://neuralkg.zjukg.cn to organize an open and shared KG representation
learning community. The source code is all publicly released at
https://github.com/zjukg/NeuralKG.


title:
Spatially-Resolved Band Gap and Dielectric Function in 2D Materials from
  Electron Energy Loss Spectroscopy

abstract:
  The electronic properties of two-dimensional (2D) materials depend
sensitively on the underlying atomic arrangement down to the monolayer level.
Here we present a novel strategy for the determination of the band gap and
complex dielectric function in 2D materials achieving a spatial resolution down
to a few nanometers. This approach is based on machine learning techniques
developed in particle physics and makes possible the automated processing and
interpretation of spectral images from electron energy-loss spectroscopy
(EELS). Individual spectra are classified as a function of the thickness with
$K$-means clustering and then used to train a deep-learning model of the
zero-loss peak background. As a proof-of-concept we assess the band gap and
dielectric function of InSe flakes and polytypic WS$_2$ nanoflowers, and
correlate these electrical properties with the local thickness. Our flexible
approach is generalizable to other nanostructured materials and to
higher-dimensional spectroscopies, and is made available as a new release of
the open-source EELSfitter framework.


title: Plectic points and Hida-Rankin p-adic L-functions

abstract:
  Plectic points were introduced by Fornea and Gehrmann as certain tensor
products of local pointson elliptic curves over arbitrary number fields $F$. In
rank $r\leq [F:\mathbb{Q}]$-situations, they conjecturally come from p-adic
regulators of basis of the Mordell-Weil group defined over dihedral extensions
of $F$. In this article we define two variable anticyclotomic $p$-adic
L-functions attached to a family of overconvergent modular symbols defined over
$F$ and a quadratic extension $K/F$. Their restriction to the weight space
provide Hida-Rankin $p$-adic L-functions. If such a family passes through an
overconvergent modular symbol attached to a modular elliptic curve $E/F$, we
obtain a $p$-adic Gross-Zagier formula that computes higher derivatives of such
Hida-Rankin $p$-adic L-functions in terms of plectic points. This result
generalizes that of Bertolini and Darmon, which has been key to demonstrating
the rationality of Darmon points.


title: On the Use of Torque Measurement in Centroidal State Estimation

abstract:
  State of the art legged robots are either capable of measuring torque at the
output of their drive systems, or have transparent drive systems which enable
the computation of joint torques from motor currents. In either case, this
sensor modality is seldom used in state estimation. In this paper, we propose
to use joint torque measurements to estimate the centroidal states of legged
robots. To do so, we project the whole-body dynamics of a legged robot into the
nullspace of the contact constraints, allowing expression of the dynamics
independent of the contact forces. Using the constrained dynamics and the
centroidal momentum matrix, we are able to directly relate joint torques and
centroidal states dynamics. Using the resulting model as the process model of
an Extended Kalman Filter (EKF), we fuse the torque measurement in the
centroidal state estimation problem. Through real-world experiments on a
quadruped robot with different gaits, we demonstrate that the estimated
centroidal states from our torque-based EKF drastically improve the estimation
of these quantities compared to direct computation.


title:
Mining Naturally-occurring Corrections and Paraphrases from Wikipedia's
  Revision History

abstract:
  Naturally-occurring instances of linguistic phenomena are important both for
training and for evaluating automatic processes on text. When available in
large quantities, they also prove interesting material for linguistic studies.
In this article, we present a new resource built from Wikipedia's revision
history, called WiCoPaCo (Wikipedia Correction and Paraphrase Corpus), which
contains numerous editings by human contributors, including various corrections
and rewritings. We discuss the main motivations for building such a resource,
describe how it was built and present initial applications on French.


title: A Survey of Multilingual Models for Automatic Speech Recognition

abstract:
  Although Automatic Speech Recognition (ASR) systems have achieved human-like
performance for a few languages, the majority of the world's languages do not
have usable systems due to the lack of large speech datasets to train these
models. Cross-lingual transfer is an attractive solution to this problem,
because low-resource languages can potentially benefit from higher-resource
languages either through transfer learning, or being jointly trained in the
same multilingual model. The problem of cross-lingual transfer has been well
studied in ASR, however, recent advances in Self Supervised Learning are
opening up avenues for unlabeled speech data to be used in multilingual ASR
models, which can pave the way for improved performance on low-resource
languages. In this paper, we survey the state of the art in multilingual ASR
models that are built with cross-lingual transfer in mind. We present best
practices for building multilingual models from research across diverse
languages and techniques, discuss open questions and provide recommendations
for future work.


title: Challenges and Opportunities for Machine Learning in Fluid Mechanics

abstract:
  Big data and machine learning are driving comprehensive economic and social
transformations and are rapidly re-shaping the toolbox and the methodologies of
applied scientists. Machine learning tools are designed to learn functions from
data with little to no need of prior knowledge. As continuous developments in
experimental and numerical methods improve our ability to collect high-quality
data, machine learning tools become increasingly viable and promising also in
disciplines rooted in physical principles. These notes explore how machine
learning can be integrated and combined with more classic methods in fluid
dynamics. After a brief review of the machine learning landscape, we show how
many problems in fluid mechanics can be framed as machine learning problems and
we explore challenges and opportunities. We consider several relevant
applications: aeroacoustic noise prediction, turbulence modelling,
reduced-order modelling and forecasting, meshless integration of (partial)
differential equations, super-resolution and flow control. While this list is
by no means exhaustive, the presentation will provide enough concrete examples
to offer perspectives on how machine learning might impact the way we do
research and learn from data.


title:
Learning to Liquidate Forex: Optimal Stopping via Adaptive Top-K
  Regression

abstract:
  We consider learning a trading agent acting on behalf of the treasury of a
firm earning revenue in a foreign currency (FC) and incurring expenses in the
home currency (HC). The goal of the agent is to maximize the expected HC at the
end of the trading episode by deciding to hold or sell the FC at each time step
in the trading episode. We pose this as an optimization problem, and consider a
broad spectrum of approaches with the learning component ranging from
supervised to imitation to reinforcement learning. We observe that most of the
approaches considered struggle to improve upon simple heuristic baselines. We
identify two key aspects of the problem that render standard solutions
ineffective - i) while good forecasts of future FX rates can be highly
effective in guiding good decisions, forecasting FX rates is difficult, and
erroneous estimates tend to degrade the performance of trading agents instead
of improving it, ii) the inherent non-stationary nature of FX rates renders a
fixed decision-threshold highly ineffective. To address these problems, we
propose a novel supervised learning approach that learns to forecast the top-K
future FX rates instead of forecasting all the future FX rates, and bases the
hold-versus-sell decision on the forecasts (e.g. hold if future FX rate is
higher than current FX rate, sell otherwise). Furthermore, to handle the
non-stationarity in the FX rates data which poses challenges to the i.i.d.
assumption in supervised learning methods, we propose to adaptively learn
decision-thresholds based on recent historical episodes. Through extensive
empirical evaluation, we show that our approach is the only approach which is
able to consistently improve upon a simple heuristic baseline. Further
experiments show the inefficacy of state-of-the-art statistical and
deep-learning-based forecasting methods as they degrade the performance of the
trading agent.


title: Convex hulls of stable random walks

abstract:
  We consider convex hulls of random walks whose steps belong to the domain of
attraction of a stable law in $\mathbb{R}^d$. We prove convergence of the
convex hull in the space of all convex and compact subsets of $\mathbb{R}^d$,
equipped with the Hausdorff distance, towards the convex hull spanned by a path
of the limit stable L\'{e}vy process. As an application, we establish
convergence of (expected) intrinsic volumes under some mild moment/structure
assumptions posed on the random walk.


title:
A note on the cluster set of the law of the iterated logarithm under
  sub-linear expectations

abstract:
  In this note, we establish a compact law of the iterated logarithm under the
upper capacity for independent and identically distributed random variables in
a sub-linear expectation space. For showing the result, a self-normalized law
of the iterated logarithm is established.


title: What is the speed limit of martensitic transformations?

abstract:
  Structural martensitic transformations enable various applications, which
range from high stroke actuation and sensing to energy efficient magnetocaloric
refrigeration and thermomagnetic energy harvesting. All these emerging
applications benefit from a fast transformation, but up to now the speed limit
of martensitic transformations has not been explored. Here, we demonstrate that
a martensite to austenite transformation can be completed in under ten
nanoseconds. We heat an epitaxial Ni-Mn-Ga film with a laser pulse and use
synchrotron diffraction to probe the influence of initial sample temperature
and overheating on transformation rate and ratio. We demonstrate that an
increase of thermal energy drives this transformation faster. Though the
observed speed limit of 2.5 x 10^{27} (Js)^{-1} per unit cell leaves plenty of
room for a further acceleration of applications, our analysis reveals that the
practical limit will be the energy required for switching. Our experiments
unveil that martensitic transformations obey similar speed limits as in
microelectronics, which are expressed by the Margolus-Levitin theorem.


title:
A Blockchain-Based Consent Mechanism for Access to Fitness Data in the
  Healthcare Context

abstract:
  Wearable fitness devices are widely used to track an individual's health and
physical activities to improve the quality of health services. These devices
sense a considerable amount of sensitive data processed by a centralized third
party. While many researchers have thoroughly evaluated privacy issues
surrounding wearable fitness trackers, no study has addressed privacy issues in
trackers by giving control of the data to the user. Blockchain is an emerging
technology with outstanding advantages in resolving consent management privacy
concerns. As there are no fully transparent, legally compliant solutions for
sharing personal fitness data, this study introduces an architecture for a
human-centric, legally compliant, decentralized and dynamic consent system
based on blockchain and smart contracts. Algorithms and sequence diagrams of
the proposed system's activities show consent-related data flow among various
agents, which are used later to prove the system's trustworthiness by
formalizing the security requirements. The security properties of the proposed
system were evaluated using the formal security modeling framework SeMF, which
demonstrates the feasibility of the solution at an abstract level based on
formal language theory. As a result, we have empirically proven that blockchain
technology is suitable for mitigating the privacy issues of fitness providers
by recording individuals' consent using blockchain and smart contracts.


title:
The moments of the maximum of normalized partial sums related to laws of
  the iterated logarithm under the sub-linear expectation

abstract:
  Let $\{X_n;n\ge 1\}$ be a sequence of independent and identically distributed
random variables on a sub-linear expectation space
$(\Omega,\mathscr{H},\widehat{\mathbb E})$, $S_n=X_1+\ldots+X_n$. We consider
the moments of $\max_{n\ge 1}|S_n|/\sqrt{2n\log\log n}$. The sufficient and
necessary conditions for the moments to be finite are given. As an application,
we obtain the law of the iterated logarithm for moving average processes of
independent and identically distributed random variables.


title: The discrete random energy model and one step replica symmetry breaking

abstract:
  We solve the random energy model when the energies of the configurations take
only integer values. In the thermodynamic limit, the average overlaps remain
size dependent and oscillate as the system size increases. While the extensive
part of the free energy can still be obtained by a standard replica calculation
with one step replica symmetry breaking, it is no longer possible to recover
the overlaps in this way. A possible way to adapt the replica approach is to
allow the sizes of the blocks in the Parisi matrix to fluctuate and to take
complex values.


title:
Model Predictive Control with Preview: Recursive Feasibility and
  Stability

abstract:
  This paper proposes a stabilising model predictive control (MPC) scheme with
preview information of disturbance for nonlinear systems. The proposed MPC
algorithm is able to not only reject disturbance by making use of disturbance
preview information as necessary, but also take advantage of the disturbance if
it is good for a control task. This is realised by taking into account both the
task (e.g. reference trajectory) and disturbance preview in the prediction
horizon when performing online optimisation. Conditions are established to
ensure recursive feasibility and stability under the disturbance. First the
disturbance within the horizon is augmented with the state to form a new
composite system and then the stage cost function is modified accordingly. With
the help of input-to-state stability theory, a terminal cost and a terminal
constraint are constructed and added to the MPC algorithm with preview to
guarantee its recursive feasibility and stability under a pre-bounded
disturbance. Numerical simulation results demonstrate the effectiveness of the
proposed MPC algorithm.


title: Spatio-Temporal Latent Graph Structure Learning for Traffic Forecasting

abstract:
  Accurate traffic forecasting, the foundation of intelligent transportation
systems (ITS), has never been more significant than nowadays due to the
prosperity of the smart cities and urban computing. Recently, Graph Neural
Network truly outperforms the traditional methods. Nevertheless, the most
conventional GNN based model works well while given a pre-defined graph
structure. And the existing methods of defining the graph structures focus
purely on spatial dependencies and ignored the temporal correlation. Besides,
the semantics of the static pre-defined graph adjacency applied during the
whole training progress is always incomplete, thus overlooking the latent
topologies that may fine-tune the model. To tackle these challenges, we
proposed a new traffic forecasting framework--Spatio-Temporal Latent Graph
Structure Learning networks (ST-LGSL). More specifically, the model employed a
graph generator based on Multilayer perceptron and K-Nearest Neighbor, which
learns the latent graph topological information from the entire data
considering both spatial and temporal dynamics. Furthermore, with the
initialization of MLP-kNN based on ground-truth adjacency matrix and similarity
metric in kNN, ST-LGSL aggregates the topologies focusing on geography and node
similarity. Additionally, the generated graphs act as the input of
spatio-temporal prediction module combined with the Diffusion Graph
Convolutions and Gated Temporal Convolutions Networks. Experimental results on
two benchmarking datasets in real world demonstrate that ST-LGSL outperforms
various types of state-of-art baselines.


title:
Local Intensity Order Transformation for Robust Curvilinear Object
  Segmentation

abstract:
  Segmentation of curvilinear structures is important in many applications,
such as retinal blood vessel segmentation for early detection of vessel
diseases and pavement crack segmentation for road condition evaluation and
maintenance. Currently, deep learning-based methods have achieved impressive
performance on these tasks. Yet, most of them mainly focus on finding powerful
deep architectures but ignore capturing the inherent curvilinear structure
feature (e.g., the curvilinear structure is darker than the context) for a more
robust representation. In consequence, the performance usually drops a lot on
cross-datasets, which poses great challenges in practice. In this paper, we aim
to improve the generalizability by introducing a novel local intensity order
transformation (LIOT). Specifically, we transfer a gray-scale image into a
contrast-invariant four-channel image based on the intensity order between each
pixel and its nearby pixels along with the four (horizontal and vertical)
directions. This results in a representation that preserves the inherent
characteristic of the curvilinear structure while being robust to contrast
changes. Cross-dataset evaluation on three retinal blood vessel segmentation
datasets demonstrates that LIOT improves the generalizability of some
state-of-the-art methods. Additionally, the cross-dataset evaluation between
retinal blood vessel segmentation and pavement crack segmentation shows that
LIOT is able to preserve the inherent characteristic of curvilinear structure
with large appearance gaps. An implementation of the proposed method is
available at https://github.com/TY-Shi/LIOT.


title:
Active Learning for Point Cloud Semantic Segmentation via
  Spatial-Structural Diversity Reasoning

abstract:
  The expensive annotation cost is notoriously known as the main constraint for
the development of the point cloud semantic segmentation technique. Active
learning methods endeavor to reduce such cost by selecting and labeling only a
subset of the point clouds, yet previous attempts ignore the spatial-structural
diversity of the selected samples, inducing the model to select clustered
candidates with similar shapes in a local area while missing other
representative ones in the global environment. In this paper, we propose a new
3D region-based active learning method to tackle this problem. Dubbed SSDR-AL,
our method groups the original point clouds into superpoints and incrementally
selects the most informative and representative ones for label acquisition. We
achieve the selection mechanism via a graph reasoning network that considers
both the spatial and structural diversities of superpoints. To deploy SSDR-AL
in a more practical scenario, we design a noise-aware iterative labeling
strategy to confront the "noisy annotation" problem introduced by the previous
"dominant labeling" strategy in superpoints. Extensive experiments on two point
cloud benchmarks demonstrate the effectiveness of SSDR-AL in the semantic
segmentation task. Particularly, SSDR-AL significantly outperforms the baseline
method and reduces the annotation cost by up to 63.0% and 24.0% when achieving
90% performance of fully supervised learning, respectively.


title: Empirical spectral processes for stationary state space processes

abstract:
  In this paper, we consider function-indexed normalized weighted integrated
periodograms for equidistantly sampled multivariate continuous-time state space
models which are multivariate continuous-time ARMA processes. Thereby, the
sampling distance is fixed and the driving L\'evy process has at least a finite
fourth moment. Under different assumptions on the function space and the
moments of the driving L\'evy process we derive a central limit theorem for the
function-indexed normalized weighted integrated periodogram. Either the
assumption on the function space or the assumption on the existence of moments
of the L\'evy process is weaker. Furthermore, we show the weak convergence in
both the space of continuous functions and in the dual space to a Gaussian
process and give an explicit representation of the covariance function. The
results can be used to derive the asymptotic behavior of the Whittle estimator
and to construct goodness-of-fit test statistics as the Grenander-Rosenblatt
statistic and the Cram\'er-von Mises statistic. We present the exact limit
distributions of both statistics and show their performance through a
simulation study.


title:
Why does maximum Tc occur at the cross-over from weak to strong
  electron-phonon coupling in high temperature superconductors?

abstract:
  In cuprate superconductors, a pronounced maximum of superconducting Tc is
observed in compounds that have an in-plane Cu-O distance close to ~1.92
Angstroms. On the other hand, direct measurements of the electron-phonon
coupling lambda as a function of Cu-O distance show a clear linear correlation,
implying that Tc is a strongly non-linear function of lambda. Conventional
superconductivity theories based on the electron-phonon interaction predict a
monotonic dependence of Tc on electron phonon coupling constant, which makes
them incompatible with the observed behaviour. The observed cross-over
behaviour as a function of lambda suggests that Tc occurs at the cross-over
from weak to strong coupling, which is also associated with the onset of
carrier localization. A coexistence, with a dynamical exchange of localized and
itinerant carriers in a two-component superconductivity scenario are in
agreement with the observed anomalous behavior and are suggested to be the key
to understanding the mechanism for achieving high Tc.


title: Effect of quantum jumps on non-Hermitian system

abstract:
  One among the possible realizations of non-Hermitian systems is based on open
quantum systems by omitting quantum jump terms in the master equation. This is
a good approximation at short time when the effect of quantum jumps are
ignorable. In this paper, by treating the quantum jumps as a perturbation, we
examine the effect of the quantum jumps on the non-Hermitian system. For this
purpose, we first derive an effective Hamiltonian on the basis of the master
equation to describe the dynamics of open quantum systems, then expand the
system state up to the first and second order in the quantum jumps. Finally, we
apply our theory to a dissipative two-level system and dissipative fermionic
superfluids. The effect of quantum jump on the dynamics of the two-level system
and the nonequilibrium phase transition of the fermionic superfluids is
demonstrated and discussed.


title: Actions over Core-closed Knowledge Bases

abstract:
  We present new results on the application of semantic- and knowledge-based
reasoning techniques to the analysis of cloud deployments. In particular, to
the security of Infrastructure as Code configuration files, encoded as
description logic knowledge bases. We introduce an action language to model
mutating actions; that is, actions that change the structural configuration of
a given deployment by adding, modifying, or deleting resources. We mainly focus
on two problems: the problem of determining whether the execution of an action,
no matter the parameters passed to it, will not cause the violation of some
security requirement (static verification), and the problem of finding
sequences of actions that would lead the deployment to a state where
(un)desirable properties are (not) satisfied (plan existence and plan
synthesis). For all these problems, we provide definitions, complexity results,
and decision procedures.


title: A sharp-interface mesoscopic model for dendritic growth

abstract:
  The grain envelope model (GEM) describes the growth of envelopes of dendritic
crystal grains during solidification. Numerically the growing envelopes are
usually tracked using an interface capturing method employing a phase field
equation on a fixed grid. Such an approach describes the envelope as a diffuse
interface, which can lead to numerical artefacts that are possibly detrimental.
In this work, we present a sharp-interface formulation of the GEM that
eliminates such artefacts and can thus track the envelope with high accuracy.
The new formulation uses an adaptive meshless discretization method to solve
the diffusion in the liquid around the grains. We use the ability of the
meshless method to operate on scattered nodes to accurately describe the
interface, i.e., the envelope. The proposed algorithm combines parametric
surface reconstruction, meshless discretization of parametric surfaces, global
solution construction procedure and partial differential operator approximation
using monomials as basis functions. The approach is demonstrated on a
two-dimensional h-adaptive solution of diffusive growth of dendrites and
assessed by comparison to a conventional diffuse-interface fixed-grid GEM.


title: Complexity of Deliberative Coalition Formation

abstract:
  Elkind et al. (AAAI, 2021) introduced a model for deliberative coalition
formation, where a community wishes to identify a strongly supported proposal
from a space of alternatives, in order to change the status quo. In their
model, agents and proposals are points in a metric space, agents' preferences
are determined by distances, and agents deliberate by dynamically forming
coalitions around proposals that they prefer over the status quo. The
deliberation process operates via k-compromise transitions, where agents from k
(current) coalitions come together to form a larger coalition in order to
support a (perhaps new) proposal, possibly leaving behind some of the
dissenting agents from their old coalitions. A deliberation succeeds if it
terminates by identifying a proposal with the largest possible support. For
deliberation in d dimensions, Elkind et al. consider two variants of their
model: in the Euclidean model, proposals and agent locations are points in R^d
and the distance is measured according to ||...||_2; and in the hypercube
model, proposals and agent locations are vertices of the d-dimensional
hypercube and the metric is the Hamming distance. They show that in the
continuous model 2-compromises are guaranteed to succeed, but in the discrete
model for deliberation to succeed it may be necessary to use k-compromises with
k >= d. We complement their analysis by (1) proving that in both models it is
hard to find a proposal with a high degree of support, and even a 2-compromise
transition may be hard to compute; (2) showing that a sequence of 2-compromise
transitions may be exponentially long; (3) strengthening the lower bound on the
size of the compromise for the d-hypercube model from d to 2^{\Omega(d)}.


title:
Evolutionary scheduling of university activities based on consumption
  forecasts to minimise electricity costs

abstract:
  This paper presents a solution to a predict then optimise problem which goal
is to reduce the electricity cost of a university campus. The proposed
methodology combines a multi-dimensional time series forecast and a novel
approach to large-scale optimization. Gradient-boosting method is applied to
forecast both generation and consumption time-series of the Monash university
campus for the month of November 2020. For the consumption forecasts we employ
log transformation to model trend and stabilize variance. Additional
seasonality and trend features are added to the model inputs when applicable.
The forecasts obtained are used as the base load for the schedule optimisation
of university activities and battery usage. The goal of the optimisation is to
minimize the electricity cost consisting of the price of electricity and the
peak electricity tariff both altered by the load from class activities and
battery use as well as the penalty of not scheduling some optional activities.
The schedule of the class activities is obtained through evolutionary
optimisation using the covariance matrix adaptation evolution strategy and the
genetic algorithm. This schedule is then improved through local search by
testing possible times for each activity one-by-one. The battery schedule is
formulated as a mixed-integer programming problem and solved by the Gurobi
solver. This method obtains the second lowest cost when evaluated against 6
other methods presented at an IEEE competition that all used mixed-integer
programming and the Gurobi solver to schedule both the activities and the
battery use.


title:
A Probabilistic Oracle Inequality and Quantification of Uncertainty of a
  modified Discrepancy Principle for Statistical Inverse Problems

abstract:
  In this note we consider spectral cut-off estimators to solve a statistical
linear inverse problem under arbitrary white noise. The truncation level is
determined with a recently introduced adaptive method based on the classical
discrepancy principle. We provide probabilistic oracle inequalities together
with quantification of uncertainty for general linear problems. Moreover, we
compare the new method to existing ones, namely early stopping sequential
discrepancy principle and the balancing principle, both theoretically and
numerically.


title: Context-Hierarchy Inverse Reinforcement Learning

abstract:
  An inverse reinforcement learning (IRL) agent learns to act intelligently by
observing expert demonstrations and learning the expert's underlying reward
function. Although learning the reward functions from demonstrations has
achieved great success in various tasks, several other challenges are mostly
ignored. Firstly, existing IRL methods try to learn the reward function from
scratch without relying on any prior knowledge. Secondly, traditional IRL
methods assume the reward functions are homogeneous across all the
demonstrations. Some existing IRL methods managed to extend to the
heterogeneous demonstrations. However, they still assume one hidden variable
that affects the behavior and learn the underlying hidden variable together
with the reward from demonstrations. To solve these issues, we present Context
Hierarchy IRL(CHIRL), a new IRL algorithm that exploits the context to scale up
IRL and learn reward functions of complex behaviors. CHIRL models the context
hierarchically as a directed acyclic graph; it represents the reward function
as a corresponding modular deep neural network that associates each network
module with a node of the context hierarchy. The context hierarchy and the
modular reward representation enable data sharing across multiple contexts and
state abstraction, significantly improving the learning performance. CHIRL has
a natural connection with hierarchical task planning when the context hierarchy
represents subtask decomposition. It enables to incorporate the prior knowledge
of causal dependencies of subtasks and make it capable of solving large complex
tasks by decoupling it into several subtasks and conquering each subtask to
solve the original task. Experiments on benchmark tasks, including a large
scale autonomous driving task in the CARLA simulator, show promising results in
scaling up IRL for tasks with complex reward functions.


title:
Bridging the Gap Between Patient-specific and Patient-independent
  Seizure Prediction via Knowledge Distillation

abstract:
  Objective. Deep neural networks (DNN) have shown unprecedented success in
various brain-machine interface (BCI) applications such as epileptic seizure
prediction. However, existing approaches typically train models in a
patient-specific fashion due to the highly personalized characteristics of
epileptic signals. Therefore, only a limited number of labeled recordings from
each subject can be used for training. As a consequence, current DNN based
methods demonstrate poor generalization ability to some extent due to the
insufficiency of training data. On the other hand, patient-independent models
attempt to utilize more patient data to train a universal model for all
patients by pooling patient data together. Despite different techniques
applied, results show that patient-independent models perform worse than
patient-specific models due to high individual variation across patients. A
substantial gap thus exists between patient-specific and patient-independent
models. In this paper, we propose a novel training scheme based on knowledge
distillation which makes use of a large amount of data from multiple subjects.
It first distills informative features from signals of all available subjects
with a pre-trained general model. A patient-specific model can then be obtained
with the help of distilled knowledge and additional personalized data.
Significance. The proposed training scheme significantly improves the
performance of patient-specific seizure predictors and bridges the gap between
patient-specific and patient-independent predictors. Five state-of-the-art
seizure prediction methods are trained on the CHB-MIT sEEG database with our
proposed scheme. The resulting accuracy, sensitivity, and false prediction rate
show that our proposed training scheme consistently improves the prediction
performance of state-of-the-art methods by a large margin.


title: Making Life More Confusing for Firefighters

abstract:
  It is well known that fighting a fire is a hard task. The Firefighter problem
asks how to optimally deploy firefighters to defend the vertices of a graph
from a fire. This problem is NP-Complete on all but a few classes of graphs.
Thankfully, firefighters do not have to work alone, and are often aided by the
efforts of good natured civilians who slow the spread of a fire by maintaining
firebreaks when they are able. We will show that this help, although
well-intentioned, unfortunately makes the optimal deployment of firefighters an
even harder problem. To model this scenario we introduce the Temporal
Firefighter problem, an extension of Firefighter to temporal graphs. We show
that Temporal Firefighter is also NP-Complete, and remains so on all but one of
the underlying classes of graphs on which Firefighter is known to have
polynomial time solutions. This motivates us to explore making use of the
temporal structure of the graph in our search for tractability, and we conclude
by presenting an FPT algorithm for Temporal Firefighter with respect to the
temporal graph parameter vertex-interval-membership-width.


title:
X-ray fine structure of a limb solar flare revealed by Insight-HXMT,
  RHESSI and Fermi

abstract:
  We conduct a detailed analysis of an M1.3 limb flare occurring on 2017 July
3, which have the X-ray observations recorded by multiple hard X-ray
telescopes, including Hard X-ray Modulation Telescope (Insight-HXMT), Ramaty
High Energy Solar Spectroscopic Imager (RHESSI), and The Fermi Gamma-ray Space
Telescope (FERMI). Joint analysis has also used the EUV imaging data from the
Atmospheric Imaging Assembly (AIA) aboard the Solar Dynamic Observatory. The
hard X-ray spectral and imaging evolution suggest a lower corona source, and
the non-thermal broken power law distribution has a rather low break energy
$\sim$ 15 keV. The EUV imaging shows a rather stable plasma configuration
before the hard X-ray peak phase, and accompanied by a filament eruption during
the hard X-ray flare peak phase. Hard X-ray image reconstruction from RHESSI
data only shows one foot point source. We also determined the DEM for the peak
phase by SDO/AIA data. The integrated EM beyond 10 MK at foot point onset after
the peak phase, while the $>$ 10 MK source around reconnection site began to
fade. The evolution of EM and hard X-ray source supports lower corona plasma
heating after non-thermal energy dissipation. The combination of hard X-ray
spectra and images during the limb flare provides the understanding on the
interchange of non-thermal and thermal energies, and relation between lower
corona heating and the upper corona instability.


title: Amateur Observers Witness the Return of Venus' Cloud Discontinuity

abstract:
  Firstly identified in images from JAXA's orbiter Akatsuki, the cloud
discontinuity of Venus is a planetary-scale phenomenon known to be recurrent
since, at least, the 1980s. Interpreted as a new type of Kelvin wave, this
disruption is associated to dramatic changes in the clouds' opacity and
distribution of aerosols, and it may constitute a critical piece for our
understanding of the thermal balance and atmospheric circulation of Venus.
Here, we report its reappearance on the dayside middle clouds four years after
its last detection with Akatsuki/IR1, and for the first time, we characterize
its main properties using exclusively near-infrared images from amateur
observations. In agreement with previous reports, the discontinuity exhibited
temporal variations in its zonal speed, orientation, length, and its effect
over the clouds' albedo during the 2019/2020 eastern elongation. Finally, a
comparison with simultaneous observations by Akatsuki UVI and LIR confirmed
that the discontinuity is not visible on the upper clouds' albedo or thermal
emission, while zonal speeds are slower than winds at the clouds' top and
faster than at the middle clouds, evidencing that this Kelvin wave might be
transporting momentum up to upper clouds.


title:
Global martingale solutions for stochastic Shigesada-Kawasaki-Teramoto
  population models

abstract:
  The existence of global nonnegative martingale solutions to cross-diffusion
systems of Shigesada-Kawasaki-Teramoto type with multiplicative noise is
proven. The model describes the stochastic segregation dynamics of an arbitrary
number of population species in a bounded domain with no-flux boundary
conditions. The diffusion matrix is generally neither symmetric nor positive
semidefinite, which excludes standard methods for evolution equations. Instead,
the existence proof is based on the entropy structure of the model, a novel
regularization of the entropy variable, higher-order moment estimates, and
fractional time regularity. The regularization technique is generic and is
applied to the population system with self-diffusion in any space dimension and
without self-diffusion in two space dimensions.


title:
Language technology practitioners as language managers: arbitrating data
  bias and predictive bias in ASR

abstract:
  Despite the fact that variation is a fundamental characteristic of natural
language, automatic speech recognition systems perform systematically worse on
non-standardised and marginalised language varieties. In this paper we use the
lens of language policy to analyse how current practices in training and
testing ASR systems in industry lead to the data bias giving rise to these
systematic error differences. We believe that this is a useful perspective for
speech and language technology practitioners to understand the origins and
harms of algorithmic bias, and how they can mitigate it. We also propose a
re-framing of language resources as (public) infrastructure which should not
solely be designed for markets, but for, and with meaningful cooperation of,
speech communities.


title:
Comparison between MAST-U conventional and Super-X configurations
  through SOLPS-ITER modelling

abstract:
  MAST-U has recently started operating with a Super-X divertor, designed to
increase total flux expansion and neutral trapping, both predicted through
simple analytic models and SOLPS calculations to reduce the plasma and impurity
density detachment thresholds. In this study, utilising the SOLPS-ITER code, we
are quantifying the possible gain allowed by the MAST-U Super-X configuration,
in terms of access to detachment. We show that a significant reduction of the
upstream density detachment threshold (up to a factor 1.6) could be achieved in
MAST-U, mainly through an increased total flux expansion, neutral trapping
being found very similar between the different configurations. We also show
that variations of the strike-point angle are complex to interpret in such a
tightly baffled geometry, and that a more "vertical target" does not
necessarily imply a lower detachment threshold. As in previous calculations for
TCV, we quantify the role of neutral effects through developing and applying a
quantitative definition of neutral trapping.


title:
Assessing the quality of relaxation-time approximations with
  fully-automated computations of phonon-limited mobilities

abstract:
  The mobility of carriers, as limited by their scattering with phonons, can
now routinely be obtained from first-principles electron-phonon coupling
calculations. However, so far, most computations have relied on some form of
simplification of the linearized Boltzmann transport equation based on either
the self-energy, the momentum- or constant relaxation time approximations.
Here, we develop a high-throughput infrastructure and an automatic workflow and
we compute 69 phonon-limited mobilities in semiconductors. We compare the
results resorting to the approximations with the exact iterative solution. We
conclude that the approximate values may deviate significantly from the exact
ones and are thus not reliable. Given the minimal computational overhead, our
work encourages to rely on this exact iterative solution and warns on the
possible inaccuracy of earlier results reported using relaxation time
approximations.


title:
Quantitative exploration of the absorber behavior of
  metal-insulator-metal metamaterials within terahertz via an asymmetric peak
  model

abstract:
  Terahertz (THz) metamaterials have been developed for THz sensing, detection,
imaging, and many other functions due to their unusual absorbers. However, the
unusual absorption spectra change with different incident angles. Thus, we
designed and fabricated a focal plane array with metal-insulator-metal (MIM)
structure metamaterial absorbers for further research. The absorption spectrum
with incident angles from 20 to 60 was measured using THz time-domain
spectroscopy (THz-TDS), and the experimental results reveal that the absorption
spectrum changes with incident angle variations. A basic analytical asymmetric
peak model for extracting absorption-frequency characteristics was developed in
this study to quantitatively explore this variation in the absorber behavior
with incident angles. The best result was that the frequency corresponding to
the highest absorption can be easily found using this peak model. The
experimental data was coherent with the validation of the asymmetric peak
model. Moreover, a second model to quantitatively relate parameters to the
incident angle was discovered, allowing for the prediction of absorption
spectrum shifts and changes. The absorption spectrum was predicted to have a
valley-like absorption curve at particular incident angles based on the
secondary models deduction. The proposed extraction method's essential feature
is that it can be applied to any physics-based MIM metamaterial system. Such a
model will guide the design and optimization of THz metamaterial absorbers,
sensors, imagers, and many others.


title: JParaCrawl v3.0: A Large-scale English-Japanese Parallel Corpus

abstract:
  Most current machine translation models are mainly trained with parallel
corpora, and their translation accuracy largely depends on the quality and
quantity of the corpora. Although there are billions of parallel sentences for
a few language pairs, effectively dealing with most language pairs is difficult
due to a lack of publicly available parallel corpora. This paper creates a
large parallel corpus for English-Japanese, a language pair for which only
limited resources are available, compared to such resource-rich languages as
English-German. It introduces a new web-based English-Japanese parallel corpus
named JParaCrawl v3.0. Our new corpus contains more than 21 million unique
parallel sentence pairs, which is more than twice as many as the previous
JParaCrawl v2.0 corpus. Through experiments, we empirically show how our new
corpus boosts the accuracy of machine translation models on various domains.
The JParaCrawl v3.0 corpus will eventually be publicly available online for
research purposes.


title:
Space of initial conditions and universality in nonequilibrium quantum
  dynamics

abstract:
  We study analytically the role of initial conditions in nonequilibrium
quantum dynamics considering the one-dimensional ferromagnets in the regime of
spontaneously broken symmetry. We analyze the expectation value of local
operators for the infinite-dimensional space of initial conditions of domain
wall type, generally intended as initial conditions spatially interpolating
between two different ground states. At large times the unitary time evolution
takes place inside a light cone produced by the spatial inhomogeneity of the
initial condition. In the innermost part of the light cone the form of the
space-time dependence is universal, in the sense that it is specified by data
of the equilibrium universality class. The global limit shape in the variable
$x/t$ changes with the initial condition. In systems with more than two ground
states the tuning of an interaction parameter can induce a transition which is
the nonequilibrium quantum analog of the interfacial wetting transition
occurring in classical systems at equilibrium. We illustrate the general
results through the examples of the Ising, Potts and Ashkin-Teller chains.


title:
Equivalence of canonical and phenomenological formulations of spin
  hydrodynamics

abstract:
  Two formulations of relativistic hydrodynamics of particles with spin 1/2 are
compared. The first one uses expressions for the energy-momentum and spin
tensors that have properties that follow a direct application of Noether's
theorem, including a totally antisymmetric spin tensor. The other one is based
on a simplified form of the spin tensor that is commonly used in the current
literature under the name of phenomenological pseudo-gauge. We show that these
two frameworks are equivalent, since they can be directly connected by a
suitably defined pseudogauge transformation. Our analysis uses arguments
related to the positivity of entropy production. The latter turns out to be
independent of the pseudo-gauge provided the canonical energy-momentum tensor
is properly improved.


title: Cones of special cycles of codimension 2 on orthogonal Shimura varieties

abstract:
  Let $X$ be an orthogonal Shimura variety associated to a unimodular lattice.
We investigate the polyhedrality of the cone $\mathcal{C}_X$ of special cycles
of codimension 2 on $X$. We show that the rays generated by such cycles
accumulate towards infinitely many rays, the latter generating a non-trivial
cone. We also prove that such accumulation cone is polyhedral. The proof relies
on analogous properties satisfied by the cones of Fourier coefficients of
Siegel modular forms. We show that the accumulation rays of $\mathcal{C}_X$ are
generated by combinations of Heegner divisors intersected with the Hodge class
of $X$. As a result we implement an algorithm in SageMath to certify the
polyhedrality of $\mathcal{C}_X$ in some cases.


title: Phase Object Reconstruction for 4D-STEM using Deep Learning

abstract:
  In this study we explore the possibility to use deep learning for the
reconstruction of phase images from 4D scanning transmission electron
microscopy (4D-STEM) data. The process can be divided into two main steps.
First, the complex electron wave function is recovered for a convergent beam
electron diffraction pattern (CBED) using a convolutional neural network (CNN).
Subsequently a corresponding patch of the phase object is recovered using the
phase object approximation (POA). Repeating this for each scan position in a
4D-STEM dataset and combining the patches by complex summation yields the full
phase object. Each patch is recovered from a kernel of 3x3 adjacent CBEDs only,
which eliminates common, large memory requirements and enables live processing
during an experiment. The machine learning pipeline, data generation and the
reconstruction algorithm are presented. We demonstrate that the CNN can
retrieve phase information beyond the aperture angle, enabling super-resolution
imaging. The image contrast formation is evaluated showing a dependence on
thickness and atomic column type. Columns containing light and heavy elements
can be imaged simultaneously and are distinguishable. The combination of
super-resolution, good noise robustness and intuitive image contrast
characteristics makes the approach unique among live imaging methods in
4D-STEM.


title: Rheological response of a glass-forming liquid having large bidispersity

abstract:
  Using extensive numerical simulations, we investigate the flow behaviour of a
model glass-forming binary mixture whose constituent particles have a large
size ratio. The rheological response to applied shear is studied in the regime
where the larger species are spatially predominant. We demonstrate that the
macroscopic rigidity that emerges with increasing density occurs in the regime
where the larger species undergo a glass transition while the smaller species
continue to be highly diffusive. We analyse the interplay between the timescale
imposed by the shear and the quiescent relaxation dynamics of the two species
to provide a microscopic insight into the observed rheological response.
Finally, by tuning the composition of the mixture, we illustrate that the
systematic insertion of the smaller particles affects the rheology by lowering
of viscosity of the system.


title:
LF-VIO: A Visual-Inertial-Odometry Framework for Large Field-of-View
  Cameras with Negative Plane

abstract:
  Visual-inertial-odometry has attracted extensive attention in the field of
autonomous driving and robotics. The size of Field of View (FoV) plays an
important role in Visual-Odometry (VO) and Visual-Inertial-Odometry (VIO), as a
large FoV enables to perceive a wide range of surrounding scene elements and
features. However, when the field of the camera reaches the negative half
plane, one cannot simply use [u,v,1]^T to represent the image feature points
anymore. To tackle this issue, we propose LF-VIO, a real-time VIO framework for
cameras with extremely large FoV. We leverage a three-dimensional vector with
unit length to represent feature points, and design a series of algorithms to
overcome this challenge. To address the scarcity of panoramic visual odometry
datasets with ground-truth location and pose, we present the PALVIO dataset,
collected with a Panoramic Annular Lens (PAL) system with an entire FoV of
360x(40-120) degrees and an IMU sensor. With a comprehensive variety of
experiments, the proposed LF-VIO is verified on both the established PALVIO
benchmark and a public fisheye camera dataset with a FoV of 360x(0-93.5)
degrees. LF-VIO outperforms state-of-the-art visual-inertial-odometry methods.
Our dataset and code are made publicly available at
https://github.com/flysoaryun/LF-VIO


title: Dynamic Traffic Allocation in IEEE 802.11be Multi-link WLANs

abstract:
  The multi-link operation (MLO) is a key feature of the next IEEE 802.11be
Extremely High Throughput amendment. Through its adoption, it is expected to
enhance users' experience by improving throughput rates and latency. However,
potential MLO gains are tied to how traffic is distributed across the multiple
radio interfaces. In this paper, we introduce a traffic manager atop MLO, and
evaluate different high-level traffic-to-link allocation policies to distribute
incoming traffic over the set of enabled interfaces. Following a flow-level
approach, we compare both non-dynamic and dynamic traffic balancing policy
types. The results show that the use of a dynamic policy, along with MLO,
allows to significantly reduce the congestion suffered by traffic flows,
enhancing the traffic delivery in all the evaluated scenarios, and in
particular improving the quality of service received by video flows. Moreover,
we show that the adoption of MLO in future Wi-Fi networks improves also the
coexistence with non-MLO networks, which results in performance gains for both
MLO and non-MLO networks.


title: Fall of a Particle to the Center of a Singular Potential: Exact results

abstract:
  A fall of a particle to the center of a singular potential is one of a few
fundamental problems of quantum mechanics. Nonetheless, its solution is not
complete yet. The known results just indicate that if the singularity of the
potential is strong enough, the spectrum of the Schrodinger equation is not
bounded from below. However, the wave functions of the problem do not admit the
limiting transition to the ground state. Therefore, the unboundedness of the
spectrum is only a necessary condition. To prove that a quantum particle indeed
can fall to the center, a wave function describing the fall should be obtained
explicitly. This is done in the present paper. Firstly, classical collapse is
inspected. Then, the quantum problem is analyzed. A family of exact
self-similar solutions of the time-dependent Schrodinger equation corresponding
to the fall is obtained and discussed. A law for the collapse of the region of
the wave function localization to a single point is obtained explicitly. It is
shown that the known necessary conditions for the particle to fall
simultaneously are sufficient. Comparison of the classical and quantum collapse
exhibits striking similarity of the two cases.


title: Optimizing thermalizations

abstract:
  We present a rigorous approach, based on the concept of continuous
thermomajorisation, to algorithmically characterise the full set of energy
occupations of a quantum system accessible from a given initial state through
weak interactions with a heat bath. The algorithm can be deployed to solve
complex optimization problems in out-of-equilibrium setups and it returns
explicit elementary control sequences realizing optimal transformations. We
illustrate this by finding optimal protocols in the context of cooling, work
extraction and catalysis. The same tools also allow one to quantitatively
assess the role played by memory effects in the performance of thermodynamic
protocols. We obtained exhaustive solutions on a laptop machine for systems
with dimension $d\leq 7$, but with heuristic methods one could access much
higher $d$.


title:
Algorithmic Computability and Approximability of Capacity-Achieving
  Input Distributions

abstract:
  The capacity of a channel can usually be characterized as a maximization of
certain entropic quantities. From a practical point of view it is of primary
interest to not only compute the capacity value, but also to find the
corresponding optimizer, i.e., the capacity-achieving input distribution. This
paper addresses the general question of whether or not it is possible to find
algorithms that can compute the optimal input distribution depending on the
channel. For this purpose, the concept of Turing machines is used which
provides the fundamental performance limits of digital computers and therewith
fully specifies tasks are algorithmically feasible in principle. It is shown
for discrete memoryless channels that it is impossible to algorithmically
compute the capacity-achieving input distribution, where the channel is given
as an input to the algorithm (or Turing machine). Finally, it is further shown
that it is even impossible to algorithmically approximate these input
distributions.


title: Getting the best from skylines and top-k queries

abstract:
  Top-k and skylines are two important techniques that can be used to extract
the best objects from a set. Both the approaches have well-known pros and cons:
a quite big limitation of skyline queries is the impossibility to control the
cardinality of the output and the difficulty in specifying a trade-off among
attributes, whereas the ranking queries allow so. On the other hand, the usage
of ranking implies that ranking functions need to be specified by users and
renouncing the simplicity of skylines. Flexible/ restricted skylines present a
new approach to tackle this problem, combining the best characteristics of both
techniques making use of a new flexible relation of dominance.


title: Fluid Simulation System Based on Graph Neural Network

abstract:
  Traditional computational fluid dynamics calculates the physical information
of the flow field by solving partial differential equations, which takes a long
time to calculate and consumes a lot of computational resources. We build a
fluid simulation simulator based on the graph neural network architecture. The
simulator has fast computing speed and low consumption of computing resources.
We regard the computational domain as a structural graph, and the computational
nodes in the structural graph determine neighbor nodes through adaptive
sampling. Building deep learning architectures with attention graph neural
networks. The fluid simulation simulator is trained according to the simulation
results of the flow field around the cylinder with different Reynolds numbers.
The trained fluid simulation simulator not only has a very high accuracy for
the prediction of the flow field in the training set, but also can extrapolate
the flow field outside the training set. Compared to traditional CFD solvers,
the fluid simulation simulator achieves a speedup of 2-3 orders of magnitude.
The fluid simulation simulator provides new ideas for the rapid optimization
and design of fluid mechanics models and the real-time control of intelligent
fluid mechanisms.


title: Chirped Elliptic Waves: Coupled Helmholtz Equations

abstract:
  Exact chirped elliptic wave solutions are obtained within the framework of
coupled cubic nonlinear Helmholtz equations in the presence of non-Kerr
nonlinearity like self steepening and self frequency shift. It is shown that,
for a particular combination of the self steepening and the self frequency
shift parameters, the associated nontrivial phase gives rise to chirp reversal
across the solitary wave profile. But a different combination of non-Kerr terms
leads to chirping but no chirp reversal. The effect of nonparaxial parameter on
physical quantities such as intensity, speed and pulse-width of the elliptic
waves is studied too. It is found that the speed of the solitary wave can be
tuned by altering the nonparaxial parameter. Stable propagation of these
nonparaxial elliptic waves is achieved by an appropriate choice of parameters.


title:
On ideals in group algebras: an uncertainty principle and the Schur
  product

abstract:
  In this paper we investigate some properties of ideals in group algebras of
finite groups over fields. First, we highlight an important link between their
dimension, their minimal Hamming distance and the group order. This is a
generalized version of an uncertainty principle shown in 1992 by Meshulam.
Secondly, we introduce the notion of the Schur product of ideals in group
algebras and investigate the module structure and the dimension of the Schur
square. We give a structural result on ideals that coincide with their Schur
square, and we provide conditions for an ideal to be such that its Schur square
has the projective cover of the trivial module as a direct summand. This has
particularly interesting consequences for group algebras of p-groups over
fields of characteristic p.


title: Towards neoRL networks; the emergence of purposive graphs

abstract:
  The neoRL framework for purposive AI implements latent learning by emulated
cognitive maps, with general value functions (GVF) expressing operant desires
toward separate states. The agent's expectancy of reward, expressed as learned
projections in the considered space, allows the neoRL agent to extract
purposive behavior from the learned map according to the reward hypothesis. We
explore this allegory further, considering neoRL modules as nodes in a network
with desire as input and state-action Q-value as output; we see that action
sets with Euclidean significance imply an interpretation of state-action
vectors as Euclidean projections of desire. Autonomous desire from neoRL nodes
within the agent allows for deeper neoRL behavioral graphs. Experiments confirm
the effect of neoRL networks governed by autonomous desire, verifying the four
principles for purposive networks. A neoRL agent governed by purposive networks
can navigate Euclidean spaces in real-time while learning, exemplifying how
modern AI still can profit from inspiration from early psychology.


title:
Energetic neutron identification with pulse shape discrimination in pure
  CsI crystals

abstract:
  Pulse shape discrimination with pure CsI scintillators is investigated as a
method for separating energy deposits by energetic neutrons and photons at
particle physics experiments. Using neutron data collected near the European
XFEL XS1 beam window the pulse shape discrimination capabilities of pure CsI
are studied and compared to CsI(Tl) using near-identical detector setups, which
were operated in parallel. The inelastic interactions of 100MeV neutrons are
observed to produce a slower scintillation emission in pure CsI relative to
energy deposits from cosmic muons. By employing a charge-ratio method for pulse
shape characterization, pulse shape discrimination with pure CsI is shown to be
effective for identifying energy deposits from neutrons vs. cosmic muons,
however, pure CsI was not able resolve the specific type of neutron inelastic
interactions as can be done with CsI(Tl). Using pulse shape discrimination, the
rate of energetic neutron interactions in a pure CsI detector is measured as a
function of time and shown to be correlated with the European XFEL beam power.
The results demonstrate that pulse shape discrimination with pure CsI has
significant potential to improve electromagnetic vs. hadronic shower
identification at future particle physics experiments.


title: The mechanism of high harmonic generation in liquid alcohol

abstract:
  The observation of non-perturbative harmonic emission in solids from
ultrashort laser pulses [1] sparked a wave of studies [2,3] as a probe of
charge carrier dynamics in solids under strong fields and a route to extreme
ultraviolet (XUV) attosecond photonic devices [4]. High harmonic generation
(HHG) in liquids [5,6] is far less explored, despite their relevance to
biological media, and the mechanism is hotly debated. Using few-cycle pulses
below the breakdown threshold, we demonstrate HHG in alcohol with data showing
carrier-envelope-phase-dependent XUV spectra extending to 50 eV from
isopropanol. We study the mechanism of the harmonic emission through its
dependence on the driving field and find it to be consistent with a
strong-field recombination mechanism. This maps emitted photon energy to the
electron trajectories. We explore the role of the liquid environment in
scattering the trajectories and find evidence that information on electron
scattering from neighbouring molecules is encoded in the harmonic spectra.
Using simulations we exploit this to estimate the scattering cross section and
we confirm that the cross-section in liquid isopropanol is significantly
reduced compared to vapour. Our findings suggest an \textit{in situ}
measurement strategy for retrieving accurate values of scattering cross
sections in liquids, and also a pathway to liquid-based attosecond XUV devices.


title:
Constructive subsampling of finite frames with applications in optimal
  function recovery

abstract:
  In this paper we present new constructive methods, random and deterministic,
for the efficient subsampling of finite frames in $\mathbb C^m$. Based on a
suitable random subsampling strategy, we are able to extract from any given
frame with bounds $0 < A \le B < \infty$ (and condition $B/A$) a similarly
conditioned reweighted subframe consisting of merely $\mathcal O(m \log m)$
elements. Further, utilizing a deterministic subsampling method based on
principles developed in [1, Sec. 3], we are able to reduce the number of
elements to $\mathcal O(m)$ (with a constant close to one). By controlling the
weights via a preconditioning step, we can, in addition, preserve the lower
frame bound in the unweighted case. This allows to derive new quasi-optimal
unweighted (left) Marcinkiewicz-Zygmund inequalities for $L_2(D, \nu)$ with
constructible node sets of size $\mathcal O(m)$ for $m$-dimensional subspaces
of bounded functions. Those can be applied e.g. for (plain) least-squares
sampling reconstruction of functions, where we obtain new quasi-optimal results
avoiding the Kadison-Singer theorem. Numerical experiments indicate the
applicability of our results.


title: Joint Answering and Explanation for Visual Commonsense Reasoning

abstract:
  Visual Commonsense Reasoning (VCR), deemed as one challenging extension of
the Visual Question Answering (VQA), endeavors to pursue a more high-level
visual comprehension. It is composed of two indispensable processes: question
answering over a given image and rationale inference for answer explanation.
Over the years, a variety of methods tackling VCR have advanced the performance
on the benchmark dataset. Despite significant as these methods are, they often
treat the two processes in a separate manner and hence decompose the VCR into
two irrelevant VQA instances. As a result, the pivotal connection between
question answering and rationale inference is interrupted, rendering existing
efforts less faithful on visual reasoning. To empirically study this issue, we
perform some in-depth explorations in terms of both language shortcuts and
generalization capability to verify the pitfalls of this treatment. Based on
our findings, in this paper, we present a plug-and-play knowledge distillation
enhanced framework to couple the question answering and rationale inference
processes. The key contribution is the introduction of a novel branch, which
serves as the bridge to conduct processes connecting. Given that our framework
is model-agnostic, we apply it to the existing popular baselines and validate
its effectiveness on the benchmark dataset. As detailed in the experimental
results, when equipped with our framework, these baselines achieve consistent
and significant performance improvements, demonstrating the viability of
processes coupling, as well as the superiority of the proposed framework.


title:
Exchanging quantum correlations and non-local information between three
  qubit-syatem

abstract:
  The possibility of exchanging the quantum correlations and the non-local
information between three qubits interact directly or indirectly via
Dzyaloshinskii-Moriya (DM)is discussed. The initial state settings and the
interaction strength represent control parameters on the exchanging phenomena.
The non-local information that encoded on the different partitions doesn't
exceed the initial one. It is shown that, the ability of DM interaction to
generate entanglement is larger than that displayed for the dipole interaction.
The possibility of maximizing the quantum correlations between the three qubits
increases as one increase the strength of interaction and starting with large
initial quantum correlations. The long-lived quantum correlations could be
achieved by controlling the strength of the dipole interaction.


title: Predicting 4D Liver MRI for MR-guided Interventions

abstract:
  Organ motion poses an unresolved challenge in image-guided interventions. In
the pursuit of solving this problem, the research field of time-resolved
volumetric magnetic resonance imaging (4D MRI) has evolved. However, current
techniques are unsuitable for most interventional settings because they lack
sufficient temporal and/or spatial resolution or have long acquisition times.
In this work, we propose a novel approach for real-time, high-resolution 4D MRI
with large fields of view for MR-guided interventions. To this end, we trained
a convolutional neural network (CNN) end-to-end to predict a 3D liver MRI that
correctly predicts the liver's respiratory state from a live 2D navigator MRI
of a subject. Our method can be used in two ways: First, it can reconstruct
near real-time 4D MRI with high quality and high resolution (209x128x128 matrix
size with isotropic 1.8mm voxel size and 0.6s/volume) given a dynamic
interventional 2D navigator slice for guidance during an intervention. Second,
it can be used for retrospective 4D reconstruction with a temporal resolution
of below 0.2s/volume for motion analysis and use in radiation therapy. We
report a mean target registration error (TRE) of 1.19 $\pm$0.74mm, which is
below voxel size. We compare our results with a state-of-the-art retrospective
4D MRI reconstruction. Visual evaluation shows comparable quality. We show that
small training sizes with short acquisition times down to 2min can already
achieve promising results and 24min are sufficient for high quality results.
Because our method can be readily combined with earlier methods, acquisition
time can be further decreased while also limiting quality loss. We show that an
end-to-end, deep learning formulation is highly promising for 4D MRI
reconstruction.


title: Four-body system of ${}^4\mathrm{He}$ atoms: Dimer-dimer scattering

abstract:
  The strong short-range repulsion, characteristic to realistic interatomic
potentials, complicates the description of weakly-bound few-body systems such
as those of \He atoms. The present work proposes an approach for solving this
problem and applies it to a realistic system of four ${}^4\mathrm{He}$ atoms.
The potential is gradually softened such that rigorous four-body equations for
bound and scattering tates can be accurately solved in the momentum-space
framework, and the results are extrapolated back to the limit of the original
potential. Linear correlations between three- and four body quantities are
observed, and the accuracy of the procedure is improved by extrapolating in one
of the three-body quantities. Results for the ${}^4\mathrm{He}$ tetramer ground
and excited state binding energies and atom-trimer scattering agree well with
at least some of earlier determinations and shed light on the existing
disagreements. An additional case of the Phillips correlation line is
established for the dimer-dimer scattering length. The trimer production rate
via the ultracold two-dimer collisions is estimated, it exhibits significant
finite-range effects despite the weak binding of the dimer.


title: Some results on homogeneous locally nilpotent derivations on k[X,Y,Z]

abstract:
  Let $k$ be a field of characteristic zero. In this paper we have answered a
question of Freudenburg on the rank of homogeneous locally nilpotent
derivations on $k[X,Y,Z]$. We have also investigated the results for
homogeneous locally nilpotent $R$-derivation on $R[X,Y,Z]$ over a Dedekind
domain $R$. Further we have characterised homogeneous locally nilpotent
triangularizable derivations on $k[X,Y,Z]$. When $k$ is an algebraically closed
field, we have characterised irreducible non triangularizable derivations of
rank 2 and of certain degree.


title: Benchmarking LHC searches for light 2HDM+$\boldsymbol{a}$ pseudoscalars

abstract:
  Using two suitable benchmark scenarios that satisfy the experimental
constraints on the total decay width of the $125 \, {\rm GeV}$ Higgs boson, we
determine the bounds on light CP-odd spin-0 states in the 2HDM+$a$ model that
arise from existing LHC searches. Our work should prove useful for 2HDM+$a$
interpretations of future ATLAS, CMS and LHCb searches for pseudoscalars with
masses below the electroweak scale.


title:
Brittle yielding in supercooled liquids below the critical temperature
  of mode coupling theory

abstract:
  Molecular Dynamics (MD) computer simulations of a polydisperse soft-sphere
model under shear are presented. Starting point for these simulations are
deeply supercooled samples far below the critical temperature, $T_c$, of mode
coupling theory. These samples are fully equilibrated with the aid of the swap
Monte Carlo technique. For states below $T_c$, we identify a life time
$\tau_{\rm lt}$ that measures the time scale on which the system can be
considered as an amorphous solid. The temperature dependence of $\tau_{\rm lt}$
can be well described by an Arrhenius law. The existence of transient amorphous
solid states below $T_c$ is associated with the possibility of brittle
yielding, as manifested by a sharp stress drop in the stress-strain relation
and shear banding. We show that brittle yielding requires on the one hand low
shear rates and on the other hand, the time scale corresponding to the inverse
shear rate has to be smaller or of the order of $\tau_{\rm lt}$. Both
conditions can be only met for large life time $\tau_{\rm lt}$, i.e.~for states
far below $T_c$.


title:
Residence time of inertial particles in 3D thermal convection:
  implications for magma reservoirs

abstract:
  The dynamic behavior of crystals in convecting fluids determines how magma
bodies solidify. In particular, it is often important to estimate how long
crystals stay in suspension in the host liquid before being deposited at its
bottom (or top, for light particles). We perform a systematic 3D numerical
study of particle-laden Rayleigh-Benard convection, and derive a robust model
for the particle residence time. For Rayleigh numbers higher than 10^7,
inertial particles' trajectories exhibit a monotonic transition from fluid
tracer-like to free-fall dynamics, the control parameter being the ratio
between particle Stokes velocity and the fluid velocity. The average settling
rate is proportional to the particle Stokes velocity in both the end-member
regimes, but the distribution of the residence times differs markedly from one
to the other. For lower Rayleigh numbers (<10^7), an interaction between
large-scale circulation and particle motion emerges, increasing the settling
rates on average. Nevertheless, the mean residence time does not exceed the
terminal time, i.e. the settling time from a quiescent fluid, by a factor
larger than four. An exception are simulations with only a slightly
super-critical Rayleigh number (~ 10^4), for which stationary convection
develops and some particles become trapped indefinitely. 2D simulations of the
same problem overestimate the flow-particle interaction - and hence the
residence time - for both high and low Rayleigh numbers, which stresses the
importance of using 3D geometries for simulating particle-laden flows. We
outline how our model can be used to explain depth changes of crystal size
distribution in sedimentary layers of magmatic intrusions that are thought to
have formed via settling of a crystal cargo, and discuss how the
micro-structural observations of solidified intrusions can be used to infer the
past convective velocity of magma.


title:
Deep Dirichlet uncertainty for unsupervised out-of-distribution
  detection of eye fundus photographs in glaucoma screening

abstract:
  The development of automatic tools for early glaucoma diagnosis with color
fundus photographs can significantly reduce the impact of this disease.
However, current state-of-the-art solutions are not robust to real-world
scenarios, providing over-confident predictions for out-of-distribution cases.
With this in mind, we propose a model based on the Dirichlet distribution that
allows to obtain class-wise probabilities together with an uncertainty
estimation without exposure to out-of-distribution cases. We demonstrate our
approach on the AIROGS challenge. At the start of the final test phase (8 Feb.
2022), our method had the highest average score among all submissions.


title:
Efficient room-temperature molecular single-photon sources for quantum
  key distribution

abstract:
  Quantum Key Distribution (QKD) allows the distribution of cryptographic keys
between multiple users in an information-theoretic secure way, exploiting
quantum physics. While current QKD systems are mainly based on attenuated laser
pulses, deterministic single-photon sources could give concrete advantages in
terms of secret key rate (SKR) and security owing to the negligible probability
of multi-photon events. Here, we introduce and demonstrate a proof-of-concept
QKD system exploiting a molecule-based single-photon source operating at room
temperature and emitting at 785nm. With an estimated SKR of 0.5 Mbps, our
solution paves the way for room-temperature single-photon sources for quantum
communication protocols.


title: Learning Multi-Task Gaussian Process Over Heterogeneous Input Domains

abstract:
  Multi-task Gaussian process (MTGP) is a well-known non-parametric Bayesian
model for learning correlated tasks effectively by transferring knowledge
across tasks. But current MTGP models are usually limited to the multi-task
scenario defined in the same input domain, leaving no space for tackling the
practical heterogeneous case, i.e., the features of input domains vary over
tasks. To this end, this paper presents a novel heterogeneous stochastic
variational linear model of coregionalization (HSVLMC) model for simultaneously
learning the tasks with varied input domains. Particularly, we develop the
stochastic variational framework with a Bayesian calibration method that (i)
takes into account the effect of dimensionality reduction raised by domain
mapping in order to achieve effective input alignment; and (ii) employs a
residual modeling strategy to leverage the inductive bias brought by prior
domain mappings for better model inference. Finally, the superiority of the
proposed model against existing LMC models has been extensively verified on
diverse heterogeneous multi-task cases.


title: Do autoencoders need a bottleneck for anomaly detection?

abstract:
  A common belief in designing deep autoencoders (AEs), a type of unsupervised
neural network, is that a bottleneck is required to prevent learning the
identity function. Learning the identity function renders the AEs useless for
anomaly detection. In this work, we challenge this limiting belief and
investigate the value of non-bottlenecked AEs.
  The bottleneck can be removed in two ways: (1) overparameterising the latent
layer, and (2) introducing skip connections. However, limited works have
reported on the use of one of the ways. For the first time, we carry out
extensive experiments covering various combinations of bottleneck removal
schemes, types of AEs and datasets. In addition, we propose the infinitely-wide
AEs as an extreme example of non-bottlenecked AEs.
  Their improvement over the baseline implies learning the identity function is
not trivial as previously assumed. Moreover, we find that non-bottlenecked
architectures (highest AUROC=0.857) can outperform their bottlenecked
counterparts (highest AUROC=0.696) on the popular task of CIFAR (inliers) vs
SVHN (anomalies), among other tasks, shedding light on the potential of
developing non-bottlenecked AEs for improving anomaly detection.


title:
Local multiplet formation around a single vacancy in graphene: an
  effective Anderson model analysis based on the block-Lanczos DMRG method

abstract:
  To better understand the electronic structure of a single vacancy in
graphene, we study the ground state property of an effective Anderson model,
consisting of three dangling $sp^2$ orbitals of the surrounding carbon atoms
around the vacancy and the $\pi$ orbitals of carbon atoms that form the
honeycomb lattice with a single vacancy. Employing the block-Lanczos
density-matrix renormalization group method, we show that there are two phases
in the relevant parameter space, i.e., a nonmagnetic phase in the weak coupling
region and a free magnetic moment phase in the realistic parameter region. The
systematic analysis finds that, in the free magnetic moment phase, local
multiplets of the doubly degenerate irreducible representation of the
$\mathcal{C}_3$ point group with spin 1 become dominant in the ground state,
and approximately half of this local spin 1 is screened by electrons in the
surrounding $\pi$ orbitals, indicating the emergence of the residual spin-1/2
free magnetic moment. Furthermore, we find that the emergence of the free
magnetic moment is robust against carrier doping, which is in sharp contrast to
the case of graphene with an adatom, thus explaining the qualitative difference
observed experimentally in these two classes of systems. We also find the
enhancement of the spin correlation function between $\pi$ electrons around the
vacancy and those in the conduction band away from the vacancy in the undoped
case, as compared to that in the doped case, while the spin correlation
function between the $\sigma$ electrons in the $sp^2$ dangling orbitals around
the vacancy and the $\pi$ electrons in the conduction band remains large in
both undoped and doped cases. Our calculations thus support qualitatively the
previous experiment that suggests the emergence of free magnetic moment with
two distinct origins.


title:
Goal-Oriented Communication for Edge Learning based on the Information
  Bottleneck

abstract:
  Whenever communication takes place to fulfil a goal, an effective way to
encode the source data to be transmitted is to use an encoding rule that allows
the receiver to meet the requirements of the goal. A formal way to identify the
relevant information with respect to a goal can be obtained exploiting the
information bottleneck (IB) principle. In this paper, we propose a
goal-oriented communication system, based on the combination of IB and
stochastic optimization. The IB principle is used to design the encoder in
order to find an optimal balance between representation complexity and
relevance of the encoded data with respect to the goal. Stochastic optimization
is then used to adapt the parameters of the IB to find an efficient resource
allocation of communication and computation resources. Our goal is to minimize
the average energy consumption under constraints on average service delay and
accuracy of the learning task applied to the received data in a dynamic
scenario. Numerical results assess the performance of the proposed strategy in
two cases: regression from Gaussian random variables, where we can exploit
closed-form solutions, and image classification using deep neural networks,
with adaptive network splitting between transmit and receive sides.


title: The Hamiltonian Extended Krylov Subspace Method

abstract:
  An algorithm for constructing a $J$-orthogonal basis of the extended Krylov
subspace $\mathcal{K}_{r,s}=\operatorname{range}\{u,Hu, H^2u,$ $ \ldots, $
$H^{2r-1}u, H^{-1}u, H^{-2}u, \ldots, H^{-2s}u\},$ where $H \in \mathbb{R}^{2n
\times 2n}$ is a large (and sparse) Hamiltonian matrix is derived (for $r =
s+1$ or $r=s$). Surprisingly, this allows for short recurrences involving at
most five previously generated basis vectors. Projecting $H$ onto the subspace
$\mathcal{K}_{r,s}$ yields a small Hamiltonian matrix. The resulting HEKS
algorithm may be used in order to approximate $f(H)u$ where $f$ is a function
which maps the Hamiltonian matrix $H$ to, e.g., a (skew-)Hamiltonian or
symplectic matrix. Numerical experiments illustrate that approximating $f(H)u$
with the HEKS algorithm is competitive for some functions compared to the use
of other (structure-preserving) Krylov subspace methods.


title:
Site independent strong phonon-vacancy scattering in high temperature
  ceramics ZrB$_2$ and HfB$_2$

abstract:
  Similar effects of metal and boron vacancies on phonon scattering and lattice
thermal conductivity ($\kappa_l$) of ZrB$_2$ and HfB$_2$ are reported. These
defects challenge the conventional understanding that associates larger impacts
to bigger defects. We find the underlying reason to be a strong local
perturbation caused by the boron vacancy that substantially changes the
interatomic force constants. In contrast, a long ranged but weaker perturbation
is seen in the case metal vacancies. We show that these behaviours originate
from a mixed metallic and covalent bonding nature in the metal diborides. The
thermal transport calculations are performed in a complete \textit{ab initio}
framework based on Boltzmann transport equation and density functional theory.
Phonon-vacancy scattering is calculated using \textit{ab initio} Green's
function approach. Effects of natural isotopes and grain boundaries on
$\kappa_l$ are also systematically investigated, however we find an influential
role of vacancies to explain large variations seen in the experiments. We
further report a two-order of magnitude difference between the amorphous and
pure-crystal limits. Our results outline significant material design aspects
for these multi-functional high temperature ceramics.


title:
New Antenna Selection Schemes for Full-Duplex Cooperative MIMO-NOMA
  Systems

abstract:
  In this paper, we address the antenna selection (AS) problem in full-duplex
(FD) cooperative non-orthogonal multiple access (NOMA) systems, where a
multi-antenna FD relay bridges the connection between the multi-antenna base
station and NOMA far user. Specifically, two AS schemes, namely max-$\SUu$ and
max-$\SUuu$, are proposed to maximize the end-to-end
signal-to-interference-plus-noise ratio at either or both near and far users,
respectively. Moreover, a two-stage AS scheme, namely quality-of-service (QoS)
provisioning scheme, is designed to realize a specific rate at the far user
while improving the near user's rate. To enhance the performance of the QoS
provisioning AS scheme, the idea of dynamic antenna clustering is applied at
the relay to adaptively partition the relay's antennas into transmit and
receive subsets. The proposed AS schemes' exact outage probability and
achievable rate expressions are derived. To provide more insight, closed-form
asymptotic outage probability expressions for the max-$\SUu$ and max-$\SUuu$ AS
schemes are obtained. Our results show that while the QoS provisioning AS
scheme can deliver a near-optimal performance for static antenna setup at the
relay, it provides up to $12\%$ average sum rate gain over the optimum AS
selection with fixed antenna setup.


title: Harmonic gated compensation network plus for ICASSP 2022 DNS CHALLENGE

abstract:
  The harmonic structure of speech is resistant to noise, but the harmonics may
still be partially masked by noise. Therefore, we previously proposed a
harmonic gated compensation network (HGCN) to predict the full harmonic
locations based on the unmasked harmonics and process the result of a coarse
enhancement module to recover the masked harmonics. In addition, the auditory
loudness loss function is used to train the network. For the DNS Challenge, we
update HGCN with the following aspects, resulting in HGCN+. First, a high-band
module is employed to help the model handle full-band signals. Second, cosine
is used to model the harmonic structure more accurately. Then, the dual-path
encoder and dual-path rnn (DPRNN) are introduced to take full advantage of the
features. Finally, a gated residual linear structure replaces the gated
convolution in the compensation module to increase the receptive field of
frequency. The experimental results show that each updated module brings
performance improvement to the model. HGCN+ also outperforms the referenced
models on both wide-band and full-band test sets.


title: Sparse multivariate modeling for stock returns predictability

abstract:
  We develop a new variational Bayes estimation method for large-dimensional
sparse multivariate predictive regression models. Our approach allows to elicit
ordering-invariant shrinkage priors directly on the regression coefficient
matrix rather than a Cholesky-based linear transformation, as typically
implemented in existing MCMC and variational Bayes approaches. Both a
simulation and an empirical study on the cross-industry predictability of
equity risk premiums in the US, show that by directly shrinking weak industry
inter-dependencies one can substantially improve both the statistical and
economic out-of-sample performance of multivariate regression models for return
predictability. This holds across alternative continuous shrinkage priors, such
as the adaptive Bayesian lasso, adaptive normal-gamma and the horseshoe.