title: Modeling Dual Read/Write Paths for Simultaneous Machine Translation

abstract:
  Simultaneous machine translation (SiMT) outputs translation while reading
source sentence and hence requires a policy to decide whether to wait for the
next source word (READ) or generate a target word (WRITE), the actions of which
form a read/write path. Although the read/write path is essential to SiMT
performance, no direct supervision is given to the path in the existing
methods. In this paper, we propose a method of dual-path SiMT which introduces
duality constraints to direct the read/write path. According to duality
constraints, the read/write path in source-to-target and target-to-source SiMT
models can be mapped to each other. As a result, the two SiMT models can be
optimized jointly by forcing their read/write paths to satisfy the mapping.
Experiments on En-Vi and De-En tasks show that our method can outperform strong
baselines under all latency.


title: A Survey on Brain-Computer Interface and Related Applications

abstract:
  BCI systems are able to communicate directly between the brain and computer
using neural activity measurements without the involvement of muscle movements.
For BCI systems to be widely used by people with severe disabilities, long-term
studies of their real-world use are needed, along with effective and feasible
dissemination models. In addition, the robustness of the BCI systems'
performance should be improved so they reach the same level of robustness as
natural muscle-based health monitoring. In this chapter, we review the recent
BCI related studies, followed by the most relevant applications of BCI systems.
We also present the key issues and challenges which exist in regard to the BCI
systems and also provide future directions.


title: Partitions, Multiple Zeta Values and the q-bracket

abstract:
  We provide a framework for relating certain q-series defined by sums over
partitions to multiple zeta values. In particular, we introduce a space of
polynomial functions on partitions for which the associated q-series are
q-analogues of multiple zeta values. By explicitly describing the (regularized)
multiple zeta values one obtains as $q\to 1$, we extend previous results known
in this area. Using this together with the fact that other families of
functions on partitions, such as shifted symmetric functions, are elements in
our space will then give relations among (q-analogues of) multiple zeta values.
Conversely, we will show that relations among multiple zeta values can be
`lifted' to the world of functions on partitions, which provides new examples
of functions where the associated q-series are quasimodular.


title: Linear isoperimetric inequality for homogeneous Hadamard manifolds

abstract:
  It is well known that simply connected symmetric spaces of non-positive
sectional curvature admit a linear isoperimetric filling inequality for cycles
of dimension greater than or equal to the rank of the space. In this note we
extend that result to homogeneous Hadamard manifolds.


title:
UWED: Unsigned Distance Field for Accurate 3D Scene Representation and
  Completion

abstract:
  Scene Completion is the task of completing missing geometry from a partial
scan of a scene. The majority of previous methods compute an implicit
representation from range data using a Truncated Signed Distance Function
(TSDF) on a 3D grid as input to neural networks. The truncation limits but does
not remove the ambiguous cases introduced by the sign for non-closed surfaces.
As an alternative, we present an Unsigned Distance Function (UDF) called
Unsigned Weighted Euclidean Distance (UWED) as input to the scene completion
neural networks. UWED is simple and efficient as a surface representation, and
can be computed on any noisy point cloud without normals. To obtain the
explicit geometry, we present a method for extracting a point cloud from
discretized UDF values on a regular grid. We compare different SDFs and UDFs
for the scene completion task on indoor and outdoor point clouds collected from
RGB-D and LiDAR sensors and show improved completion using the proposed UWED
function.


title:
On the Pitfalls of Heteroscedastic Uncertainty Estimation with
  Probabilistic Neural Networks

abstract:
  Capturing aleatoric uncertainty is a critical part of many machine learning
systems. In deep learning, a common approach to this end is to train a neural
network to estimate the parameters of a heteroscedastic Gaussian distribution
by maximizing the logarithm of the likelihood function under the observed data.
In this work, we examine this approach and identify potential hazards
associated with the use of log-likelihood in conjunction with gradient-based
optimizers. First, we present a synthetic example illustrating how this
approach can lead to very poor but stable parameter estimates. Second, we
identify the culprit to be the log-likelihood loss, along with certain
conditions that exacerbate the issue. Third, we present an alternative
formulation, termed $\beta$-NLL, in which each data point's contribution to the
loss is weighted by the $\beta$-exponentiated variance estimate. We show that
using an appropriate $\beta$ largely mitigates the issue in our illustrative
example. Fourth, we evaluate this approach on a range of domains and tasks and
show that it achieves considerable improvements and performs more robustly
concerning hyperparameters, both in predictive RMSE and log-likelihood
criteria.


title:
Dual-domain Attention-based Deep Network for Sparse-view CT Artifact
  Reduction

abstract:
  Due to the wide applications of X-ray computed tomography (CT) in medical
imaging activities, radiation exposure has become a major concern for public
health. Sparse-view CT is a promising approach to reduce the radiation dose by
down-sampling the total number of acquired projections. However, the CT images
reconstructed by this sparse-view imaging approach suffer from severe streaking
artifacts and structural information loss. In this work, an end-to-end
dual-domain attention-based deep network (DDANet) is proposed to solve such an
ill-posed CT image reconstruction problem. The image-domain CT image and the
projection-domain sinogram are put into the two parallel sub-networks of the
DDANet to independently extract the distinct high-level feature maps. In
addition, a specified attention module is introduced to fuse the aforementioned
dual-domain feature maps to allow complementary optimizations of removing the
streaking artifacts and mitigating the loss of structure. Numerical
simulations, anthropomorphic thorax phantom and in vivo pre-clinical
experiments are conducted to verify the sparse-view CT imaging performance of
the DDANet. Results demonstrate that this newly developed approach is able to
robustly remove the streaking artifacts while maintaining the fine structures.
As a result, the DDANet provides a promising solution in achieving high quality
sparse-view CT imaging.


title:
Recurrent Neural Networks for Forecasting Time Series with Multiple
  Seasonality: A Comparative Study

abstract:
  This paper compares recurrent neural networks (RNNs) with different types of
gated cells for forecasting time series with multiple seasonality. The cells we
compare include classical long short term memory (LSTM), gated recurrent unit
(GRU), modified LSTM with dilation, and two new cells we proposed recently,
which are equipped with dilation and attention mechanisms. To model the
temporal dependencies of different scales, our RNN architecture has multiple
dilated recurrent layers stacked with hierarchical dilations. The proposed RNN
produces both point forecasts and predictive intervals (PIs) for them. An
empirical study concerning short-term electrical load forecasting for 35
European countries confirmed that the new gated cells with dilation and
attention performed best.


title: Condensed domains and the $D+XL[X]$ construction

abstract:
  Let $D$ be an integral domain with quotient field $K$ and let $\mathcal{I}%
(D) $ be the set of nonzero ideals of $D$. Call, for $I,J\in \mathcal{I}(D)$ ,
the product $IJ$ of ideals condensed if $IJ=\{ij|i\in I,j\in J\}.$ Call $D$ a
condensed domain if for each pair $I,J$ the product $IJ$ is condensed. We show
that if $a,b$ are elements of a condensed domain such that $aD\cap bD=abD,$
then $(a,b)=D.$ It was shown in [Comm. Algebra 15 (1987), 1895-1920] that a
pre-Schreier domain is a $\ast $-domain, i.e., $D$ satisfies $\ast :$ For every
pair $\{a_{i}\}_{i=1}^{m},\{b_{j}\}_{j=1}^{n}$ of sets of nonzero elements of
$D$ we have $(\cap (a_{i}))(\cap b_{j})=\cap (a_{i}b_{j}).$ We show that a
condensed domain $D$ is pre-Schreier if and only if $D$ is a $ \ast $-domain.
We also show that if $A\subseteq B$ is an extension of domains and $A+XB[X]$ is
condensed, then $B$ must be a field and $A$ must be condensed and in this case
$[B:K]<4.$ In particular we study the necessary and sufficient conditions for
$D+XL[X]$ to be condensed, where $D$ is a domain and $L$ an extension field of
$K.$ It may be noted that if $D$ is not a field $D[X]$ is never condensed. So
for $D$ condensed $D+XK[X]$ is a way of constructing new condensed domains from
old


title:
Jitter-calibrated second-order correlation measurement of
  low-repetition-rate pulsed excitation

abstract:
  We report a new technique for the realization of second-order correlation
($g^2(\tau)$) measurement under low-repetition-rate pulsed excitation (1 kHz),
with timing jitter calibrated to restore lateral $g^2(\tau)$ curves and
determine $g^2(0)$. We use CdSe nanowire (NW) laser to demonstrate the
jitter-calibrated $g^2(\tau)$ measurement, where $g^2(0)$ evolution shows the
laser emission transition process. The exciting pulses are split into reference
and excitation channels, which enables the jitter calibration when the exciting
pulses have significant timing jitter and low repetition rate. After using the
reference signals to calibrate and rearrange the single-photon signals, lateral
$g^2(\tau)$ curves can be entirely restored, and $g^2(0)$ evolution is
demonstrated from our method.


title: On Vision Features in Multimodal Machine Translation

abstract:
  Previous work on multimodal machine translation (MMT) has focused on the way
of incorporating vision features into translation but little attention is on
the quality of vision models. In this work, we investigate the impact of vision
models on MMT. Given the fact that Transformer is becoming popular in computer
vision, we experiment with various strong models (such as Vision Transformer)
and enhanced features (such as object-detection and image captioning). We
develop a selective attention model to study the patch-level contribution of an
image in MMT. On detailed probing tasks, we find that stronger vision models
are helpful for learning translation from the visual modality. Our results also
suggest the need of carefully examining MMT models, especially when current
benchmarks are small-scale and biased. Our code could be found at
\url{https://github.com/libeineu/fairseq_mmt}.


title: Nearest Neighbor Classifier with Margin Penalty for Active Learning

abstract:
  As deep learning becomes the mainstream in the field of natural language
processing, the need for suitable active learning method are becoming
unprecedented urgent. Active Learning (AL) methods based on nearest neighbor
classifier are proposed and demonstrated superior results. However, existing
nearest neighbor classifier are not suitable for classifying mutual exclusive
classes because inter-class discrepancy cannot be assured by nearest neighbor
classifiers. As a result, informative samples in the margin area can not be
discovered and AL performance are damaged. To this end, we propose a novel
Nearest neighbor Classifier with Margin penalty for Active Learning(NCMAL).
Firstly, mandatory margin penalty are added between classes, therefore both
inter-class discrepancy and intra-class compactness are both assured. Secondly,
a novel sample selection strategy are proposed to discover informative samples
within the margin area. To demonstrate the effectiveness of the methods, we
conduct extensive experiments on for datasets with other state-of-the-art
methods. The experimental results demonstrate that our method achieves better
results with fewer annotated samples than all baseline methods.


title:
Generalized Classification of Satellite Image Time Series with Thermal
  Positional Encoding

abstract:
  Large-scale crop type classification is a task at the core of remote sensing
efforts with applications of both economic and ecological importance. Current
state-of-the-art deep learning methods are based on self-attention and use
satellite image time series (SITS) to discriminate crop types based on their
unique growth patterns. However, existing methods generalize poorly to regions
not seen during training mainly due to not being robust to temporal shifts of
the growing season caused by variations in climate. To this end, we propose
Thermal Positional Encoding (TPE) for attention-based crop classifiers. Unlike
previous positional encoding based on calendar time (e.g. day-of-year), TPE is
based on thermal time, which is obtained by accumulating daily average
temperatures over the growing season. Since crop growth is directly related to
thermal time, but not calendar time, TPE addresses the temporal shifts between
different regions to improve generalization. We propose multiple TPE
strategies, including learnable methods, to further improve results compared to
the common fixed positional encodings. We demonstrate our approach on a crop
classification task across four different European regions, where we obtain
state-of-the-art generalization results.


title:
ODE Transformer: An Ordinary Differential Equation-Inspired Model for
  Sequence Generation

abstract:
  Residual networks are an Euler discretization of solutions to Ordinary
Differential Equations (ODE). This paper explores a deeper relationship between
Transformer and numerical ODE methods. We first show that a residual block of
layers in Transformer can be described as a higher-order solution to ODE.
Inspired by this, we design a new architecture, {\it ODE Transformer}, which is
analogous to the Runge-Kutta method that is well motivated in ODE. As a natural
extension to Transformer, ODE Transformer is easy to implement and efficient to
use. Experimental results on the large-scale machine translation, abstractive
summarization, and grammar error correction tasks demonstrate the high
genericity of ODE Transformer. It can gain large improvements in model
performance over strong baselines (e.g., 30.77 and 44.11 BLEU scores on the
WMT'14 English-German and English-French benchmarks) at a slight cost in
inference efficiency.


title: The Variable Volatility Elasticity Model from Commodity Markets

abstract:
  In this paper, we propose and study a novel continuous-time model, based on
the well-known constant elasticity of variance (CEV) model, to describe the
asset price process. The basic idea is that the volatility elasticity of the
CEV model can not be treated as a constant from the perspective of stochastic
analysis. To address this issue, we deduce the price process of assets from the
perspective of volatility elasticity, propose the constant volatility
elasticity (CVE) model, and further derive a more general variable volatility
elasticity (VVE) model. Moreover, our model can describe the positive
correlation between volatility and asset prices existing in the commodity
markets, while CEV model can only describe the negative correlation. Through
the empirical research on the financial market, many assets, especially
commodities, often show this positive correlation phenomenon in some time
periods, which shows that our model has strong practical application value.
Finally, we provide the explicit pricing formula of European options based on
our model. This formula has an elegant form convenient to calculate, which is
similarly to the renowned Black-Scholes formula and of great significance to
the research of derivatives market.

categories: q-fin.MF q-fin.CP q-fin.GN q-fin.RM q-fin.ST


title: Multilingual Detection of Personal Employment Status on Twitter

abstract:
  Detecting disclosures of individuals' employment status on social media can
provide valuable information to match job seekers with suitable vacancies,
offer social protection, or measure labor market flows. However, identifying
such personal disclosures is a challenging task due to their rarity in a sea of
social media content and the variety of linguistic forms used to describe them.
Here, we examine three Active Learning (AL) strategies in real-world settings
of extreme class imbalance, and identify five types of disclosures about
individuals' employment status (e.g. job loss) in three languages using
BERT-based classification models. Our findings show that, even under extreme
imbalance settings, a small number of AL iterations is sufficient to obtain
large and significant gains in precision, recall, and diversity of results
compared to a supervised baseline with the same number of labels. We also find
that no AL strategy consistently outperforms the rest. Qualitative analysis
suggests that AL helps focus the attention mechanism of BERT on core terms and
adjust the boundaries of semantic expansion, highlighting the importance of
interpretable models to provide greater control and visibility into this
dynamic learning process.


title:
Maximum Likelihood Estimation in Gaussian Process Regression is
  Ill-Posed

abstract:
  Gaussian process regression underpins countless academic and industrial
applications of machine learning and statistics, with maximum likelihood
estimation routinely used to select appropriate parameters for the covariance
kernel. However, it remains an open problem to establish the circumstances in
which maximum likelihood estimation is well-posed. That is, when the
predictions of the regression model are continuous (or insensitive to small
perturbations) in the training data. This article presents a rigorous proof
that the maximum likelihood estimator fails to be well-posed in Hellinger
distance in a scenario where the data are noiseless. The failure case occurs
for any Gaussian process with a stationary covariance function whose
lengthscale parameter is estimated using maximum likelihood. Although the
failure of maximum likelihood estimation is informally well-known, these
theoretical results appear to be the first of their kind, and suggest that
well-posedness may need to be assessed post-hoc, on a case-by-case basis, when
maximum likelihood estimation is used to train a Gaussian process model.


title:
A Novel End-To-End Network for Reconstruction of Non-Regularly Sampled
  Image Data Using Locally Fully Connected Layers

abstract:
  Quarter sampling and three-quarter sampling are novel sensor concepts that
enable the acquisition of higher resolution images without increasing the
number of pixels. This is achieved by non-regularly covering parts of each
pixel of a low-resolution sensor such that only one quadrant or three quadrants
of the sensor area of each pixel is sensitive to light. Combining a properly
designed mask and a high-quality reconstruction algorithm, a higher image
quality can be achieved than using a low-resolution sensor and subsequent
upsampling. For the latter case, the image quality can be further enhanced
using super resolution algorithms such as the very deep super resolution
network (VDSR). In this paper, we propose a novel end-to-end neural network to
reconstruct high resolution images from non-regularly sampled sensor data. The
network is a concatenation of a locally fully connected reconstruction network
(LFCR) and a standard VDSR network. Altogether, using a three-quarter sampling
sensor with our novel neural network layout, the image quality in terms of PSNR
for the Urban100 dataset can be increased by 2.96 dB compared to the
state-of-the-art approach. Compared to a low-resolution sensor with VDSR, a
gain of 1.11 dB is achieved.


title: An Interactive Explanatory AI System for Industrial Quality Control

abstract:
  Machine learning based image classification algorithms, such as deep neural
network approaches, will be increasingly employed in critical settings such as
quality control in industry, where transparency and comprehensibility of
decisions are crucial. Therefore, we aim to extend the defect detection task
towards an interactive human-in-the-loop approach that allows us to integrate
rich background knowledge and the inference of complex relationships going
beyond traditional purely data-driven approaches. We propose an approach for an
interactive support system for classifications in an industrial quality control
setting that combines the advantages of both (explainable) knowledge-driven and
data-driven machine learning methods, in particular inductive logic programming
and convolutional neural networks, with human expertise and control. The
resulting system can assist domain experts with decisions, provide transparent
explanations for results, and integrate feedback from users; thus reducing
workload for humans while both respecting their expertise and without removing
their agency or accountability.


title:
Properties of a confirmatory two-stage adaptive procedure for assessing
  average bioequivalence

abstract:
  We investigate a confirmatory two stage adaptive procedure for assessing
average bioequivalence and provide some insights to its theoretical properties.
Effectively, we perform Two One-Sided Tests (TOST) to reach overall decision
about each of the two traditional null-hypotheses involved in declaring average
bioequivalence. The tests are performed as combination tests separately for
each hypothesis based on the corresponding pair of stagewise p-values. Features
of the procedure include a built in futility, sample size reassessment, and the
ability to simultaneously assess average bioequivalence with respect to
multiple endpoints while controlling the familywise error rate. To facilitate
inference at the end of a trial we also derive overall confidence limits that
match the decision reached on each one sided hypothesis and provide theory
ensuring their appropriateness. The performance is assessed by simulation in
the context of planning a study to compare two different administrations of a
biologic treatment of atopic dermatitis.


title: RoMe: A Robust Metric for Evaluating Natural Language Generation

abstract:
  Evaluating Natural Language Generation (NLG) systems is a challenging task.
Firstly, the metric should ensure that the generated hypothesis reflects the
reference's semantics. Secondly, it should consider the grammatical quality of
the generated sentence. Thirdly, it should be robust enough to handle various
surface forms of the generated sentence. Thus, an effective evaluation metric
has to be multifaceted. In this paper, we propose an automatic evaluation
metric incorporating several core aspects of natural language understanding
(language competence, syntactic and semantic variation). Our proposed metric,
RoMe, is trained on language features such as semantic similarity combined with
tree edit distance and grammatical acceptability, using a self-supervised
neural network to assess the overall quality of the generated sentence.
Moreover, we perform an extensive robustness analysis of the state-of-the-art
methods and RoMe. Empirical results suggest that RoMe has a stronger
correlation to human judgment over state-of-the-art metrics in evaluating
system-generated sentences across several NLG tasks.


title:
Mass-difference measurements on heavy nuclides with at an eV/c2 accuracy
  level with PENTATRAP

abstract:
  First ever measurements of the ratios of free cyclotron frequencies of heavy
highly charged ions with Z>50 with relative uncertainties close to 1e-11 are
presented. Such accurate measurements have become realistic due to the
construction of the novel cryogenic multi-Penning-trap mass spectrometer
PENTATRAP. Based on the measured frequency ratios, the mass differences of five
pairs of stable xenon isotopes, ranging from 126Xe to 134Xe, have been
determined. Moreover, the first direct measurement of an electron binding
energy in a heavy highly charged ion, namely of the 37th atomic electron in
xenon, with an uncertainty of a few eV is demonstrated. The obtained value
agrees with the calculated one using two independent different implementations
of the multiconfiguration Dirac-Hartree-Fock method. PENTATRAP opens the door
to future measurements of electron binding energies in highly charged heavy
ions for more stringent tests of bound-state quantum electrodynamics in strong
electromagnetic fields and for an investigation of the manifestation of Light
Dark Matter in isotopic chains of certain chemical elements.


title: Phase Optimization for Massive IRS-aided Two-way Relay Network

abstract:
  In this paper, with the help of an intelligent reflecting surface (IRS), the
source (S) and destination (D) exchange information through the two-way
decode-and-forward relay (TW-DFR). We mainly focus on the phase optimization of
IRS to improve the system rate performance. Firstly, a maximizing receive power
sum (Max-RPS) method is proposed via eigenvalue decomposition (EVD) with an
appreciable rate enhancement, which is called Max-RPS-EVD. To further achieve a
higher rate, a method of maximizing minimum rate (Max-Min-R) is proposed with
high complexity. To reduce its complexity, a low-complexity method of
maximizing the sum rate (Max-SR) via general power iterative (GPI) is proposed,
which is called Max-SR-GPI. Simulation results show that the proposed three
methods outperform the case of random phase method, especially the proposed
Max-SR-GPI method is the best one achieving at least 20\% rate gain over random
phase. Additionally, it is also proved the optimal rate can be achieved when
TW-DFR and IRS are located in the middle of S and D.


title: The CLIC project

abstract:
  The Compact Linear Collider (CLIC) is a multi-TeV high-luminosity linear
e$^+$e$^-$-collider under development by the CLIC accelerator collaboration,
hosted by CERN. The CLIC accelerator has been optimised for three energy stages
at centre-of-mass energies 380 GeV, 1.5 TeV and 3 TeV. CLIC uses a novel
two-beam acceleration technique, with normal-conducting accelerating structures
operating in the range of 70-100 MV/m.
  The report describes recent achievements in accelerator design, technology
development and prototyping, system tests and beam tests. Large-scale
CLIC-specific beam tests have taken place, for example, at the CLIC Test
Facility CTF3 at CERN, at the Accelerator Test Facility ATF2 at KEK, at the
FACET facility at SLAC and at the FERMI facility in Trieste. Together, they
demonstrate that all implications of the CLIC design parameters are well
understood and reproducible in beam tests and prove that the CLIC performance
goals are realistic. The implementation of CLIC near CERN has been
investigated. Focusing on a staged approach starting at 380 GeV, this includes
civil engineering aspects, electrical networks, cooling and ventilation and
installation scheduling, transport. All CLIC studies have put emphasis on
optimising cost and energy efficiency, and the resulting power and cost
estimates are reported. The report follows very closely the accelerator project
description in the CLIC Summary Report for the European Particle Physics
Strategy update 2018-19.
  Detailed studies of the physics potential and detector for CLIC, and R&D on
detector technologies, have been carried out by the CLIC detector and physics
(CLICdp) collaboration. CLIC provides excellent sensitivity to Beyond Standard
Model physics, through direct searches and via a broad set of precision
measurements of Standard Model processes, particularly in the Higgs and
top-quark sectors.


title: Force-free and twisted, relativistic neutron star magnetospheres

abstract:
  In this poster we present the structure of an axisymmetric, force-free
magnetosphere of a twisted, aligned rotating dipole within a corotating
plasma-filled magnetosphere. We explore various profiles for the twist. We find
that as the current increases more field lines cross the light cylinder leading
to more efficient spin-down. Moreover, we notice that the twist cannot be
increased indefinitely and after a finite twist of about ${\pi}/2$ the field
becomes approximately radial. This could have implications for torque
variations of magnetars related to outbursts.


title: Electronic properties of twisted multilayer graphene

abstract:
  Twisted bilayer graphene displays many fascinating properties that can be
tuned by varying the relative angle (also called twist angle) between its
monolayers. As a remarkable feature, both the electronic flat bands and the
corresponding strong electron localization have been obtained at a specific
"magic" angle ($\sim 1.1^{\circ}$), leading to the observation of several
strongly correlated electronic phenomena. Such a discovery has hence inspired
the creation of a novel research field called twistronics, i.e., aiming to
explore novel physical properties in vertically stacked 2D structures when
tuning the twist angle between the related layers. In this paper, a
comprehensive and systematic study related to the electronic properties of
twisted multilayer graphene (TMG) is presented based on atomistic calculations.
The dependence of both the global and the local electronic quantities on the
twist angle and on the stacking configuration are analyzed, fully taking into
account atomic reconstruction effects. Consequently, the correlation between
structural and electronic properties are clarified, thereby highlighting the
shared characteristics and differences between various TMG systems as well as
providing a comprehensive and essential overview. On the basis of these
investigations, possibilities to tune the electronic properties are discussed,
allowing for further developments in the field of twistronics.


title:
Supersolid induced by dislocations with superfluid cores (Review
  article)

abstract:
  Dislocation model of the supersolid state of $^4$He was proposed in 1987 by
one of the authors of the review. The model obtained strong support by numerous
experimental and theoretical investigations from 2007 to date. In these
investigations the validity of the idea put forward in 1987 was confirmed, and
new conceptions of superclimb of dislocations and of the giant isochoric
compressibility or the syringe effect were proposed. In this paper we review
main achievements of theoretical and experimental studies of
dislocation-induced supersolid and present current understanding of this
phenomenon.


title: Optimal Adaptive Droop Design via a Modified Relaxation of the OPF

abstract:
  The ever increasing penetration of Renewable Energy Resources (RESs) in power
distribution networks has brought, among others, the challenge of maintaining
the grid voltages within the secure region. Employing droop voltage regulators
on the RES inverters is an efficient and low cost solution to reach this
objective. However, fixing droop parameters or optimizing them only for
overvoltage conditions does not provide the required robustness and optimality
under changing operating conditions. In this paper, a convex optimization
approach is proposed for reconfiguring PV and QV droop regulators during online
operation. The objective is to minimize power curtailment, power losses, and
voltage deviation subject to electrical security constraints. This enables to
optimally operate the grid with high RES penetration under variable conditions
while preserving electrical security constraints (e.g., current and voltage
limits). As a first contribution, a mixed integer linear model of the droop
characteristics is developed. According to this model, a droop regulated
generation unit is represented as a constant power generator in parallel with a
constant impedance load. As a second contribution, a Modified Augmented Relaxed
Optimal Power Flow (MAROPF) formulation is proposed to guarantee that the
electrical security constraints are respected in the presence of constant
impedance loads in the network. Sufficient conditions for the feasibility of
the MAROPF solution are provided. Those conditions can be checked a priori and
are valid for several real distribution networks. Furthermore, an iterative
approach is proposed to derive an approximate solution to the MAROPF that is
close to the global optimal one. The performance of the MAROPF approach and the
accuracy of the proposed model are evaluated on standard 34 bus and 85 bus test
networks.


title: Abstract Interpretation on E-Graphs

abstract:
  Recent e-graph applications have typically considered concrete semantics of
expressions, where the notion of equivalence stems from concrete interpretation
of expressions. However, equivalences that hold over one interpretation may not
hold in an alternative interpretation. Such an observation can be exploited. We
consider the application of abstract interpretation to e-graphs, and show that
within an e-graph, the lattice meet operation associated with the abstract
domain has a natural interpretation for an e-class, leading to improved
precision in over-approximation. In this extended abstract, we use Interval
Arithmetic (IA) to illustrate this point.


title:
Entropy-based Attention Regularization Frees Unintended Bias Mitigation
  from Lists

abstract:
  Natural Language Processing (NLP) models risk overfitting to specific terms
in the training data, thereby reducing their performance, fairness, and
generalizability. E.g., neural hate speech detection models are strongly
influenced by identity terms like gay, or women, resulting in false positives,
severe unintended bias, and lower performance. Most mitigation techniques use
lists of identity terms or samples from the target domain during training.
However, this approach requires a-priori knowledge and introduces further bias
if important terms are neglected. Instead, we propose a knowledge-free
Entropy-based Attention Regularization (EAR) to discourage overfitting to
training-specific terms. An additional objective function penalizes tokens with
low self-attention entropy. We fine-tune BERT via EAR: the resulting model
matches or exceeds state-of-the-art performance for hate speech classification
and bias metrics on three benchmark corpora in English and Italian. EAR also
reveals overfitting terms, i.e., terms most likely to induce bias, to help
identify their effect on the model, task, and predictions.


title: Database of Indian Social Media Influencers on Twitter

abstract:
  Databases of highly networked individuals have been indispensable in studying
narratives and influence on social media. To support studies on Twitter in
India, we present a systematically categorised database of accounts of
influence on Twitter in India, identified and annotated through an iterative
process of friends, networks, and self-described profile information, verified
manually. We built an initial set of accounts based on the friend network of a
seed set of accounts based on real-world renown in various fields, and then
snowballed "friends of friends" multiple times, and rank ordered individuals
based on the number of in-group connections, and overall followers. We then
manually classified identified accounts under the categories of entertainment,
sports, business, government, institutions, journalism, civil society accounts
that have independent standing outside of social media, as well as a category
of "digital first" referring to accounts that derive their primary influence
from online activity. Overall, we annotated 11580 unique accounts across all
categories. The database is useful studying various questions related to the
role of influencers in polarisation, misinformation, extreme speech, political
discourse etc.


title:
An RF Timer of Electrons and Photons with the Potential to reach
  Picosecond Precision

abstract:
  This paper describes a new radio frequency timer of keV energy electrons. It
is based on a helical deflector, which performs circular or elliptical sweeps
of keV electrons, by means of 500 MHz radio frequency field. By converting a
time distribution of incident electrons to a hit position distribution on a
circle or ellipse, this device achieves extremely precise timing. Streak
Cameras, based on similar principles, routinely operate in the ps and sub-ps
time domain, but have substantial dead time associated with the readout system.
Here, we report a new type of RF timing technique, where the position sensor,
consisting of microchannel plates and a delay-line anode, produces ~ns duration
pulses with small dead time. Measurements made with sub-ps duration laser
pulses, synchronized to the radio frequency power, produced a timing resolution
of ~10 ps. This ultra-high precision technique has potential applications in a
large variety of scientific devices, and in all cases, electrons are timed and
detected simultaneously in the same device.


title:
Details or Artifacts: A Locally Discriminative Learning Approach to
  Realistic Image Super-Resolution

abstract:
  Single image super-resolution (SISR) with generative adversarial networks
(GAN) has recently attracted increasing attention due to its potentials to
generate rich details. However, the training of GAN is unstable, and it often
introduces many perceptually unpleasant artifacts along with the generated
details. In this paper, we demonstrate that it is possible to train a GAN-based
SISR model which can stably generate perceptually realistic details while
inhibiting visual artifacts. Based on the observation that the local statistics
(e.g., residual variance) of artifact areas are often different from the areas
of perceptually friendly details, we develop a framework to discriminate
between GAN-generated artifacts and realistic details, and consequently
generate an artifact map to regularize and stabilize the model training
process. Our proposed locally discriminative learning (LDL) method is simple
yet effective, which can be easily plugged in off-the-shelf SISR methods and
boost their performance. Experiments demonstrate that LDL outperforms the
state-of-the-art GAN based SISR methods, achieving not only higher
reconstruction accuracy but also superior perceptual quality on both synthetic
and real-world datasets. Codes and models are available at
https://github.com/csjliang/LDL.


title: Isolating an outflow component in single-epoch spectra of quasars

abstract:
  Gaseous outflows appear to be a universal property of type-1 and type-2
active galactic nuclei (AGN). The main diagnostic is provided by emission
features shifted to higher frequency via the Doppler effect, implying that the
emitting gas is moving toward the observer. However, beyond the presence of
blueshift, the observational signatures of the outflows are often unclear, and
no established criteria exist to isolate the outflow contribution in the
integrated, single-epoch spectra of type-1 AGN. The emission spectrum collected
with the typical apertures of long-slit spectroscopy or of fiber optics sample
contributions over a broad range of spatial scales, making it difficult to
analyze the line profiles in terms of different kinematic components.
Nevertheless, hundred of thousands of quasars spectra collected at moderate
resolution demand a proper analysis of the line profiles for proper dynamical
modelling of the emitting regions. In this small contribution we shall analyze
several profiles of the HI Balmer line H\b{eta} from composite and individual
optical spectra of sources radiating at moderate Eddington ratio (Population B
following Sulentic et al. 2000). Features and profile shapes that might be
traced to outflow due to narrow-line region gas are detected over a wide range
of luminosity.


title: Dispersive landslide

abstract:
  Considering the non-hydrostatic mass flow model (Pudasaini, 2022), here, we
derive a novel dispersive wave equation for landslide. The new dispersive wave
for landslide recovers the classical dispersive water waves as a special case.
We show that the frequency dispersion relation for landslide is inherently
different than the classical frequency dispersion for water waves. The wave
frequency with dispersion increases non-linearly as a function of the wave
number. For dispersive landslide, the wave frequency without dispersion appears
to heavily overestimate the dispersive wave frequency for higher wave number.
Due to the dispersion term emerging from the non-hydrostatic contribution for
landslide, the phase velocity becomes a function of the wave number. This gives
rise to the group velocity that is significantly different from the phase
velocity, characterizing the dispersive mass flow. The dispersive phase
velocity and group velocity decrease non-linearly with the wave number. Yet,
the group velocity is substantially lower than the phase velocity. We
analytically derive a dispersion number as the ratio between the phase velocity
and the group velocity, which measures the deviation of the group velocity from
the phase velocity, provides a dynamic scaling between them and summarizes the
overall effect of dispersion in the mass flow. The dispersion number for
landslide increases rapidly with the wave number, which is in contrast to the
dispersion in water waves. With the definition of the effective dispersive
lateral stress, we prove the existence of an anti-restoring force in landslide.
We reveal the fact that due to the anti-restoring force, landslides are more
dispersive than the piano strings. So, the wave dispersion in landslide is
fundamentally different than the wave dispersion in the piano string. Our model
constitutes a foundation for the wave phenomenon in dispersive mass flows.


title:
White Paper: AWAKE, Plasma Wakefield Acceleration of Electron Bunches
  for Near and Long Term Particle Physics Applications

abstract:
  Plasma-based accelerators have made remarkable progress over the last two
decades. Their unique characteristics make them tools that can revolutionize
fields of science and applications. AWAKE takes advantage of the availability
of high-energy, relativistic proton bunches to drive large amplitude wakefields
($\sim$GV/m) in a single plasma over distances sufficient to produce hundreds
of GeV to TeV electron bunches.


title: Unified inverse correspondence for DLE-Logics

abstract:
  By exploiting the algebraic and order theoretic mechanisms behind Sahlqvist
correspondence, the theory of unified correspondence provides powerful tools
for correspondence and canonicity across different semantics and signatures,
covering all the logics whose algebraic semantics are given by normal
(distributive) lattice expansions (referred to as (D)LEs). In particular, the
algorithm ALBA, parametric in each (D)LE, effectively computes the first order
correspondents of (D)LE-inductive formulas. We present an algorithm that makes
use of ALBA's rules and algebraic language to invert its steps in the DLE
setting; therefore effectively computing an inductive formula starting from its
first order correspondent.


title:
Novel Consistency Check For Fast Recursive Reconstruction Of
  Non-Regularly Sampled Video Data

abstract:
  Quarter sampling is a novel sensor design that allows for an acquisition of
higher resolution images without increasing the number of pixels. When being
used for video data, one out of four pixels is measured in each frame.
Effectively, this leads to a non-regular spatio-temporal sub-sampling. Compared
to purely spatial or temporal sub-sampling, this allows for an increased
reconstruction quality, as aliasing artifacts can be reduced. For the fast
reconstruction of such sensor data with a fixed mask, recursive variant of
frequency selective reconstruction (FSR) was proposed. Here, pixels measured in
previous frames are projected into the current frame to support its
reconstruction. In doing so, the motion between the frames is computed using
template matching. Since some of the motion vectors may be erroneous, it is
important to perform a proper consistency checking. In this paper, we propose
faster consistency checking methods as well as a novel recursive FSR that uses
the projected pixels different than in literature and can handle dynamic masks.
Altogether, we are able to significantly increase the reconstruction quality by
+ 1.01 dB compared to the state-of-the-art recursive reconstruction method
using a fixed mask. Compared to a single frame reconstruction, an average gain
of about + 1.52 dB is achieved for dynamic masks. At the same time, the
computational complexity of the consistency checks is reduced by a factor of 13
compared to the literature algorithm.


title: Non-minimally assisted chaotic inflation

abstract:
  Conventional wisdom says that a chaotic inflation model with a power-law
potential is ruled out by the recent Planck-BICEP/Keck results. We find,
however, that the model can be assisted by a non-minimally coupled scalar field
and still provides a successful inflation. Considering a power-law chaotic
inflation model of the type $V\sim \varphi^n$ with $n=\{2, 4/3, 1, 2/3, 1/3\}$,
we show that $n=1/3$ ($n=\{2/3, 1/3\}$) may be revived with the help of the
quadratic (quartic) non-minimal coupling of the assistant field to gravity.


title:
Performance of the LHC injector chain after the upgrade and potential
  development

abstract:
  The CERN accelerator complex prepares protons for the four big experiments at
the Interaction Regions of the Large Hadron Collider (LHC) as well as for a
number of fixed target experiments, which take beam directly from the LHC
injectors. Between 2010 and 2021, a series of major changes were designed and
implemented to the injector complex under the LHC Injectors Upgrade (LIU)
project, with the main goal to enable the injector chain to produce LHC beams
with doubled intensity and brightness. As a byproduct of these upgrades, the
fixed target beams are also expected to benefit and possibly access parameter
ranges previously unexplored. This letter summarises the main fixed target
beams produced by the LHC injectors, their expected performance after the
upgrade for the various beam types and shows first results from 2021 beam
commissioning.


title: A Rellich type theorem for the generalized oscillator

abstract:
  For the generalized oscillator, we prove a Rellich type theorem, or
characterize the order of growth of eigenfunctions. The proofs are given by an
extensive use of commutator arguments invented recently by Ito and Skibsted.
These arguments are simple and elementary and do not employ energy cut-offs or
microlocal analysis.


title:
Investigation of Physics-Informed Deep Learning for the Prediction of
  Parametric, Three-Dimensional Flow Based on Boundary Data

abstract:
  The placement of temperature sensitive and safety-critical components is
crucial in the automotive industry. It is therefore inevitable, even at the
design stage of new vehicles that these components are assessed for potential
safety issues. However, with increasing number of design proposals, risk
assessment quickly becomes expensive. We therefore present a parameterized
surrogate model for the prediction of three-dimensional flow fields in
aerothermal vehicle simulations. The proposed physics-informed neural network
(PINN) design is aimed at learning families of flow solutions according to a
geometric variation. In scope of this work, we could show that our
nondimensional, multivariate scheme can be efficiently trained to predict the
velocity and pressure distribution for different design scenarios and geometric
scales. The proposed algorithm is based on a parametric minibatch training
which enables the utilization of large datasets necessary for the
three-dimensional flow modeling. Further, we introduce a continuous resampling
algorithm that allows to operate on one static dataset. Every feature of our
methodology is tested individually and verified against conventional CFD
simulations. Finally, we apply our proposed method in context of an exemplary
real-world automotive application.


title: SoK: Differential Privacy on Graph-Structured Data

abstract:
  In this work, we study the applications of differential privacy (DP) in the
context of graph-structured data. We discuss the formulations of DP applicable
to the publication of graphs and their associated statistics as well as machine
learning on graph-based data, including graph neural networks (GNNs). The
formulation of DP in the context of graph-structured data is difficult, as
individual data points are interconnected (often non-linearly or sparsely).
This connectivity complicates the computation of individual privacy loss in
differentially private learning. The problem is exacerbated by an absence of a
single, well-established formulation of DP in graph settings. This issue
extends to the domain of GNNs, rendering private machine learning on
graph-structured data a challenging task. A lack of prior systematisation work
motivated us to study graph-based learning from a privacy perspective. In this
work, we systematise different formulations of DP on graphs, discuss challenges
and promising applications, including the GNN domain. We compare and separate
works into graph analysis tasks and graph learning tasks with GNNs. Finally, we
conclude our work with a discussion of open questions and potential directions
for further research in this area.


title:
Angle-resolved photoemission spectroscopy study of the charge density
  wave order in layered semiconductor EuTe4

abstract:
  Layered tellurides have been extensively studied as a platform for
investigating the Fermi surface (FS) nesting-driven charge density wave (CDW)
states. EuTe4, one of quasi-two-dimensional (quasi-2D) binary rare-earth
tetratellurides CDW compounds, with unconventional hysteretic transition, is
currently receiving much attention. Here, the CDW modulation vector, momentum
and temperature dependence of CDW gaps in EuTe4 are investigated using
angle-resolved photoemission spectroscopy. Our results reveal that (i) a FS
nesting vector q ~ 0.67 b* drives the formation of CDW state, (ii) a large
anisotropic CDW gap is fully open in the whole FS, and maintains a considerable
size even at 300 K, leading to appearance of semiconductor properties, (iii) an
abnormal non-monotonic increase of CDW gap in magnitude as a function of
temperature, (iv) an extra, larger gap opens at a higher binding energy due to
the interaction between the different orbits of the main bands.


title:
Simulation-Driven Training of Vision Transformers Enabling Metal
  Segmentation in X-Ray Images

abstract:
  In several image acquisition and processing steps of X-ray radiography,
knowledge of the existence of metal implants and their exact position is highly
beneficial (e.g. dose regulation, image contrast adjustment). Another
application which would benefit from an accurate metal segmentation is cone
beam computed tomography (CBCT) which is based on 2D X-ray projections. Due to
the high attenuation of metals, severe artifacts occur in the 3D X-ray
acquisitions. The metal segmentation in CBCT projections usually serves as a
prerequisite for metal artifact avoidance and reduction algorithms. Since the
generation of high quality clinical training is a constant challenge, this
study proposes to generate simulated X-ray images based on CT data sets
combined with self-designed computer aided design (CAD) implants and make use
of convolutional neural network (CNN) and vision transformer (ViT) for metal
segmentation. Model test is performed on accurately labeled X-ray test datasets
obtained from specimen scans. The CNN encoder-based network like U-Net has
limited performance on cadaver test data with an average dice score below 0.30,
while the metal segmentation transformer with dual decoder (MST-DD) shows high
robustness and generalization on the segmentation task, with an average dice
score of 0.90. Our study indicates that the CAD model-based data generation has
high flexibility and could be a way to overcome the problem of shortage in
clinical data sampling and labelling. Furthermore, the MST-DD approach
generates a more reliable neural network in case of training on simulated data.


title: Neural Compression-Based Feature Learning for Video Restoration

abstract:
  How to efficiently utilize the temporal features is crucial, yet challenging,
for video restoration. The temporal features usually contain various noisy and
uncorrelated information, and they may interfere with the restoration of the
current frame. This paper proposes learning noise-robust feature
representations to help video restoration. We are inspired by that the neural
codec is a natural denoiser. In neural codec, the noisy and uncorrelated
contents which are hard to predict but cost lots of bits are more inclined to
be discarded for bitrate saving. Therefore, we design a neural compression
module to filter the noise and keep the most useful information in features for
video restoration. To achieve robustness to noise, our compression module
adopts a spatial channel-wise quantization mechanism to adaptively determine
the quantization step size for each position in the latent. Experiments show
that our method can significantly boost the performance on video denoising,
where we obtain 0.13 dB improvement over BasicVSR++ with only 0.23x FLOPs.
Meanwhile, our method also obtains SOTA results on video deraining and
dehazing.


title:
Quasinormal modes of Schwarzschild black holes in metric affine
  Chern-Simons theory

abstract:
  We reformulate the Chern-Simons modified gravity in the metric-affine
formalism, by enlarging the Pontryagin density with homothetic curvature terms
which restore projective invariance without spoiling topologicity. The latter
is then violated by promoting the coupling of the Chern-Simons term to a
(pseudo)-scalar field. We derive the perturbative solutions for torsion and
nonmetricity from the background fields, and we describe the dynamics for the
resulting linearized metric and the scalar fields in a Schwarzschild black hole
background. Then, by adopting numerical techniques we compute the quasinormal
mode spectrum and the late-time tails for scalar and metric perturbations.


title:
Universal Conditional Masked Language Pre-training for Neural Machine
  Translation

abstract:
  Pre-trained sequence-to-sequence models have significantly improved Neural
Machine Translation (NMT). Different from prior works where pre-trained models
usually adopt an unidirectional decoder, this paper demonstrates that
pre-training a sequence-to-sequence model but with a bidirectional decoder can
produce notable performance gains for both Autoregressive and
Non-autoregressive NMT. Specifically, we propose CeMAT, a conditional masked
language model pre-trained on large-scale bilingual and monolingual corpora in
many languages. We also introduce two simple but effective methods to enhance
the CeMAT, aligned code-switching & masking and dynamic dual-masking. We
conduct extensive experiments and show that our CeMAT can achieve significant
performance improvement for all scenarios from low- to extremely high-resource
languages, i.e., up to +14.4 BLEU on low resource and +7.9 BLEU improvements on
average for Autoregressive NMT. For Non-autoregressive NMT, we demonstrate it
can also produce consistent performance gains, i.e., up to +5.3 BLEU. To the
best of our knowledge, this is the first work to pre-train a unified model for
fine-tuning on both NMT tasks. Code, data, and pre-trained models are available
at https://github.com/huawei-noah/Pretrained-Language-Model/CeMAT


title: Eventually homological isomorphisms and Gorenstein projective modules

abstract:
  We prove that a certain eventually homological isomorphism between module
categories induces a triangle equivalence between their singularity categories,
Gorenstein defect categories and the stable categories of Gorenstein projective
modules. Further, we show that Auslander-Reiten conjecture and Gorenstein
symmetry conjecture can be reduced by eventually homological isomorphisms.
Applying the results to arrow removal and vertex removal, we describe the
Gorenstein projective modules over some non-monomial algebras, and we verify
the Auslander-Reiten conjecture for certain algebras.


title:
Atomic-Scale Visualization of Chiral Charge Density Wave States and
  Their Reversible Transition

abstract:
  Chirality is essential for various amazing phenomena in life and matter.
However,chirality and its switching in electronic superlattices, such as charge
density wave(CDW) arrays, remain elusive. In this study, we characterize the
chirality transition with atom-resolution imaging in a single-layer NbSe2 CDW
pattern by technique of scanning tunneling microscopy. The atomic lattice of
the CDW array is found continuous and intact although its chirality is
switched. Several intermediate states are tracked by time-resolved imaging,
revealing the fast and dynamic chirality transition. Importantly, the switching
is reversibly realized with an external electric-field. Our findings unveil the
delicate transition process of chiral CDW array in a 2D crystal down to the
atomic scale and may be applicable for future nanoscale devices.


title:
Electronic transport in double-nanowire superconducting islands with
  multiple terminals

abstract:
  We characterize in-situ grown parallel nanowires bridged by a superconducting
island. The magnetic-field and temperature dependence of Coulomb blockade peaks
measured across different pairs of nanowire ends are consistent with a sub-gap
state extended over the hybrid parallel-nanowire island. Being gate-tunable,
accessible by multiple terminals and free of quasiparticle poisoning, these
nanowires show promise for the implementation of several proposals that rely on
parallel nanowire platforms.


title:
Obtaining Smoothly Navigable Approximation Sets in Bi-Objective
  Multi-Modal Optimization

abstract:
  Even if a Multi-modal Multi-Objective Evolutionary Algorithm (MMOEA) is
designed to find all locally optimal approximation sets of a Multi-modal
Multi-objective Optimization Problem (MMOP), there is a risk that the found
approximation sets are not smoothly navigable because the solutions belong to
various niches, reducing the insight for decision makers. Moreover, when the
multi-modality of MMOPs increases, this risk grows and the trackability of
finding all locally optimal approximation sets decreases. To tackle these
issues, two new MMOEAs are proposed: Multi-Modal B\'ezier Evolutionary
Algorithm (MM-BezEA) and Set B\'ezier Evolutionary Algorithm (Set-BezEA). Both
MMOEAs produce approximation sets that cover individual niches and exhibit
inherent decision-space smoothness as they are parameterized by B\'ezier
curves. MM-BezEA combines the concepts behind the recently introduced BezEA and
MO-HillVallEA to find all locally optimal approximation sets. Set-BezEA employs
a novel multi-objective fitness function formulation to find limited numbers of
diverse, locally optimal, approximation sets for MMOPs of high multi-modality.
Both algorithms, but especially MM-BezEA, are found to outperform the MMOEAs
MO_Ring_PSO_SCD and MO-HillVallEA on MMOPs of moderate multi-modality with
linear Pareto sets. Moreover, for MMOPs of high multi-modality, Set-BezEA is
found to indeed be able to produce high-quality approximation sets, each
pertaining to a single niche.


title:
HSC4D: Human-centered 4D Scene Capture in Large-scale Indoor-outdoor
  Space Using Wearable IMUs and LiDAR

abstract:
  We propose Human-centered 4D Scene Capture (HSC4D) to accurately and
efficiently create a dynamic digital world, containing large-scale
indoor-outdoor scenes, diverse human motions, and rich interactions between
humans and environments. Using only body-mounted IMUs and LiDAR, HSC4D is
space-free without any external devices' constraints and map-free without
pre-built maps. Considering that IMUs can capture human poses but always drift
for long-period use, while LiDAR is stable for global localization but rough
for local positions and orientations, HSC4D makes both sensors complement each
other by a joint optimization and achieves promising results for long-term
capture. Relationships between humans and environments are also explored to
make their interaction more realistic. To facilitate many down-stream tasks,
like AR, VR, robots, autonomous driving, etc., we propose a dataset containing
three large scenes (1k-5k $m^2$) with accurate dynamic human motions and
locations. Diverse scenarios (climbing gym, multi-story building, slope, etc.)
and challenging human activities (exercising, walking up/down stairs, climbing,
etc.) demonstrate the effectiveness and the generalization ability of HSC4D.
The dataset and code are available at http://www.lidarhumanmotion.net/hsc4d/.


title:
Critical-point phenomena and finite-size scaling in mean-field
  equal-coupling photonic networks

abstract:
  The mean-field optical phase transition in multimode equal-coupling photonic
networks is studied by temporal evolution of the nonlinear equations of motion
of the coupled modes. Analogies to statistical mechanics models of interacting
classical spins, built upon the correspondence between complex-valued modes and
two-component spins, are employed to define two-component and single-component
order parameters. A comprehensive finite-size scaling analysis is performed to
estimate critical points and exponents of a second-order phase transition,
driven by the optical energy per mode. Equilibrium properties of the system are
compared to exact results whenever applicable. Considering various parameter
settings, our results confirm the mean-field nature of the transition and
establish the critical line in the nonlinearity--energy-density phase diagram.
Critical scaling leads to infer the upper critical dimension $d_c=4$.
Connection to thermodynamic quantities is established by means of a kinetic
temperature with appropriate zero-temperature limit. In the low temperature
phase (low energy per mode), spins align. In the high temperature phase (large
energy per mode), spins rotate independent of one another.


title: Mapping Conductance and Switching Behavior of Graphene Devices In Situ

abstract:
  Graphene has been proposed for use in various nanodevice designs, many of
which harness emergent quantum properties for device functionality. However,
visualization, measurement, and manipulation become non-trivial at nanometer
and atomic scales, representing a significant challenge for device fabrication,
characterization, and optimization at length scales where quantum effects
emerge. Here, we present proof of principle results at the crossroads between
2D nanoelectronic devices, e-beam-induced modulation, and imaging with
secondary electron e-beam induced currents (SEEBIC). We introduce a device
platform compatible with scanning transmission electron microscopy
investigations. We then show how the SEEBIC imaging technique can be used to
visualize conductance and connectivity in single layer graphene nanodevices,
even while supported on a thicker substrate (conditions under which
conventional imaging fails). Finally, we show that the SEEBIC imaging technique
can detect subtle differences in charge transport through time in non-ohmic
graphene nanoconstrictions indicating the potential to reveal dynamic
electronic processes.


title:
A Mixed Finite Element Method for a Class of Evolution Differential
  Equations with $p$-Laplacian and Memory

abstract:
  We present a new mixed finite element method for a class of parabolic
equations with $p$-Laplacian and nonlinear memory. The applicability, stability
and convergence of the method are studied. First, the problem is written in a
mixed formulation as a system of one parabolic equation and a Volterra
equation. Then, the system is discretized in the space variable using the
finite element method with Lagrangian basis of degree $r\geq1$. Finally, the
Cranck-Nicolson method with the trapezoidal quadrature is applied to discretize
the time variable. For each method, we establish existence, uniqueness and
regularity of the solutions. The convergence order is found to be dependent on
the parameter $p$ on the $p$-Laplacian in the sense that it decreases as $p$
increases.


title: Centrality Measures in multi-layer Knowledge Graphs

abstract:
  Knowledge graphs play a central role for linking different data which leads
to multiple layers. Thus, they are widely used in big data integration,
especially for connecting data from different domains. Few studies have
investigated the questions how multiple layers within graphs impact methods and
algorithms developed for single-purpose networks, for example social networks.
This manuscript investigates the impact of multiple layers on centrality
measures compared to single-purpose graph. In particular, (a) we develop an
experimental environment to (b) evaluate two different centrality measures -
degree and betweenness centrality - on random graphs inspired by social network
analysis: small-world and scale-free networks. The presented approach (c) shows
that the graph structures and topology has a great impact on its robustness for
additional data stored. Although the experimental analysis of random graphs
allows us to make some basic observations we will (d) make suggestions for
additional research on particular graph structures that have a great impact on
the stability of networks.


title: Sharp critical thresholds for a class of nonlocal traffic flow models

abstract:
  We study a class of traffic flow models with nonlocal look-ahead
interactions. The global regularity of solutions depend on the initial data. We
obtain sharp critical threshold conditions that distinguish the initial data
into a trichotomy: subcritical initial conditions lead to global smooth
solutions, while two types of supercritical initial conditions lead to two
kinds of finite time shock formations. The existence of non-trivial subcritical
initial data indicates that the nonlocal look-ahead interactions can help avoid
shock formations, and hence prevent the creation of traffic jams.


title: SCL and mixed SCL are not equivalent for surface groups

abstract:
  For the pair of the surface group $G=\pi_1(\Sigma_{\ell})$ of genus at least
$2$ and its commutator subgroup
$N=[\pi_1(\Sigma_{\ell}),\pi_1(\Sigma_{\ell})]$, the stable commutator length
$\mathrm{scl}_{G}$ and the stable mixed commutator length $\mathrm{scl}_{G,N}$
are not equivalent. This pair is the first example of such $(G,N)$ in which $G$
is finitely generated.


title: Observation of banded spherulite in a pure compound by rhythmic growth

abstract:
  Banded spherulitic growth of crystal is observed in some materials with
spherically symmetric growth front and periodic radial variation of
birefringence. This variation of birefringence in quasi two dimensional
geometry produces concentric interference colour bands when viewed through
crossed polarisers. In most materials, the banded spherulites are found to be
formed by radially oriented periodically twisted fibrillar crystallites. Here,
we report the formation of banded spherulites due to the rhythmic growth of
concentric crystallite-rich and crystallite-poor bands for a pure compound
consisting of strongly polar rod like molecules. The compound exhibits
coexistence of untwisted fibrillar crystallites and an amorphous phase in its
most stable solid state. On sufficient supercooling of the sample from its
melting point, the banded spherulites are formed with a periodic variation of
composition of untwisted radially aligned fibrillar crystallites and an
amorphous solid phase. We have developed a time dependent Ginzburg-Landau model
to account for the observed banded spherulitic growth.


title: Simplicity of augmentations of codimension 1 germs or by Morse functions

abstract:
  We study the simplicity of map-germs obtained by the operation of
augmentation and describe how to obtain their versal unfoldings. When the
augmentation comes from an $\mathscr{A}_e$-codimension 1 germ or the augmenting
function is a Morse function, we give a complete characterisation for
simplicity. These characterisations yield all the simple augmentations in all
known classifications of $\mathscr{A}$-simple monogerms except for one ($F_4$
in Mond's list from $\mathbb{C}^2$ to $\mathbb{C}^3$). On the other hand, this
gives a method to produce examples of finitely determined non-simple germs with
any number of modal parameters. Moreover, using our results we produce a list
of simple augmentations from $\mathbb{C}^4$ to $\mathbb{C}^4$.


title:
Frequency-Selective Mesh-to-Mesh Resampling for Color Upsampling of
  Point Clouds

abstract:
  With the increased use of virtual and augmented reality applications, the
importance of point cloud data rises. High-quality capturing of point clouds is
still expensive and thus, the need for point cloud super-resolution or point
cloud upsampling techniques emerges. In this paper, we propose an interpolation
scheme for color upsampling of three-dimensional color point clouds. As a point
cloud represents an object's surface in three-dimensional space, we first
conduct a local transform of the surface into a two-dimensional plane.
Secondly, we propose to apply a novel Frequency-Selective Mesh-to-Mesh
Resampling (FSMMR) technique for the interpolation of the points in 2D. FSMMR
generates a model of weighted superpositions of basis functions on scattered
points. This model is then evaluated for the final points in order to increase
the resolution of the original point cloud. Evaluation shows that our approach
outperforms common interpolation schemes. Visual comparisons of the jaguar
point cloud underlines the quality of our upsampling results. The high
performance of FSMMR holds for various sampling densities of the input point
cloud.


title:
Neighbourhood semantics and axioms for strategic fragment of classical
  stit logic

abstract:
  STIT (sees to it that) semantics is one of the most prominent tools in modal
logic of agency, widely used among both philosophers and responsible AI
scholars. STIT logic surveys the properties of agents seeing to it that some
state of affairs holds without specifying concrete actions by which that state
of affairs is guaranteed. In comparison with other multi-agent modal logics,
the main advantage of STIT theories is expressive power. STIT logic allows to
study not only statements about agents abilities to perform certain actions (as
it is in variations of Coalition Logic or Propositional Dynamic Logic), but
about what choices they make and what they de-facto achieve as well.
  Nevertheless, in some occasions such expressivity may be redundant. This
paper surveys a specific fragment of classical STIT logic, which has only
strategic modal operator, standing for the fact that agent has an ability to
see to it that some state of affairs holds. The neighbourhood semantics for the
fragment is presented, accompanied with the soundness, canoniciy hence strong
completeness results. Furthermore, the paper presents basic considerations on
epistemic extension of the presented fragment.


title:
Efficient and certified solution of parametrized one-way coupled
  problems through DEIM-based data projection across non-conforming interfaces

abstract:
  One of the major challenges of coupled problems is to manage nonconforming
meshes at the interface between two models and/or domains, due to different
numerical schemes or domains discretizations employed. Moreover, very often
complex submodels depend on (e.g., physical or geometrical) parameters.
Understanding how outputs of interest are affected by parameter variations thus
plays a key role to gain useful insights on the problem's physics; however,
expensive repeated solutions of the problem using high-fidelity, full-order
models are often unaffordable. In this paper, we propose a parametric reduced
order modeling (ROM) technique for parametrized one-way coupled problems made
by a first independent model, the master model, and a second model, the slave
model, that depends on the master model through Dirichlet interface conditions.
We combine a reduced basis (RB) method, applied to each subproblems, with the
discretized empirical interpolation method (DEIM) to efficiently interpolate or
project Dirichlet data across conforming and non-conforming meshes at the
domains interface, building a low-dimensional representation of the overall
coupled problem. The proposed technique is then numerically verified by
considering a series of test cases involving both steady and unsteady problems,
and deriving a-posteriori error estimates on the solution of the coupled
problem in both cases. This work arises from the need to solve staggered
cardiac electrophysiological models and represents the first step towards the
setting of ROM techniques for the more general two-way Dirichlet-Neumann
coupled problems solved with domain decomposition sub-structuring methods, when
interface non-conformity is involved.


title:
Non-Elitist Selection among Survivor Configurations can Improve the
  Performance of Irace

abstract:
  Modern optimization strategies such as evolutionary algorithms, ant colony
algorithms, Bayesian optimization techniques, etc.~come with several parameters
that steer their behavior during the optimization process. To obtain
high-performing algorithm instances, automated algorithm configuration
techniques have been developed. One of the most popular tools is irace, which
evaluates configurations in sequential races, making use of iterated
statistical tests to discard poorly performing configurations. At the end of
the race, a set of elite configurations are selected from those survivor
configurations which were not discarded, using greedy truncation selection.
  We study two alternative selection methods: one keeps the best survivor and
selects the remaining configurations uniformly at random from the set of
survivors while the other applies entropy to maximize the diversity of the
elites. These methods are tested for tuning ant colony optimization algorithms
for traveling salesperson problems and the quadratic assignment problem and
tuning an exact tree search solver for satisfiability problems. The
experimental results show improvement on the tested benchmarks compared to the
default selection of irace. In addition, the obtained results indicate that
non-elitist can obtain diverse algorithm configurations, which encourages us to
explore a wider range of solutions to understand the behavior of algorithms.


title:
Re-shaping Post-COVID-19 Teaching and Learning: A Blueprint of
  Virtual-Physical Blended Classrooms in the Metaverse Era

abstract:
  During the COVID-19 pandemic, most countries have experienced some form of
remote education through video conferencing software platforms. However, these
software platforms fail to reduce immersion and replicate the classroom
experience. The currently emerging Metaverse addresses many of such limitations
by offering blended physical-digital environments. This paper aims to assess
how the Metaverse can support and improve e-learning. We first survey the
latest applications of blended environments in education and highlight the
primary challenges and opportunities. Accordingly, we derive our proposal for a
virtual-physical blended classroom configuration that brings students and
teachers into a shared educational Metaverse. We focus on the system
architecture of the Metaverse classroom to achieve real-time synchronization of
a large number of participants and activities across physical (mixed reality
classrooms) and virtual (remote VR platform) learning spaces. Our proposal
attempts to transform the traditional physical classroom into virtual-physical
cyberspace as a new social network of learners and educators connected at an
unprecedented scale.


title: Electron-beam Introduction of Heteroatomic Pt-Si Structures in Graphene

abstract:
  Electron-beam (e-beam) manipulation of single dopant atoms in an
aberration-corrected scanning transmission electron microscope is emerging as a
method for directed atomic motion and atom-by-atom assembly. Until now, the
dopant species have been limited to atoms closely matched to carbon in terms of
ionic radius and capable of strong covalent bonding with carbon atoms in the
graphene lattice. In situ dopant insertion into a graphene lattice has thus far
been demonstrated only for Si, which is ubiquitously present as a contaminant
in this material. Here, we achieve in situ manipulation of Pt atoms and their
insertion into the graphene host matrix using the e-beam deposited Pt on
graphene as a host system. We further demonstrate a mechanism for stabilization
of the Pt atom, enabled through the formation of Si-stabilized Pt heteroatomic
clusters attached to the graphene surface. This study provides evidence toward
the universality of the e-beam assembly approach, opening a pathway for
exploring cluster chemistry through direct assembly.


title:
Surgical Workflow Recognition: from Analysis of Challenges to
  Architectural Study

abstract:
  Algorithmic surgical workflow recognition is an ongoing research field and
can be divided into laparoscopic (Internal) and operating room (External)
analysis. So far many different works for the internal analysis have been
proposed with the combination of a frame-level and an additional temporal model
to address the temporal ambiguities between different workflow phases. For the
External recognition task, Clip-level methods are in the focus of researchers
targeting the local ambiguities present in the OR scene. In this work we
evaluate combinations of different model architectures for the task of surgical
workflow recognition to provide a fair comparison of the methods for both
Internal and External analysis. We show that methods designed for the Internal
analysis can be transferred to the external task with comparable performance
gains for different architectures.


title:
Speaker recognition using residual signal of linear and nonlinear
  prediction models

abstract:
  This Paper discusses the usefulness of the residual signal for speaker
recognition. It is shown that the combination of both a measure defined over
LPCC coefficients and a measure defined over the energy of the residual signal
gives rise to an improvement over the classical method which considers only the
LPCC coefficients. If the residual signal is obtained from a linear prediction
analysis, the improvement is 2.63% (error rate drops from 6.31% to 3.68%) and
if it is computed through a nonlinear predictive neural nets based model, the
improvement is 3.68%.


title:
Nonlocality of quantum matter corrections and cosmological constant
  running

abstract:
  Semiclassical contributions to the gravitational action include various terms
with zero, two, and four derivatives of the metric as well as nonlocal form
factors for these terms. Contributions to some of these terms could be confused
with others on a specific metric background or for a particular gauge fixing.
We present a critical analysis of the recent works where the tensor structure
and the number of derivatives in the action of gravity were not properly taken
into account. Taking these relevant aspects into account, we show that although
some contributions owing to the quantum fluctuations of massive or massless
scalar as well as fermion and vector fields may be attributed to the "running"
of the cosmological constant, in reality they correspond to the fourth
derivative terms of the action.


title:
On the Complexity of Techniques That Make Transition Systems
  Implementable by Boolean Nets

abstract:
  Synthesis consists in deciding whether a given labeled transition system (TS)
$A$ can be implemented by a net $N$ of type $\tau$. In case of a negative
decision, it may be possible to convert $A$ into an implementable TS $B$ by
applying various modification techniques, like relabeling edges that previously
had the same label, suppressing edges/states/events, etc. It may however be
useful to limit the number of such modifications to stay close to the original
problem, or optimize the technique. In this paper, we show that most of the
corresponding problems are NP-complete if $\tau$ corresponds to the type of
flip-flop nets or some flip-flop net derivatives.


title:
Autonomous Quantum Error Correction in a Four-Photon Kerr Parametric
  Oscillator

abstract:
  Autonomous quantum error correction has gained considerable attention to
avoid complicated measurements and feedback. Despite its simplicity compared
with the conventional measurement-based quantum error correction, it is still a
far from practical technique because of significant hardware overhead. We
propose an autonomous quantum error correction scheme for a rotational
symmetric bosonic code in a four-photon Kerr parametric oscillator. Our scheme
is the simplest possible error correction scheme that can surpass the
break-even point -- it requires only a single continuous microwave tone. We
also introduce an unconditional reset scheme that requires one more continuous
microwave tone in addition to that for the error correction. The key properties
underlying this simplicity are protected quasienergy states of a four-photon
Kerr parametric oscillator and the degeneracy in its quasienergy level
structure. These properties eliminate the need for state-by-state correction in
the Fock basis. Our schemes greatly reduce the complexity of autonomous quantum
error correction and thus may accelerate the use of the bosonic code for
practical quantum computation.


title:
On the energy dependence of the muon transfer rate from hydrogen to
  oxygen

abstract:
  The results of calculations of the muon transfer rate from the 1S state of
muonic hydrogen to the nucleus of a free oxygen atom are presented in the
interval of collision energies from 10^(-4) to 10 eV. The calculations were
performed within a version of the perturbed stationary states method proposed
earlier. The electron screening in the entrance channel of the transfer
reaction was taken into account. A p-wave resonance in the transfer rate is
predicted at collision energies of about 0.1 eV. This result is of interest in
the context of the planned laser experiment on precise measurements of the
hyperfine splitting energy of the 1S state of muonic hydrogen.


title: Weighing the techniques for data optimization in a database

abstract:
  A set of preferred records can be obtained from a large database in a
multi-criteria setting using various computational methods which either depend
on the concept of dominance or on the concept of utility or scoring function
based on the attributes of the database record. A skyline approach relies on
the dominance relationship between different data points to discover
interesting data from a huge database. On the other hand, ranking queries make
use of specific scoring functions to rank tuples in a database. An experimental
evaluation of datasets can provides us with information on the effectiveness of
each of these methods.


title:
Modified gravitational wave propagation: information from strongly
  lensed binaries and the BNS mass function

abstract:
  Modified gravitational wave propagation is a smoking gun of modifications of
gravity at cosmological scales, and can be the most promising observable for
testing such theories. The observation of gravitational waves (GW) in recent
years has allowed us to start probing this effect, and here we briefly review
two promising ways of testing it. We will show that, already with the current
network of detectors, it is possible to reach an interesting accuracy in the
estimation of the $\Xi_0$ parameter (that characterizes modified gravitational
wave propagation, with $\Xi_{0, {\rm GR}} = 1$) and with next generation
facilities, such as the Einstein Telescope, we can get a sub-percent
measurement.


title: Cosmology and modified gravity with dark sirens from GWTC-3

abstract:
  We present the latest measurements of the Hubble parameter and of the
parameter $\Xi_0$ describing modified gravitational wave propagation, obtained
from the third gravitational wave transient catalog, GWTC-3, using the
correlation with galaxy catalogs and information from the source-frame mass
distribution of binary black holes. The latter leads to the tightest bound on
$\Xi_0$ so far, i.e. $\Xi_0 = 1.2^{+0.7}_{-0.7}$ with a flat prior on $\Xi_0$,
and $\Xi_0 = 1.0^{+0.4}_{-0.8}$ with a prior uniform in $\log\Xi_0$ (Max
posterior and $68\%$ HDI). The measurement of $H_0$ is dominated by the single
bright siren GW170817, resulting in $H_0=67^{+9}_{-6} \, \rm km \, s^{-1} \,
Mpc$ when combined with the galaxy catalog.


title: Numerical Study of Viscoelastic Upstream Instability

abstract:
  In this work, we report numerical results on the flow instability and
bifurcation of a viscoelastic fluid in the upstream region of a confined
cylinder in a narrow channel. Two-dimensional direct numerical simulations
based on the FENE-P model (the finite-extensible nonlinear elastic model with
the Peterlin closure) are conducted with numerical stabilization techniques.
Our results show that the macroscopic viscoelastic constitutive relation can
capture the viscoelastic upstream instability reported in previous experiments
for low-Reynolds-number flows. The numerical simulations reveal that the
non-dimensional recirculation length ($L_D$) is affected by the cylinder
blocking rate ($BR$), the Weissenberg number ($Wi$), the viscosity ratio
($\beta$), and the maximum polymer extension ($L$). Close to the onset of
upstream recirculation, depending on the values of $L$ and $\beta$, the
bifurcation can be supercritical or subcritical. $\beta$ has a nonlinear
influence on the upstream recirculation length. This work contributes to our
theoretical understanding of this new instability mechanism in viscoelastic
wake flows.


title:
Continuous-Wave Second-Harmonic Generation in Orientation-Patterned GaP
  Waveguides at Telecom Wavelengths

abstract:
  A new process to produce Orientation-Patterned Gallium Phosphide (OP-GaP) on
GaAs with almost perfectly parallel domain boundaries is presented. Taking
advantage of the chemical selectivity between phosphides and arsenides, OP-GaP
is processed into suspended shallow-ridge waveguides. Efficient Second-Harmonic
Generation from Telecom wavelengths is achieved in both Type-I and Type-II
polarisation configurations. The highest observed conversion efficiency is
\SI{200}{\percent\per\watt\per\centi\meter\squared}, with a bandwidth of
\SI{2.67}{\nano\meter} in a \SI{1}{\milli\meter}-long waveguide. The variation
of the conversion efficiency with wavelength closely follows a squared cardinal
sine function, in excellent agreement with theory, confirming the good
uniformity of the poling period over the entire length of the waveguide.


title:
Theoretical methods for calculating rotational-vibrational-electronic
  transition intensities in triatomic molecules

abstract:
  This article covers few selected aspects of quantum theory of molecular
rotations and vibrations. Triatomic molecules are the simplest systems, which
show qualitative characteristics of larger polyatomic molecules. On the minimal
example of triatomic molecules we demonstrate several theoretical methods used
to calculate highly accurate rotational-vibrational-electronic energies,
wavefunctions and transition intensities. The reader can view this article as a
tutorial, by no means comprehensive, which still touches some of the unsolved
problems in modern theoretical molecular spectroscopy.


title: Depth-aware Neural Style Transfer using Instance Normalization

abstract:
  Neural Style Transfer (NST) is concerned with the artistic stylization of
visual media. It can be described as the process of transferring the style of
an artistic image onto an ordinary photograph. Recently, a number of studies
have considered the enhancement of the depth-preserving capabilities of the NST
algorithms to address the undesired effects that occur when the input content
images include numerous objects at various depths. Our approach uses a deep
residual convolutional network with instance normalization layers that utilizes
an advanced depth prediction network to integrate depth preservation as an
additional loss function to content and style. We demonstrate results that are
effective in retaining the depth and global structure of content images. Three
different evaluation processes show that our system is capable of preserving
the structure of the stylized results while exhibiting style-capture
capabilities and aesthetic qualities comparable or superior to state-of-the-art
methods.


title: On the Properties of Adversarially-Trained CNNs

abstract:
  Adversarial Training has proved to be an effective training paradigm to
enforce robustness against adversarial examples in modern neural network
architectures. Despite many efforts, explanations of the foundational
principles underpinning the effectiveness of Adversarial Training are limited
and far from being widely accepted by the Deep Learning community. In this
paper, we describe surprising properties of adversarially-trained models,
shedding light on mechanisms through which robustness against adversarial
attacks is implemented. Moreover, we highlight limitations and failure modes
affecting these models that were not discussed by prior works. We conduct
extensive analyses on a wide range of architectures and datasets, performing a
deep comparison between robust and natural models.


title:
Peak effect in a superconductor/normal metal strip being in vortex-free
  state

abstract:
  We theoretically predict that the critical current $I_c$ and magnetization
$M$ of hybrid superconductor/normal-metal (SN) strip may have nonmonotonous
dependence on perpendicular magnetic field - so called peak effect. In contrast
to familiar peak effect, which is connected either with vortex entry to the
superconductor or with peculiarities of vortex pinning, the found phenomenon
exists at low fields, in the vortex-free (Meissner) phase. We argue that the
effect appears at specific parameters of studied hybrid structure when its
in-plane current-supervelocity relation has two maxima. We expect that the same
peak effect may exist in two-band superconductors (like MgB$_2$) where similar
current-supervelocity dependence was predicted at low temperatures.


title:
Deterministic relation between optical polarization and lattice symmetry
  revealed in ion-doped single microcrystals

abstract:
  Rare-earth ions doped crystals are of great significance for micro-sensing
and quantum information, whilst the ions in the crystals emit light with
spontaneous partial polarization, which is, though believed to be originated
from the crystal lattice structure, still lacking a deterministic explanation
that can be tested with quantitative accuracy. We report the experimental
evidence showing the profound physical relation between the polarization degree
of light emitted by the doped ion and the lattice symmetry, by demonstrating,
with unprecedented precision, that the lattice constant ratio c/a directly
quantifies the macroscopic effective polar angle of the electric and magnetic
dipoles, which essentially determines the linear polarization degree of the
emission. Based on this discovery, we further propose a pure optical technology
to identify the three-dimensional orientation of a rod-shaped single
microcrystal using the polarization-resolved micro-spectroscopy. Our results,
revealing the physical origin of light polarization in ion-doped crystals, open
the way towards on-demand polarization control with crystallography, and
provide a versatile platform for polarization-based microscale sensing in
dynamical systems.


title: Quantum phases of bosonic chiral molecules in helicity lattices

abstract:
  We study the phase diagram of cold interacting chiral molecules immersed in
helicity lattices by means of an extended Bose-Hubbard model. These recently
proposed lattices have sites with alternating helicity which exert a
discriminatory force on chiral molecules with different handedness. We find
that a strong dipole-dipole repulsion between molecules results in the
polarization of left and right enantiomers. In an experiment, this would
produce a phase separation of enantiomers, acting as an alternative route
towards chiral discrimination.


title:
Generation and structuring of multipartite entanglement in Josephson
  parametric system

abstract:
  Quantum correlations are a vital resource in advanced information processing
based on quantum phenomena. Remarkably, the vacuum state of a quantum field may
act as a key element for the generation of multipartite quantum entanglement.
In this work, we achieve generation of genuine tripartite entangled state and
its control by the use of the phase difference between two continuous pump
tones. We demonstrate control of the subspaces of the covariance matrix for
tripartite bisqueezed state. Furthermore, by optimizing the phase relationships
in a three-tone pumping scheme we explore genuine quadripartite entanglement of
a \textit{generalized} H-graph state ($\mathscr{\tilde{H}}$-graph). Our scheme
provides a comprehensive control toolbox for the entanglement structure and
allows us to demonstrate, for first time to our knowledge, genuine
quadripartite entanglement of microwave modes. All experimental results are
verified with numerical simulations of the nonlinear quantum Langevin equation.
We envision that quantum resources facilitated by multi-pump configurations
offer enhanced prospects for quantum data processing using parametric microwave
cavities.


title:
Single-shot readout of multiple donor electron spins with a gate-based
  sensor

abstract:
  Proposals for large-scale semiconductor spin-based quantum computers require
high-fidelity single-shot qubit readout to perform error correction and read
out qubit registers at the end of a computation. However, as devices scale to
larger qubit numbers integrating readout sensors into densely packed qubit
chips is a critical challenge. Two promising approaches are minimising the
footprint of the sensors, and extending the range of each sensor to read more
qubits. Here we show high-fidelity single-shot electron spin readout using a
nanoscale single-lead quantum dot (SLQD) sensor that is both compact and
capable of reading multiple qubits. Our gate-based SLQD sensor is deployed in
an all-epitaxial silicon donor spin qubit device, and we demonstrate
single-shot readout of three $^{31}$P donor quantum dot electron spins with a
maximum fidelity of 95%. Importantly in our device the quantum dot confinement
potentials are provided inherently by the donors, removing the need for
additional metallic confinement gates and resulting in strong capacitive
interactions between sensor and donor quantum dots. We observe a $1/d^{1.4}$
scaling of the capacitive coupling between sensor and $^{31}$P dots (where $d$
is the sensor-dot distance), compared to $1/d^{2.5-3.0}$ in gate-defined
quantum dot devices. Due to the small qubit size and strong capacitive
interactions in all-epitaxial donor devices, we estimate a single sensor can
achieve single-shot readout of approximately 15 qubits in a linear array,
compared to 3-4 qubits for a similar sensor in a gate-defined quantum dot
device. Our results highlight the potential for spin qubit devices with
significantly reduced sensor densities.


title:
Fine-tuning Global Model via Data-Free Knowledge Distillation for
  Non-IID Federated Learning

abstract:
  Federated Learning (FL) is an emerging distributed learning paradigm under
privacy constraint. Data heterogeneity is one of the main challenges in FL,
which results in slow convergence and degraded performance. Most existing
approaches only tackle the heterogeneity challenge by restricting the local
model update in client, ignoring the performance drop caused by direct global
model aggregation. Instead, we propose a data-free knowledge distillation
method to fine-tune the global model in the server (FedFTG), which relieves the
issue of direct model aggregation. Concretely, FedFTG explores the input space
of local models through a generator, and uses it to transfer the knowledge from
local models to the global model. Besides, we propose a hard sample mining
scheme to achieve effective knowledge distillation throughout the training. In
addition, we develop customized label sampling and class-level ensemble to
derive maximum utilization of knowledge, which implicitly mitigates the
distribution discrepancy across clients. Extensive experiments show that our
FedFTG significantly outperforms the state-of-the-art (SOTA) FL algorithms and
can serve as a strong plugin for enhancing FedAvg, FedProx, FedDyn, and
SCAFFOLD.


title:
Symmetry-Based Representations for Artificial and Biological General
  Intelligence

abstract:
  Biological intelligence is remarkable in its ability to produce complex
behaviour in many diverse situations through data efficient, generalisable and
transferable skill acquisition. It is believed that learning "good" sensory
representations is important for enabling this, however there is little
agreement as to what a good representation should look like. In this review
article we are going to argue that symmetry transformations are a fundamental
principle that can guide our search for what makes a good representation. The
idea that there exist transformations (symmetries) that affect some aspects of
the system but not others, and their relationship to conserved quantities has
become central in modern physics, resulting in a more unified theoretical
framework and even ability to predict the existence of new particles. Recently,
symmetries have started to gain prominence in machine learning too, resulting
in more data efficient and generalisable algorithms that can mimic some of the
complex behaviours produced by biological intelligence. Finally, first
demonstrations of the importance of symmetry transformations for representation
learning in the brain are starting to arise in neuroscience. Taken together,
the overwhelming positive effect that symmetries bring to these disciplines
suggest that they may be an important general framework that determines the
structure of the universe, constrains the nature of natural tasks and
consequently shapes both biological and artificial intelligence.