title:
Optimal Interpolation Data for PDE-based Compression of Images with
  Noise

abstract:
  We introduce and discuss shape-based models for finding the best
interpolation data in the compression of images with noise. The aim is to
reconstruct missing regions by means of minimizing a data fitting term in the
$L^2$-norm between the images and their reconstructed counterparts using
time-dependent PDE inpainting. We analyze the proposed models in the framework
of the $\Gamma$-convergence from two different points of view. First, we
consider a continuous stationary PDE model, obtained by focusing on the first
iteration of the discretized time-dependent PDE, and get pointwise information
on the "relevance" of each pixel by a topological asymptotic method. Second, we
introduce a finite dimensional setting of the continuous model based on "fat
pixels" (balls with positive radius), and we study by $\Gamma$-convergence the
asymptotics when the radius vanishes. Numerical computations are presented that
confirm the usefulness of our theoretical findings for non-stationary PDE-based
image compression.


title:
Convergence to line and surface energies in nematic liquid crystal
  colloids with external magnetic field

abstract:
  We use the Landau-de Gennes energy to describe a particle immersed into
nematic liquid crystals with a constant applied magnetic field. We derive a
limit energy in a regime where both line and point defects are present, showing
quantitatively that the close-to-minimal energy is asymptotically concentrated
on lines and surfaces nearby or on the particle. We also discuss regularity of
minimizers and optimality conditions for the limit energy.


title:
Privacy-Preserving In-Bed Pose Monitoring: A Fusion and Reconstruction
  Study

abstract:
  Recently, in-bed human pose estimation has attracted the interest of
researchers due to its relevance to a wide range of healthcare applications.
Compared to the general problem of human pose estimation, in-bed pose
estimation has several inherent challenges, the most prominent being frequent
and severe occlusions caused by bedding. In this paper we explore the effective
use of images from multiple non-visual and privacy-preserving modalities such
as depth, long-wave infrared (LWIR) and pressure maps for the task of in-bed
pose estimation in two settings. First, we explore the effective fusion of
information from different imaging modalities for better pose estimation.
Secondly, we propose a framework that can estimate in-bed pose estimation when
visible images are unavailable, and demonstrate the applicability of fusion
methods to scenarios where only LWIR images are available. We analyze and
demonstrate the effect of fusing features from multiple modalities. For this
purpose, we consider four different techniques: 1) Addition, 2) Concatenation,
3) Fusion via learned modal weights, and 4) End-to-end fully trainable
approach; with a state-of-the-art pose estimation model. We also evaluate the
effect of reconstructing a data-rich modality (i.e., visible modality) from a
privacy-preserving modality with data scarcity (i.e., long-wavelength infrared)
for in-bed human pose estimation. For reconstruction, we use a conditional
generative adversarial network. We conduct ablative studies across different
design decisions of our framework. This includes selecting features with
different levels of granularity, using different fusion techniques, and varying
model parameters. Through extensive evaluations, we demonstrate that our method
produces on par or better results compared to the state-of-the-art.


title:
PointMatch: A Consistency Training Framework for Weakly
  SupervisedSemantic Segmentation of 3D Point Clouds

abstract:
  Semantic segmentation of point cloud usually relies on dense annotation that
is exhausting and costly, so it attracts wide attention to investigate
solutions for the weakly supervised scheme with only sparse points annotated.
Existing works start from the given labels and propagate them to highly-related
but unlabeled points, with the guidance of data, e.g. intra-point relation.
However, it suffers from (i) the inefficient exploitation of data information,
and (ii) the strong reliance on labels thus is easily suppressed when given
much fewer annotations. Therefore, we propose a novel framework, PointMatch,
that stands on both data and label, by applying consistency regularization to
sufficiently probe information from data itself and leveraging weak labels as
assistance at the same time. By doing so, meaningful information can be learned
from both data and label for better representation learning, which also enables
the model more robust to the extent of label sparsity. Simple yet effective,
the proposed PointMatch achieves the state-of-the-art performance under various
weakly-supervised schemes on both ScanNet-v2 and S3DIS datasets, especially on
the settings with extremely sparse labels, e.g. surpassing SQN by 21.2% and
17.2% on the 0.01% and 0.1% setting of ScanNet-v2, respectively.


title: Relational Causal Models with Cycles:Representation and Reasoning

abstract:
  Causal reasoning in relational domains is fundamental to studying real-world
social phenomena in which individual units can influence each other's traits
and behavior. Dynamics between interconnected units can be represented as an
instantiation of a relational causal model; however, causal reasoning over such
instantiation requires additional templating assumptions that capture feedback
loops of influence. Previous research has developed lifted representations to
address the relational nature of such dynamics but has strictly required that
the representation has no cycles. To facilitate cycles in relational
representation and learning, we introduce relational $\sigma$-separation, a new
criterion for understanding relational systems with feedback loops. We also
introduce a new lifted representation, $\sigma$-abstract ground graph which
helps with abstracting statistical independence relations in all possible
instantiations of the cyclic relational model. We show the necessary and
sufficient conditions for the completeness of $\sigma$-AGG and that relational
$\sigma$-separation is sound and complete in the presence of one or more cycles
with arbitrary length. To the best of our knowledge, this is the first work on
representation of and reasoning with cyclic relational causal models.


title:
Targeting occupant feedback using digital twins: Adaptive
  spatial-temporal thermal preference sampling to optimize personal comfort
  models

abstract:
  Collecting intensive longitudinal thermal preference data from building
occupants is emerging as an innovative means of characterizing the performance
of buildings and the people who use them. These techniques have occupants
giving subjective feedback using smartphones or smartwatches frequently over
the course of days or weeks. The intention is that the data will be collected
with high spatial and temporal diversity to best characterize a building and
the occupant's preferences. But in reality, leaving the occupant to respond in
an ad-hoc or fixed interval way creates unneeded survey fatigue and redundant
data. This paper outlines a scenario-based (virtual experiment) method for
optimizing data sampling using a smartwatch to achieve comparable accuracy in a
personal thermal preference model with fewer data. This method uses
BIM-extracted spatial data and Graph Neural Network-based (GNN) modeling to
find regions of similar comfort preference to identify the best scenarios for
triggering the occupant to give feedback. This method is compared to two
baseline scenarios that use conventional zoning and a generic 4x4 square meter
grid method from two field-based data sets. The results show that the proposed
Build2Vec method has an 18-23\% higher overall sampling quality than the
spaces-based and square-grid-based sampling methods. The Build2Vec method also
performs similar to the baselines when removing redundant occupant feedback
points but with better scalability potential.


title:
Electrical barriers and their elimination by tuning (Zn,Mg)O composition
  in Cu(In,Ga)S2: Systematic approach to achieve over 14% power conversion
  efficiency

abstract:
  Traditional CdS buffer layer in selenium-free Cu(In,Ga)S2 solar cells leads
to reduced open-circuit voltage because of a negative conduction band offset at
the Cu(In,Ga)S2/CdS interface. Reducing this loss necessitates the substitution
of CdS by an alternative buffer layer. However, the substitute buffer layer may
introduce electrical barriers in the device due to unfavorable band alignment
at the other interfaces such as between buffer/ZnO i-layer. This study aims to
reduce interface recombinations and eliminate electrical barriers in
Cu(In,Ga)S2 solar cells using a combination of Zn1-xMgxO and Al-doped Zn1-xMgxO
buffer and i-layer combination deposited using atomic layer deposition and
magnetron sputtering, respectively. The devices prepared with these layers are
characterized by current-voltage and photoluminescence measurements. Numerical
simulations are performed to comprehend the influence of electrical barriers on
the device characteristics. An optimal composition of Zn1 xMgxO x = 0.27 is
identified for a suitable conduction band alignment with Cu(In,Ga)S2 with a
bandgap of ~1.6 eV, suppressing interface recombination and avoiding barriers.
Optimized buffer composition together with a suitable i-layer led to a device
with 14 % efficiency and an open-circuit voltage of 943 mV. A comparison of
optoelectronic measurements for devices prepared with ZnO and Al:(Zn,Mg)O shows
the necessity to replace the ZnO i-layer with Al:(Zn,Mg)O i-layer for a
high-efficiency device.


title:
Detecting a single atom in a cavity using the $\chi^{(2)}$ nonlinear
  medium

abstract:
  We propose a protocol for detecting a single atom in a cavity with the help
of the $\chi^{(2)}$ nonlinear medium. When the $\chi^{(2)}$ nonlinear medium is
driven by an external laser field, the cavity mode will be squeezed, and thus
one can obtain an exponentially enhanced light-matter coupling. Such a strong
coupling between the atom and the cavity field can significantly change the
output photon flux, the quantum fluctuations, the quantum statistical property,
and the photon number distributions of the cavity field. This provides
practical strategies to determine the presence or absence of an atom in a
cavity. The proposed protocol exhibits some advantages, such as controllable
squeezing strength and exponential increase of atom-cavity coupling strength,
which make the experimental phenomenon more obvious. We hope that this protocol
can supplement the existing intracavity single-atom detection protocols and
provide a promise for quantum sensing in different quantum systems.


title: Incorporating Constituent Syntax for Coreference Resolution

abstract:
  Syntax has been shown to benefit Coreference Resolution from incorporating
long-range dependencies and structured information captured by syntax trees,
either in traditional statistical machine learning based systems or recently
proposed neural models. However, most leading systems use only dependency
trees. We argue that constituent trees also encode important information, such
as explicit span-boundary signals captured by nested multi-word phrases, extra
linguistic labels and hierarchical structures useful for detecting anaphora. In
this work, we propose a simple yet effective graph-based method to incorporate
constituent syntactic structures. Moreover, we also explore to utilise
higher-order neighbourhood information to encode rich structures in constituent
trees. A novel message propagation mechanism is therefore proposed to enable
information flow among elements in syntax trees. Experiments on the English and
Chinese portions of OntoNotes 5.0 benchmark show that our proposed model either
beats a strong baseline or achieves new state-of-the-art performance. (Code is
available at https://github.com/Fantabulous-J/Coref-Constituent-Graph)


title:
Superconductivity and weak anti-localization in nodal-line semimetal
  SnTaS_2

abstract:
  Topological semimetals with superconducting properties provide an emergent
platform to explore the properties of topological superconductors. We report
magnetization, and magneto-transport measurements on high quality single
crystals of transition metal dichalcogenide SnTaS2. It is a nodal line
semimetal with superconducting transition below Tc = 2.9 K. Moderate anisotropy
(3.1) is observed in upper critical fields along H||c and H||ab plane. In the
normal state we observe large magneto-resistance and weak anti-localization
effect that provide unambiguous confirmation of topological features in SnTaS2.
Therefore, genuine topological characteristics can be studied in this material,
particularly with regard to microscopic origin of order parameter symmetry.


title:
nnSpeech: Speaker-Guided Conditional Variational Autoencoder for
  Zero-shot Multi-speaker Text-to-Speech

abstract:
  Multi-speaker text-to-speech (TTS) using a few adaption data is a challenge
in practical applications. To address that, we propose a zero-shot
multi-speaker TTS, named nnSpeech, that could synthesis a new speaker voice
without fine-tuning and using only one adaption utterance. Compared with using
a speaker representation module to extract the characteristics of new speakers,
our method bases on a speaker-guided conditional variational autoencoder and
can generate a variable Z, which contains both speaker characteristics and
content information. The latent variable Z distribution is approximated by
another variable conditioned on reference mel-spectrogram and phoneme.
Experiments on the English corpus, Mandarin corpus, and cross-dataset proves
that our model could generate natural and similar speech with only one adaption
speech.


title:
Impact of compactlike and asymmetric configurations of thick branes on
  the scalar-tensor representation of $f\left(R,T\right)$ gravity

abstract:
  In this work we study braneworld models in generalized $f\left(R,T\right)$
gravity theories where the action depends on a function of the Ricci scalar $R$
and the trace of the stress-energy tensor $T$. We explore the so-called
scalar-tensor representation of the theory, where the dependence on $R$ and $T$
are exchanged for two auxiliary real scalar fields. We introduce a first-order
formalism to relate the auxiliary fields with the source field of brane and
analyze four distinct possibilities of current interest regarding
compactification and asymmetry of the brane. We investigate the behavior of the
auxiliary fields and their stability in each one of the four cases. For the
models where the solution of the source field engenders compact behavior, the
auxiliary fields exhibit a hybrid structure, behaving differently inside and
outside the compact region described by the source configuration. In
particular, we found that the solutions of the auxiliary fields are
significantly modified in models where the geometry is strongly influenced by
the source field. Moreover, for the model capable of engendering asymmetric
features, the two auxiliary fields also respond to modifications on the
parameter that controls the asymmetry of the system.


title:
The optimal initial datum for a class of reaction-advection-diffusion
  equations

abstract:
  We consider a reaction-diffusion model with a drift term in a bounded domain.
Given a time $T,$ we prove the existence and uniqueness of an initial datum
that maximizes the total mass $\textstyle{\int_\Omega u(T,x)\mathrm{d}x}$ in
the presence of an advection term. In a population dynamics context, this
optimal initial datum can be understood as the best distribution of the initial
population that leads to a maximal the total population at a prefixed time $T.$
We also compare the total masses at a time $T$ in two cases: depending on
whether an advection term is present in the medium or not. We prove that the
presence of a large enough advection enhances the total mass.


title:
Extraction of the interaction parameters for $\alpha-$RuCl$_3$ from
  neutron data using machine learning

abstract:
  Single crystal inelastic neutron scattering data contain rich information
about the structure and dynamics of a material. Yet the challenge of matching
sophisticated theoretical models with large data volumes is compounded by
computational complexity and the ill-posed nature of the inverse scattering
problem. Here we utilize a novel machine-learning-assisted framework featuring
multiple neural network architectures to address this via high-dimensional
modeling and numerical methods. A comprehensive data set of diffraction and
inelastic neutron scattering measured on the Kitaev material $\alpha-$RuCl$_3$
is processed to extract its Hamiltonian. Semiclassical Landau-Lifshitz dynamics
and Monte-Carlo simulations were employed to explore the parameter space of an
extended Kitaev-Heisenberg Hamiltonian. A machine-learning-assisted iterative
algorithm was developed to map the uncertainty manifold to match experimental
data; a non-linear autoencoder used to undertake information compression; and
Radial Basis networks utilized as fast surrogates for diffraction and dynamics
simulations to predict potential spin Hamiltonians with uncertainty. Exact
diagonalization calculations were employed to assess the impact of quantum
fluctuations on the selected parameters around the best prediction.


title:
HRel: Filter Pruning based on High Relevance between Activation Maps and
  Class Labels

abstract:
  This paper proposes an Information Bottleneck theory based filter pruning
method that uses a statistical measure called Mutual Information (MI). The MI
between filters and class labels, also called \textit{Relevance}, is computed
using the filter's activation maps and the annotations. The filters having High
Relevance (HRel) are considered to be more important. Consequently, the least
important filters, which have lower Mutual Information with the class labels,
are pruned. Unlike the existing MI based pruning methods, the proposed method
determines the significance of the filters purely based on their corresponding
activation map's relationship with the class labels. Architectures such as
LeNet-5, VGG-16, ResNet-56\textcolor{myblue}{, ResNet-110 and ResNet-50 are
utilized to demonstrate the efficacy of the proposed pruning method over MNIST,
CIFAR-10 and ImageNet datasets. The proposed method shows the state-of-the-art
pruning results for LeNet-5, VGG-16, ResNet-56, ResNet-110 and ResNet-50
architectures. In the experiments, we prune 97.98 \%, 84.85 \%, 76.89\%,
76.95\%, and 63.99\% of Floating Point Operation (FLOP)s from LeNet-5, VGG-16,
ResNet-56, ResNet-110, and ResNet-50 respectively.} The proposed HRel pruning
method outperforms recent state-of-the-art filter pruning methods. Even after
pruning the filters from convolutional layers of LeNet-5 drastically (i.e. from
20, 50 to 2, 3, respectively), only a small accuracy drop of 0.52\% is
observed. Notably, for VGG-16, 94.98\% parameters are reduced, only with a drop
of 0.36\% in top-1 accuracy. \textcolor{myblue}{ResNet-50 has shown a 1.17\%
drop in the top-5 accuracy after pruning 66.42\% of the FLOPs.} In addition to
pruning, the Information Plane dynamics of Information Bottleneck theory is
analyzed for various Convolutional Neural Network architectures with the effect
of pruning.


title: Quantum Differential Privacy: An Information Theory Perspective

abstract:
  Differential privacy has been an exceptionally successful concept when it
comes to providing provable security guarantees for classical computations.
More recently, the concept was generalized to quantum computations. While
classical computations are essentially noiseless and differential privacy is
often achieved by artificially adding noise, near-term quantum computers are
inherently noisy and it was observed that this leads to natural differential
privacy as a feature. In this work we discuss quantum differential privacy in
an information theoretic framework by casting it as a quantum divergence. A
main advantage of this approach is that differential privacy becomes a property
solely based on the output states of the computation, without the need to check
it for every measurement. This leads to simpler proofs and generalized
statements of its properties as well as several new bounds for both, general
and specific, noise models. In particular, these include common representations
of quantum circuits and quantum machine learning concepts. Here, we focus on
the difference in the amount of noise required to achieve certain levels of
differential privacy versus the amount that would make any computation useless.
Finally, we also generalize the classical concepts of local differential
privacy, R\'enyi differential privacy and the hypothesis testing interpretation
to the quantum setting, providing several new properties and insights.


title:
Extensions of solvable Lie algebras with naturally graded filiform
  nilradical

abstract:
  In this work we consider extensions of solvable Lie algebras with naturally
graded filiform nilradicals. Note that there exist two naturally graded
filiform Lie algebras $n_{n, 1}$ and $Q_{2n}.$ We find all one-dimensional
central extensions of the algebra $n_{n, 1}$ and show that any extension of
$Q_{2n}$ is split. After that we find one-dimensional extensions of solvable
Lie algebras with nilradical $n_{n, 1}$. We prove that there exists a unique
non-split central extension of solvable Lie algebras with nilradical $n_{n, 1}$
of maximal codimension. Moreover, all one-dimensional extensions of solvable
Lie algebras with nilradical $n_{n, 1}$ whose codimension is equal to one are
found and compared these solvable algebras with the solvable algebras with
nilradicals are one-dimensional central extension of algebra $n_{n, 1}$.


title:
Orbital properties and implications for the initiation of plate
  tectonics and planetary habitability

abstract:
  The existence of plate tectonics on the Earth is directly dependent on the
internal viscosity contrast, mass of the planet, availability of liquid water
and an internal heat source. However, the initial conditions of rotational
velocity and revolutionary periodicity of the Earth around the Sun too must
have been significant for the inception of plate tectonics. The initial orbital
conditions of the Earth were significantly influenced by the diametrical
processes of core segregation and Moon formation and that had probably led to
the eventuality of initiation and persistence of plate tectonics. The change in
the orbital conditions could have rendered the Earth to evolve in a near-linear
trend so that the rotational periodicity of the planet (TP) could approach the
time taken for the planet to travel one degree in its orbit around the Sun
(T1degree), that is TP ~ T1degree. Such an optimal condition for the rotational
and revolutionary periodicities could be essential for the development of plate
tectonics on the Earth. This hypothesis has direct implications on the
possibility of plate tectonics and life in extrasolar planets and potentially
habitable solar planetary bodies such as Europa and Mars.


title: EIGNN: Efficient Infinite-Depth Graph Neural Networks

abstract:
  Graph neural networks (GNNs) are widely used for modelling graph-structured
data in numerous applications. However, with their inherently finite
aggregation layers, existing GNN models may not be able to effectively capture
long-range dependencies in the underlying graphs. Motivated by this limitation,
we propose a GNN model with infinite depth, which we call Efficient
Infinite-Depth Graph Neural Networks (EIGNN), to efficiently capture very
long-range dependencies. We theoretically derive a closed-form solution of
EIGNN which makes training an infinite-depth GNN model tractable. We then
further show that we can achieve more efficient computation for training EIGNN
by using eigendecomposition. The empirical results of comprehensive experiments
on synthetic and real-world datasets show that EIGNN has a better ability to
capture long-range dependencies than recent baselines, and consistently
achieves state-of-the-art performance. Furthermore, we show that our model is
also more robust against both noise and adversarial perturbations on node
features.


title:
Risk Parity Portfolios with Skewness Risk: An Application to Factor
  Investing and Alternative Risk Premia

abstract:
  This article develops a model that takes into account skewness risk in risk
parity portfolios. In this framework, asset returns are viewed as stochastic
processes with jumps or random variables generated by a Gaussian mixture
distribution. This dual representation allows us to show that skewness and jump
risks are equivalent. As the mixture representation is simple, we obtain
analytical formulas for computing asset risk contributions of a given
portfolio. Therefore, we define risk budgeting portfolios and derive existence
and uniqueness conditions. We then apply our model to the
equity/bond/volatility asset mix policy. When assets exhibit jump risks like
the short volatility strategy, we show that skewness-based risk parity
portfolios produce better allocation than volatility-based risk parity
portfolios. Finally, we illustrate how this model is suitable to manage the
skewness risk of long-only equity factor portfolios and to allocate between
alternative risk premia.


title: Analysis on superradiant instability of charged dilaton Black Hole

abstract:
  In this post, a new variable y($\mu=y\omega$) is added to extend the results
of the above post. We derive a very compact resonance formulation. When y is a
certain limit, the effective potential of the equation exists at the pole, and
the potential well appears outside the event horizon, when ${e}^{-4\alpha \Phi
/(n-1)}\sqrt{\alpha} Q \ll r_{+}\ll 1 / (\frac{4}{n-1}y) $, so charged dilator
black hole is superradiatively unstable at this time.


title:
Sobolev Transport: A Scalable Metric for Probability Measures with Graph
  Metrics

abstract:
  Optimal transport (OT) is a popular measure to compare probability
distributions. However, OT suffers a few drawbacks such as (i) a high
complexity for computation, (ii) indefiniteness which limits its applicability
to kernel machines. In this work, we consider probability measures supported on
a graph metric space and propose a novel Sobolev transport metric. We show that
the Sobolev transport metric yields a closed-form formula for fast computation
and it is negative definite. We show that the space of probability measures
endowed with this transport distance is isometric to a bounded convex set in a
Euclidean space with a weighted $\ell_p$ distance. We further exploit the
negative definiteness of the Sobolev transport to design positive-definite
kernels, and evaluate their performances against other baselines in document
classification with word embeddings and in topological data analysis.


title: Feature reconstruction from incomplete tomographic data without detour

abstract:
  In this paper, we consider the problem of feature reconstruction from
incomplete x-ray CT data. Such problems occurs, e.g., as a result of dose
reduction in the context medical imaging. Since image reconstruction from
incomplete data is a severely ill-posed problem, the reconstructed images may
suffer from characteristic artefacts or missing features, and significantly
complicate subsequent image processing tasks (e.g., edge detection or
segmentation). In this paper, we introduce a novel framework for the robust
reconstruction of convolutional image features directly from CT data, without
the need of computing a reconstruction firs. Within our framework we use
non-linear (variational) regularization methods that can be adapted to a
variety of feature reconstruction tasks and to several limited data situations
. In our numerical experiments, we consider several instances of edge
reconstructions from angularly undersampled data and show that our approach is
able to reliably reconstruct feature maps in this case.


title: Multi-Source Unsupervised Domain Adaptation via Pseudo Target Domain

abstract:
  Multi-source domain adaptation (MDA) aims to transfer knowledge from multiple
source domains to an unlabeled target domain. MDA is a challenging task due to
the severe domain shift, which not only exists between target and source but
also exists among diverse sources. Prior studies on MDA either estimate a mixed
distribution of source domains or combine multiple single-source models, but
few of them delve into the relevant information among diverse source domains.
For this reason, we propose a novel MDA approach, termed Pseudo Target for MDA
(PTMDA). Specifically, PTMDA maps each group of source and target domains into
a group-specific subspace using adversarial learning with a metric constraint,
and constructs a series of pseudo target domains correspondingly. Then we align
the remainder source domains with the pseudo target domain in the subspace
efficiently, which allows to exploit additional structured source information
through the training on pseudo target domain and improves the performance on
the real target domain. Besides, to improve the transferability of deep neural
networks (DNNs), we replace the traditional batch normalization layer with an
effective matching normalization layer, which enforces alignments in latent
layers of DNNs and thus gains further promotion. We give theoretical analysis
showing that PTMDA as a whole can reduce the target error bound and leads to a
better approximation of the target risk in MDA settings. Extensive experiments
demonstrate PTMDA's effectiveness on MDA tasks, as it outperforms
state-of-the-art methods in most experimental settings.


title: The duo Fenchel-Young divergence

abstract:
  By calculating the Kullback-Leibler divergence between two probability
measures belonging to different exponential families, we end up with a formula
that generalizes the ordinary Fenchel-Young divergence. Inspired by this
formula, we define the duo Fenchel-Young divergence and reports a majorization
condition on its pair of generators which guarantees that this divergence is
always non-negative. The duo Fenchel-Young divergence is also equivalent to a
duo Bregman divergence. We show the use of these duo divergences by calculating
the Kullback-Leibler divergence between densities of nested exponential
families, and report a formula for the Kullback-Leibler divergence between
truncated normal distributions. Finally, we prove that the skewed Bhattacharyya
distance between nested exponential families amounts to an equivalent skewed
duo Jensen divergence.


title: Amplification of quantum signals by the non-Hermitian skin effect

abstract:
  The non-Hermitian skin effect (NHSE) is a phenomenon whereby certain
non-Hermitian lattice Hamiltonians, particularly those with nonreciprocal
couplings, can host an extensive number of eigenmodes condensed to the
boundary, called skin modes. Although the NHSE has mostly been studied in the
classical regime, we show that it can also manifest in quantum systems
containing boson number nonconserving processes arising from uniform parametric
driving. We study lattices of coupled nonlinear resonators that can function as
reciprocal quantum amplifiers. A one-dimensional chain exhibiting NHSE can
perform strong photon amplification, aided by the skin modes, that scales
exponentially with the chain length and outperforms alternative lattice
configurations that lack the NHSE. We show also that two-dimensional nonlinear
lattices can perform directional photon amplification between different lattice
corners, due to the two-dimensional NHSE.


title: Distilled Neural Networks for Efficient Learning to Rank

abstract:
  Recent studies in Learning to Rank have shown the possibility to effectively
distill a neural network from an ensemble of regression trees. This result
leads neural networks to become a natural competitor of tree-based ensembles on
the ranking task. Nevertheless, ensembles of regression trees outperform neural
models both in terms of efficiency and effectiveness, particularly when scoring
on CPU. In this paper, we propose an approach for speeding up neural scoring
time by applying a combination of Distillation, Pruning and Fast Matrix
multiplication. We employ knowledge distillation to learn shallow neural
networks from an ensemble of regression trees. Then, we exploit an
efficiency-oriented pruning technique that performs a sparsification of the
most computationally-intensive layers of the neural network that is then scored
with optimized sparse matrix multiplication. Moreover, by studying both dense
and sparse high performance matrix multiplication, we develop a scoring time
prediction model which helps in devising neural network architectures that
match the desired efficiency requirements. Comprehensive experiments on two
public learning-to-rank datasets show that neural networks produced with our
novel approach are competitive at any point of the effectiveness-efficiency
trade-off when compared with tree-based ensembles, providing up to 4x scoring
time speed-up without affecting the ranking quality.


title: Improving Cross-lingual Speech Synthesis with Triplet Training Scheme

abstract:
  Recent advances in cross-lingual text-to-speech (TTS) made it possible to
synthesize speech in a language foreign to a monolingual speaker. However,
there is still a large gap between the pronunciation of generated cross-lingual
speech and that of native speakers in terms of naturalness and intelligibility.
In this paper, a triplet training scheme is proposed to enhance the
cross-lingual pronunciation by allowing previously unseen content and speaker
combinations to be seen during training. Proposed method introduces an extra
fine-tune stage with triplet loss during training, which efficiently draws the
pronunciation of the synthesized foreign speech closer to those from the native
anchor speaker, while preserving the non-native speaker's timbre. Experiments
are conducted based on a state-of-the-art baseline cross-lingual TTS system and
its enhanced variants. All the objective and subjective evaluations show the
proposed method brings significant improvement in both intelligibility and
naturalness of the synthesized cross-lingual speech.


title: A Lumer-Phillips type generation theorem for bi-continuous semigroups

abstract:
  The famous 1960s Lumer-Phillips Theorem states that a closed and densely
defined operator $A\colon D(A)\subseteq X\rightarrow X$ on a Banach space $X$
generates a strongly continuous contraction semigroup if and only if $(A,D(A))$
is dissipative and the range of $\lambda-A$ is surjective in $X$ for some
$\lambda>0$. In this paper, we establish a version of this result for
bi-continuous semigroups and apply the latter amongst other examples to the
transport equation as well as to flows on infinite networks.


title: Entanglement entropy of XX spin $1/2$ chain with random partitioning

abstract:
  Entanglement properties of random XX spin $1/2$ chain are studied using the
random partitioning, in which, sites of the subsystem are chosen randomly with
a probability, also the subsystem size varies, and we properly take average
over all these alternatives. We prove both analytically and numerically that
entanglement entropy for the XX spin chain with size $L$ with random
partitioning behaves like EE$(T,p) = a(T,p) L$ at an arbitrary temperature $T$
with a uniform probability $p$, i.e. it obeys volume law. We prove that $a(T,p)
= \ln(2) \langle P_s + P_{t_{\uparrow\downarrow}} \rangle p(1-p)$, where $P_s$
and $P_{t_{\uparrow\downarrow}}$ are the average probabilities of having
singlet and triplet$_{\uparrow\downarrow}$ in the entire system respectively.
We study also the temperature dependence of $a$. We show that EE with random
partitioning reveals both short and long-range correlations in the entire
system.


title: Wide-angle giant photonic spin Hall effect

abstract:
  Photonic spin Hall effect is a manifestation of spin-orbit interaction of
light and can be measured by a transverse shift \lambda of photons with
opposite spins. The precise measurement of transverse shifts can enable many
spin-related applications, such as precise metrology and optical sensing.
However, this transverse shift is generally small (i.e. \delta /\lambda
<{10}^{-1}, \lambda is the wavelength), which impedes its precise measurement.
To-date proposals to generate giant spin Hall effect (namely with \delta
/\lambda >{10}^{2}) have severe limitations, particularly its occurrence only
over a narrow angular cone (with a width of \Delta \theta <{1}^{\circ}). Here
we propose a universal scheme to realize the wide-angle giant photonic spin
Hall effect with \Delta \theta >{70}^{\circ} by exploiting the interface
between free space and uniaxial epsilon-near-zero media. The underlying
mechanism is ascribed to the almost-perfect polarization splitting between s
and p polarized waves at the designed interface. Remarkably, this
almost-perfect polarization splitting does not resort to the interference
effect and is insensitive to the incident angle, which then gives rise to the
wide-angle giant photonic spin Hall effect.


title:
Scalar quadratic maximum likelihood estimators for the CMB cross power
  spectrum

abstract:
  Estimating the cross-correlation power spectra of cosmic microwave background
(CMB), in particular, the T B and EB spectra, is important for testing parity
symmetry in cosmology and diagnosing insidious instruments systematics. The
Quadratic Maximum Likelihood (QML) estimator provides the optimal estimates of
power spectra, but it is computationally very expensive. The hybrid pseudo-Cl
estimator is computationally fast but performs poorly on large scales. As a
natural extension of previous work (Chen et al. 2021), in this article, we
present a new unbiased estimator based on the Smith-Zaldarriaga (SZ) approach
of E-B separation and scalar QML approach to reconstruct the cross-correlation
power spectrum, called QML-SZ estimator. Our new estimator relies on the
ability to construct scalar maps, which allows us to use a scalar QML estimator
to obtain the cross-correlation power spectrum. By reducing the pixel number
and algorithm complexity, the computational cost is nearly one order of
magnitude smaller and the running time is nearly two orders of magnitude faster
in the test situations.


title: Toric Foliated Minimal Model Program

abstract:
  Using the theory of Klyachko filtrations for reflexive sheaves on toric
varieties, we give a description of toric foliations and their singularities in
terms of combinatorial data. We extend Spicer's results about co-rank one toric
foliations to those of any ranks on $\mathbb{Q}$-factorial toric varieties and
prove that the toric foliated Minimal Model Program exists.


title: Generalized Koszul Algebra and Koszul Duality

abstract:
  We define generalized Koszul modules and rings and develop a generalized
Koszul theory for $\mathbb{N}$-graded rings with the degree zero part
noetherian semiperfect. This theory specializes to the classical Koszul theory
for graded rings with degree zero part artinian semisimple developed by
Beilinson-Ginzburg-Soergel and the ungraded Koszul theory for noetherian
semiperfect rings developed by Green and Martin{\'e}z-Villa. Let $A$ be a left
finite $\mathbb{N}$-graded ring generated in degree $1$ with $A_0$ noetherian
semiperfect, $J$ be its graded Jacobson radical and $S=A/J$. By the Koszul dual
of $A$ we mean the Yoneda Ext ring $\underline{\text{Ext}}_A^\bullet(S,S)$. If
$A$ is a generalized Koszul ring and $M$ is a generalized Koszul module, then
it is proved that the Koszul dual of the Koszul dual of $A$ is $\text{Gr}_J A$
and the Koszul dual of the Koszul dual of $M$ is $\text{Gr}_J M$. If $A$ is a
locally finite algebra, then the following statements are proved to be
equivalent: $A$ is generalized Koszul; the Koszul dual
$\underline{\text{Ext}}_A^\bullet(S,S)$ of $A$ is (classically) Koszul;
$\text{Gr}_J A$ is (classically) Koszul; the opposite ring $A^{op}$ of $A$ is
generalized Koszul. It is also proved that if $A$ is generalized Koszul with
finite global dimension then $A$ is generalized AS regular if and only if the
Koszul dual of $A$ is self-injective.


title: The pole counting rule and XYZ states

abstract:
  The pole counting rule is a powerful and model-independent method to
distinguish a confining state from a hadronic molecule. It has been applied to
the explorations of $X(6900)$, $X_1(2900)$ as well as $Z_c(3900)$, $X(3872)$,
$X(4660)$, etc.. For $X(6900)$, both a confining state and a molecular state
are not excluded, because lacking of enough data. For $X_1(2900)$, the analysis
shows that it should be a $\bar{D}_1 K$ molecule, with $J^P=1^-$ and an
iso-singlet interpretation is much more favorable. Finally, it is noted that
almost all XYZ particles with exotic quantum numbers can be interpreted as
hadronic molecules. The $X(3872)$ is, however, more like a charmonium.


title:
Characterizing Anderson delocalized-localized phase transition based on
  the response of the system to the boundary conditions

abstract:
  A characterization of the Anderson phase transition has been introduced,
based on the response of the system to the boundary conditions. We change the
boundary conditions from periodic to anti-periodic and look for its effect on
the eigenstate of the system. To characterize the effect we use overlap of the
states. In particular, we numerically calculate the overlap between
ground-state of the system with periodic and that with anti-periodic boundary
conditions in one-dimensional models with delocalized-localized phase
transitions. We observe that the overlap is close to $1$ in the localized phase
and it gets tangibly smaller in the delocalized phase. In addition, to
characterize the entire spectrum of the models with mobility edges, we
calculate also the overlaps between single-particle eigenstate with periodic
and anti-periodic boundary conditions, by which we are able to locate the
mobility edges.


title: Irrationality exponents of semi-regular continued fractions

abstract:
  We prove that the formula giving the exact value of the irrationality
exponent of regular continued fractions remains valid for semi-regular
continued fractions satisfyiong certain conditions.


title: JAMES: Job Title Mapping with Multi-Aspect Embeddings and Reasoning

abstract:
  One of the most essential tasks needed for various downstream tasks in career
analytics (e.g., career trajectory analysis, job mobility prediction, and job
recommendation) is Job Title Mapping (JTM), where the goal is to map
user-created (noisy and non-standard) job titles to predefined and standard job
titles. However, solving JTM is domain-specific and non-trivial due to its
inherent challenges: (1) user-created job titles are messy, (2) different job
titles often overlap their job requirements, (3) job transition trajectories
are inconsistent, and (4) the number of job titles in real world applications
is large-scale. Toward this JTM problem, in this work, we propose a novel
solution, named as JAMES, that constructs three unique embeddings of a target
job title: topological, semantic, and syntactic embeddings, together with
multi-aspect co-attention. In addition, we employ logical reasoning
representations to collaboratively estimate similarities between messy job
titles and standard job titles in the reasoning space. We conduct comprehensive
experiments against ten competing models on the large-scale real-world dataset
with more than 350,000 job titles. Our results show that JAMES significantly
outperforms the best baseline by 10.06% in Precision@10 and by 17.52% in
NDCG@10, respectively.


title:
Covariant BRST Quantization of Unimodular Gravity II -- Formulation with
  a vector antighost --

abstract:
  In our previous paper, we have presented a covariant BRST quantization of
unimodular gravity which may account for the smallness of the cosmological
constant, and have shown that the physical degrees of freedom in the theory are
the same as general relativity. The formulation has been given by using rank-2
antisymmetric tensor fields for both ghosts and antighosts. Here we give an
alternative formulation using a vector field for the antighost but keeping the
same structure for the ghosts. This gives a significantly simpler covariant
quantization with less ghosts and no tripole modes in the ghost sector. We show
that this also gives only two physical transverse modes as in general
relativity.


title:
Fluctuations and non-Gaussianity in 3D+1 quantum nonlocal nonlinear
  waves: towards quantum gravity and quantum fluid technologies

abstract:
  Nonlocal quantum fluids emerge as dark-matter models and tools for quantum
simulations and technologies. However, strongly nonlinear regimes, like those
involving multi-dimensional self-localized solitary waves (nonlocal solitons),
are marginally explored for what concerns quantum features. We study the
dynamics of 3D+1 solitons in the second-quantized nonlocal nonlinear
Schroedinger equation. We theoretically investigate the quantum diffusion of
the soliton center of mass and other parameters, varying the interaction
length. 3D+1 simulations of the Ito partial differential equations arising from
the positive P-representation of the density matrix validate the theoretical
analysis. The numerical results unveil the onset of non-Gaussian statistics of
the soliton, which may signal quantum-gravitational effects and be a resource
for quantum computing. The non-Gaussianity arises from the interplay of the
quantum diffusion of the soliton parameters and the stable invariant
propagation. The fluctuations and the non-Gaussianity are universal effects
expected for any nonlocality and dimensionality.


title:
Acceleration of Gossip Algorithms through the Euler-Poisson-Darboux
  Equation

abstract:
  Gossip algorithms and their accelerated versions have been studied
exclusively in discrete time on graphs. In this work, we take a different
approach, and consider the scaling limit of gossip algorithms in both large
graphs and large number of iterations. These limits lead to well-known partial
differential equations (PDEs) with insightful properties. On lattices, we prove
that the non-accelerated gossip algorithm of Boyd et al. [2006] converges to
the heat equation, and the accelerated Jacobi polynomial iteration of Berthier
et al. [2020] converges to the Euler-Poisson-Darboux (EPD) equation - a damped
wave equation. Remarkably, with appropriate parameters, the fundamental
solution of the EPD equation has the ideal gossip behaviour: a uniform density
over an ellipsoid, whose radius increases at a rate proportional to t - the
fastest possible rate for locally communicating gossip algorithms. This is in
contrast with the heat equation where the density spreads on a typical scale of
$\sqrt{t}$. Additionally, we provide simulations demonstrating that the gossip
algorithms are accurately approximated by their limiting PDEs.


title:
Thermal control of nucleation and propagation transition stresses in
  discrete lattices with non-local interactions and non-convex energy

abstract:
  Non-local and non-convex energies represent fundamental interacting effects
regulating the complex behavior of many systems in biophysics and materials
science. We study one dimensional, prototypical schemes able to represent the
behavior of several biomacromolecules and the phase transformation phenomena in
solid mechanics. To elucidate the effects of thermal fluctuations on the
non-convex non-local behavior of such systems, we consider three models of
different complexity relying on thermodynamics and statistical mechanics: (i)
an Ising-type scheme with an arbitrary temperature dependent number of
interfaces between different domains, (ii) a zipper model with a single
interface between two evolving domains, and (iii) an approximation based on the
stationary phase method. In all three cases, we study the system under both
isometric condition (prescribed extension, matching with the Helmholtz ensemble
of the statistical mechanics) and isotensional condition (applied force,
matching with the Gibbs ensemble). Interestingly, in the Helmholtz ensemble the
analysis shows the possibility of interpreting the experimentally observed
thermal effects with the theoretical force-extension relation characterized by
a temperature dependent force plateau (Maxwell stress) and a force peak
(nucleation stress). We obtain explicit relations for the configurational
properties of the system as well (expected values of the phase fractions and
number of interfaces). Moreover, we are able to prove the equivalence of the
two thermodynamic ensembles in the thermodynamic limit. We finally discuss the
comparison with data from the literature showing the efficiency of the proposed
model in describing known experimental effects.


title: CorefDRE: Document-level Relation Extraction with coreference resolution

abstract:
  Document-level relation extraction is to extract relation facts from a
document consisting of multiple sentences, in which pronoun crossed sentences
are a ubiquitous phenomenon against a single sentence. However, most of the
previous works focus more on mentions coreference resolution except for
pronouns, and rarely pay attention to mention-pronoun coreference and capturing
the relations. To represent multi-sentence features by pronouns, we imitate the
reading process of humans by leveraging coreference information when
dynamically constructing a heterogeneous graph to enhance semantic information.
Since the pronoun is notoriously ambiguous in the graph, a mention-pronoun
coreference resolution is introduced to calculate the affinity between pronouns
and corresponding mentions, and the noise suppression mechanism is proposed to
reduce the noise caused by pronouns. Experiments on the public dataset, DocRED,
DialogRE and MPDD, show that Coref-aware Doc-level Relation Extraction based on
Graph Inference Network outperforms the state-of-the-art.


title: Improving Systematic Generalization Through Modularity and Augmentation

abstract:
  Systematic generalization is the ability to combine known parts into novel
meaning; an important aspect of efficient human learning, but a weakness of
neural network learning. In this work, we investigate how two well-known
modeling principles -- modularity and data augmentation -- affect systematic
generalization of neural networks in grounded language learning. We analyze how
large the vocabulary needs to be to achieve systematic generalization and how
similar the augmented data needs to be to the problem at hand. Our findings
show that even in the controlled setting of a synthetic benchmark, achieving
systematic generalization remains very difficult. After training on an
augmented dataset with almost forty times more adverbs than the original
problem, a non-modular baseline is not able to systematically generalize to a
novel combination of a known verb and adverb. When separating the task into
cognitive processes like perception and navigation, a modular neural network is
able to utilize the augmented data and generalize more systematically,
achieving 70% and 40% exact match increase over state-of-the-art on two gSCAN
tests that have not previously been improved. We hope that this work gives
insight into the drivers of systematic generalization, and what we still need
to improve for neural networks to learn more like humans do.


title: CD-ROM: Complementary Deep-Reduced Order Model

abstract:
  Model order reduction through the POD-Galerkin method can lead to dramatic
gains in terms of computational efficiency in solving physical problems.
However, the applicability of the method to non linear high-dimensional
dynamical systems such as the Navier-Stokes equations has been shown to be
limited, producing inaccurate and sometimes unstable models. This paper
proposes a closure modeling approach for classical POD-Galerkin reduced order
models (ROM). We use multi layer perceptrons (MLP) to learn a continuous in
time closure model through the recently proposed Neural ODE method. Inspired by
Taken's theorem as well as the Mori-Zwanzig formalism, we augment ROMs with a
delay differential equation architecture to model non-Markovian effects in
reduced models. The proposed model, called CD-ROM (Complementary Deep-Reduced
Order Model) is able to retain information from past states of the system and
use it to correct the imperfect reduced dynamics. The model can be integrated
in time as a system of ordinary differential equations using any classical time
marching scheme. We demonstrate the ability of our CD-ROM approach to improve
the accuracy of POD-Galerkin models on two CFD examples, even in configurations
unseen during training.


title: Better Private Algorithms for Correlation Clustering

abstract:
  In machine learning, correlation clustering is an important problem whose
goal is to partition the individuals into groups that correlate with their
pairwise similarities as much as possible. In this work, we revisit the
correlation clustering under the differential privacy constraints.
Particularly, we improve previous results and achieve an $\Tilde{O}(n^{1.5})$
additive error compared to the optimal cost in expectation on general graphs.
As for unweighted complete graphs, we improve the results further and propose a
more involved algorithm which achieves $\Tilde{O}(n \sqrt{\Delta^*})$ additive
error, where $\Delta^*$ is the maximum degrees of positive edges among all
nodes.


title:
All field emission models are wrong, $\ldots$ but are any of them
  useful?

abstract:
  Field emission data are often represented on a Fowler--Nordheim plot but a
new empirical equation has been recently proposed to better analyze
experiments. Such an equation is based on approximations of the Murphy and Good
model and predicts that a constant parameter $\kappa$, depending only on the
work function of the emitter, can be extracted from the data. We compared this
empirical equation with simulations of the Murphy and Good model in order to
determine the range of validity of the approximations and the robustness of the
relationship between $\kappa$ and the work function. We found that $\kappa$ is
constant only over a limited range of electric fields and so depends
significantly on the field enhancement factor. This result calls into question
the usefulness of the new empirical equation.


title:
Location-based Initial Access for Wireless Power Transfer with
  Physically Large Arrays

abstract:
  Radio frequency (RF) wireless power transfer (WPT) is a promising technology
for 6G use cases. It enables a massive, yet sustainable deployment of
batteryless energy neutral (EN) devices at unprecedented scale. Recent research
on 6G is exploring high operating frequencies up to the THz spectrum, where
antenna arrays with large apertures are capable of forming narrow, "laser-like"
beams. At sub-10 GHz frequencies, physically large antenna arrays are
considered that are operating in the array near field. Transmitting spherical
wavefronts, power can be focused in a focal point rather than a beam, which
allows for efficient and radiation-safe WPT. We formulate a multipath channel
model comprising specular components and diffuse scattering to find the WPT
power budget in a realistic indoor scenario. Specular components can be
predicted by means of a geometric model. This is used to transmit power via
multiple beams simultaneously, increasing the available power budget and
expanding the initial access distance. We show that exploiting this "beam
diversity" reduces the required fading margin for the initial access to EN
devices.


title: Queue input estimation from discrete workload observations

abstract:
  This note considers the problem of statistical inference of the parameters of
the input process to a queue from periodic workload observations. The main
focus is the open problem of constructing statistically efficient estimators
for a given observation scheme, in the sense of minimizing the asymptotic
variance of the estimation error.


title:
Extremes for stationary regularly varying random fields over arbitrary
  index sets

abstract:
  We consider the clustering of extremes for stationary regularly varying
random fields over arbitrary growing index sets. We study sufficient
assumptions on the index set such that the limit of the point random fields of
the exceedances above a high threshold exists. Under the so-called
anti-clustering condition, the extremal dependence is only local. Thus the
index set can have a general form compared to previous literature [3, 21].
However, we cannot describe the clustering of extreme values in terms of the
usual spectral tail measure [23] except for hyperrectangles or index sets in
the lattice case. Using the recent extension of the spectral measure for
star-shaped equipped space [18], the $\upsilon$-spectral tail measure provides
a natural extension that describes the clustering effect in full generality.


title: Event-Triggered Tracking Control of Networked Multi-Agent Systems

abstract:
  This paper studies the tracking control problem of networked multi-agent
systems under both multiple networks and event-triggered mechanisms. Multiple
networks are to connect multiple agents and reference systems with
decentralized controllers to guarantee their information transmission, whereas
the event-triggered mechanisms are to reduce the information transmission via
the networks. In this paper, each agent has a network to communicate with its
controller and reference system, and all networks are independent and
asynchronous and have local event-triggered mechanisms, which are based on
local measurements and determine whether the local measurements need to be
transmitted via the corresponding network. To address this scenario, we first
implement the emulation-based approach to develop a novel hybrid model for the
tracking control of networked multi-agent systems. Next, sufficient conditions
are derived and decentralized event-triggered mechanisms are designed to
guarantee the desired tracking performance. Furthermore, the proposed approach
is applied to derive novel results for the event-triggered observer design
problem of networked multi-agent systems. Finally, two numerical examples are
presented to illustrate the validity of the developed results.


title:
Convolutional Neural Network Modelling for MODIS Land Surface
  Temperature Super-Resolution

abstract:
  Nowadays, thermal infrared satellite remote sensors enable to extract very
interesting information at large scale, in particular Land Surface Temperature
(LST). However such data are limited in spatial and/or temporal resolutions
which prevents from an analysis at fine scales. For example, MODIS satellite
provides daily acquisitions with 1Km spatial resolutions which is not
sufficient to deal with highly heterogeneous environments as agricultural
parcels. Therefore, image super-resolution is a crucial task to better exploit
MODIS LSTs. This issue is tackled in this paper. We introduce a deep
learning-based algorithm, named Multi-residual U-Net, for super-resolution of
MODIS LST single-images. Our proposed network is a modified version of U-Net
architecture, which aims at super-resolving the input LST image from 1Km to
250m per pixel. The results show that our Multi-residual U-Net outperforms
other state-of-the-art methods.

categories: cs.CV cs.AI cs.LG eess.IV physics.data-an


title: A homogeneous presentation of symmetric quandles

abstract:
  A quandle is an algebraic structure whose axioms correspond to the
Reidemeister moves of knot theory. S. Kamada introduced the notion of a quandle
with a good involution, which is later called a symmetric quandle. We are
interested in the algebraic structure of symmetric quandles. Given a group $G$,
an element $z$ and a certain subgroup $H$, one can obtain the quandle. D. Joyce
showed that every quandle is isomorphic to the disjoint union of such quandles.
In this paper, given a group $G$, elements $z,r$ in $G$ and a certain subgroup
$H$, we construct a symmetric quandle. Futhermore, we show that every symmetric
quandle is isomorphic to the disjoint union of such quandles.


title:
Station-keeping of $L_2$ halo orbits under sampled-data model predictive
  control

abstract:
  The paper deals with the design of an improved model predictive control
scheme for achieving station-keeping in a quasi Halo orbit around the $L_2$
point in the Earth-Moon system. The improvement is obtained thanks to a
multi-rate sampled-data trajectory planner that allows for simplifying the
optimization problem of the model predictive controller while guaranteeing
feasibility and convergence to the desired orbit. The multi-rate planner is
designed based on a simplified model of the dynamics under a preliminary
nonlinear regulation feedback. The proposed control scheme is shown to
outperform recent station-keeping nonlinear model predictive control designs
both in terms of tracking error and energy expenditures in different
situations. Finally, a brief study of aspects pertaining to computational time
are carried out so highlighting the possibility for real time implementation on
modern hardware.


title:
Coarse-grained Mori-Zwanzig dynamics in a time-non-local
  stationary-action framework

abstract:
  Coarse-grained (CG) models are simplified representations of soft matter
systems that are commonly employed to overcome size and time limitations in
computational studies. Many approaches have been developed to construct and
parametrise such effective models for a variety of systems of natural as well
as artificial origin. However, while extremely accurate in reproducing the
stationary and equilibrium observables obtained with more detailed
representations, CG models generally fail to preserve the original time scales
of the reference system, and hence its dynamical properties. To bridge this
gap, we here formulate the Mori-Zwanzig generalised Langevin equations (GLEs)
of motion of a CG model in terms of a time non-local stationary-action
principle. The latter is employed in combination with a data-driven
optimisation strategy to determine the parameters of the GLE. We apply this
approach to a system of water molecules in standard thermodynamical conditions,
showing that it can substantially improve the dynamical features of the
corresponding CG model.


title:
Global well-posedness and scattering of the two dimensional cubic
  focusing nonlinear Schr\"odinger system

abstract:
  In this article, we prove the global well-posedness and scattering of the
cubic focusing infinite coupled nonlinear Schr\"odinger system on
$\mathbb{R}^2$ below the threshold in $L_x^2h^1(\mathbb{R}^2\times
\mathbb{Z})$. We first establish the variational characterization of the ground
state, and derive the threshold of the global well-posedness and scattering.
Then we show the global well-posedness and scattering below the threshold by
the concentration-compactness/rigidity method, where the almost periodic
solution is excluded by adapting the argument in the proof of the mass-critical
nonlinear Schr\"odinger equations by B. Dodson. As a byproduct of the
scattering of the cubic focusing infinite coupled nonlinear Sch\"odinger
system, we obtain the scattering of the cubic focusing nonlinear Schr\"odinger
equation on the small cylinder, this is the first large data scattering result
of the focusing nonlinear Schr\"odinger equations on the cylinders. In the
article, we also show the global well-posedness and scattering of the two
dimensional $N-$coupled focusing cubic nonlinear Schr\"odinger system in
$\left(L^2(\mathbb{R}^2) \right)^N$.


title: Thinking the Fusion Strategy of Multi-reference Face Reenactment

abstract:
  In recent advances of deep generative models, face reenactment -manipulating
and controlling human face, including their head movement-has drawn much
attention for its wide range of applicability. Despite its strong
expressiveness, it is inevitable that the models fail to reconstruct or
accurately generate unseen side of the face of a given single reference image.
Most of existing methods alleviate this problem by learning appearances of
human faces from large amount of data and generate realistic texture at
inference time. Rather than completely relying on what generative models learn,
we show that simple extension by using multiple reference images significantly
improves generation quality. We show this by 1) conducting the reconstruction
task on publicly available dataset, 2) conducting facial motion transfer on our
original dataset which consists of multi-person's head movement video
sequences, and 3) using a newly proposed evaluation metric to validate that our
method achieves better quantitative results.


title: On some estimates involving Fourier coefficients of Maass cusp forms

abstract:
  Let $f$ be a Hecke-Maass cusp form for $\rm SL_2(\mathbb{Z})$ with Laplace
eigenvalue $\lambda_f(\Delta)=1/4+\mu^2$ and let $\lambda_f(n)$ be its $n$-th
normalized Fourier coefficient. It is proved that, uniformly in $\alpha, \beta
\in \mathbb{R}$, $$ \sum_{n \leq X}\lambda_f(n)e\left(\alpha n^2+\beta n\right)
\ll X^{7/8+\varepsilon}\lambda_f(\Delta)^{1/2+\varepsilon}, $$ where the
implied constant depends only on $\varepsilon$. We also consider the summation
function of $\lambda_f(n)$ and under the Ramanujan conjecture we are able to
prove $$ \sum_{n \leq X}\lambda_f(n)\ll
X^{1/3+\varepsilon}\lambda_f(\Delta)^{4/9+\varepsilon} $$ with the implied
constant depending only on $\varepsilon$.


title:
Crypto-assets better safe-havens than Gold during Covid-19: The case of
  European indices

abstract:
  As the first crisis faced by Crypto-assets, Covid-19 updated the debate about
their safehaven properties. Our paper tries to analyze the safe-haven
properties of Crypto-assets and Gold for European assets. We find that Gold has
not been more efficient than Cryptoassets (Tether, Cardano and Dogecoin) as
safe-haven during the market crash due to Covid-19 in March 2020. We also found
that during the study period Bitcoin, Ethereum, Litecoin and Ripple were just
diversifiers for the European indices. Finally, Tether, Cardano and Dogecoin
showed hedging properties like Gold before and after the market crash.


title: Characterization of affine $\mathbb{G}_m$-surfaces of hyperbolic type

abstract:
  In this note we prove that if $S$ is an affine non-toric
$\mathbb{G}_m$-surface of hyperbolic type that admits a $\mathbb{G}_a$-action
and $X$ is an affine irreducible variety such that $Aut(X)$ is isomorphic to
$Aut(S)$ as an abstract group, then $X$ is a $\mathbb{G}_m$-surface of
hyperbolic type. Further, we show that a smooth Danielewski surface $Dp = \{ xy
= p(z) \} \subset \mathbb{A}^3$, where $p$ has no multiple roots, is determined
by its automorphism group seen as an ind-group in the category of affine
irreducible varieties.


title:
Multivariate Gaussian Random Fields over Generalized Product Spaces
  involving the Hypertorus

abstract:
  The paper deals with multivariate Gaussian random fields defined over
generalized product spaces that involve the hypertorus. The assumption of
Gaussianity implies the finite dimensional distributions to be completely
specified by the covariance functions, being in this case matrix valued
mappings. We start by considering the spectral representations that in turn
allow for a characterization of such covariance functions. We then provide some
methods for the construction of these matrix valued mappings. Finally, we
consider strategies to evade radial symmetry (called isotropy in spatial
statistics) and provide representation theorems for such a more general case.


title: A Review of Affective Generation Models

abstract:
  Affective computing is an emerging interdisciplinary field where
computational systems are developed to analyze, recognize, and influence the
affective states of a human. It can generally be divided into two subproblems:
affective recognition and affective generation. Affective recognition has been
extensively reviewed multiple times in the past decade. Affective generation,
however, lacks a critical review. Therefore, we propose to provide a
comprehensive review of affective generation models, as models are most
commonly leveraged to affect others' emotional states. Affective computing has
gained momentum in various fields and applications, thanks to the leap of
machine learning, especially deep learning since 2015. With critical models
introduced, this work is believed to benefit future research on affective
generation. We conclude this work with a brief discussion on existing
challenges.


title: Scattering Theory with Unitary Twists

abstract:
  We study the spectral properties of the Laplace operator associated to a
hyperbolic surface in the presence of a unitary representation of the
fundamental group. Following the approach by Guillop\'e and Zworski, we
establish a factorization formula for the twisted scattering determinant and
describe the behavior of the scattering matrix in a neighborhood of $1/2$.


title:
Transporters with Visual Foresight for Solving Unseen Rearrangement
  Tasks

abstract:
  Rearrangement tasks have been identified as a crucial challenge for
intelligent robotic manipulation, but few methods allow for precise
construction of unseen structures. We propose a visual foresight model for
pick-and-place manipulation which is able to learn efficiently. In addition, we
develop a multi-modal action proposal module which builds on Goal-Conditioned
Transporter Networks, a state-of-the-art imitation learning method. Our method,
Transporters with Visual Foresight (TVF), enables task planning from image data
and is able to achieve multi-task learning and zero-shot generalization to
unseen tasks with only a handful of expert demonstrations. TVF is able to
improve the performance of a state-of-the-art imitation learning method on both
training and unseen tasks in simulation and real robot experiments. In
particular, the average success rate on unseen tasks improves from 55.0% to
77.9% in simulation experiments and from 30% to 63.3% in real robot experiments
when given only tens of expert demonstrations. More details can be found on our
project website: https://chirikjianlab.github.io/tvf/


title: Revisiting Semiring Provenance for Datalog

abstract:
  Data provenance consists in bookkeeping meta information during query
evaluation, in order to enrich query results with their trust level,
likelihood, evaluation cost, and more. The framework of semiring provenance
abstracts from the specific kind of meta information that annotates the data.
While the definition of semiring provenance is uncontroversial for unions of
conjunctive queries, the picture is less clear for Datalog. Indeed, the
original definition might include infinite computations, and is not consistent
with other proposals for Datalog semantics over annotated data. In this work,
we propose and investigate several provenance semantics, based on different
approaches for defining classical Datalog semantics. We study the relationship
between these semantics, and introduce properties that allow us to analyze and
compare them.


title:
Norm convergence for problems with perforation along a given manifold
  with nonlinear Robin condition on boundaries of cavities

abstract:
  In the work we consider a boundary value problem for a second order equation
with variable coefficients in a multi-dimensional domain perforated by small
cavities closely spaced along a given manifold. We assume that the linear sizes
of all cavities are of a same order of smallness, while their shapes and
distributions are arbitrary. The boundaries of the cavities are subject to a
nonlinear Robin condition. We prove that the solution of the perturbed problem
converges to that of the homogenized problem in norm $L_2$ and $W_2^1$
uniformly in $L_2$-norm of the right hand side in the equation. We also
establish the estimates for the convergence rates.


title:
Testing the Alfv\'en-wave model of the solar wind with interplanetary
  scintillation

abstract:
  Understanding the mechanism(s) of the solar wind acceleration is important in
astrophysics and geophysics. A promising model of the solar wind acceleration
is known as the wave/turbulence-driven (WTD) model, in which Alfv\'en waves
feed energy to the solar wind. In this study, we tested the WTD model with
global measurement of wind speed from interplanetary scintillation (IPS)
observations. For Carrington rotations in minimal and maximal activity phases,
we selected field lines calculated by the potential-field source-surface method
in high- and mid-latitudes and compared the simulated and observed wind
velocities. The simulation was performed in a self-consistent manner by solving
the magnetohydrodynamic equations from the photosphere to the solar wind. In
high-latitude regions, the simulated solar wind velocity agrees better with the
IPS observation than with the classical Wang--Sheeley empirical estimation,
both in maximal and minimal activity phases. In mid-latitude regions, the
agreement worsens, possibly because of the inaccuracy of the WTD model and/or
the magnetic-field extrapolation. Our results indicate that the high-latitude
solar wind is likely to be driven by waves and turbulence, and that the
physics-based prediction of the solar wind velocity is highly feasible with an
improved magnetic-field extrapolation.


title: Adaptive Cholesky Gaussian Processes

abstract:
  We present a method to fit exact Gaussian process models to large datasets by
considering only a subset of the data. Our approach is novel in that the size
of the subset is selected on the fly during exact inference with little
computational overhead. From an empirical observation that the log-marginal
likelihood often exhibits a linear trend once a sufficient subset of a dataset
has been observed, we conclude that many large datasets contain redundant
information that only slightly affects the posterior. Based on this, we provide
probabilistic bounds on the full model evidence that can identify such subsets.
Remarkably, these bounds are largely composed of terms that appear in
intermediate steps of the standard Cholesky decomposition, allowing us to
modify the algorithm to adaptively stop the decomposition once enough data have
been observed. Empirically, we show that our method can be directly plugged
into well-known inference schemes to fit exact Gaussian process models to large
datasets.


title:
A SBP-SAT FDTD Subgridding Method Using Staggered Yee's Grids Without
  Modifying Field Components

abstract:
  A summation-by-parts simultaneous approximation term (SBP-SAT)
finite-difference time-domain (FDTD) subgridding method is proposed to model
geometrically fine structures in this paper. Compared with our previous work,
the proposed SBP-SAT FDTD method uses the staggered Yee's grid without adding
or modifying any field components through field extrapolation on the boundaries
to make the discrete operators satisfy the SBP property. The accuracy of
extrapolation keeps consistency with that of the second-order finite-difference
scheme near the boundaries. In addition, the SATs are used to weakly enforce
the tangential boundary conditions between multiple mesh blocks with different
mesh sizes. With carefully designed interpolation matrices and selected free
parameters of the SATs, no dissipation occurs in the whole computational
domain. Therefore, its long-time stability is theoretically guaranteed. Three
numerical examples are carried out to validate its effectiveness. Results show
that the proposed SBP-SAT FDTD subgridding method is stable, accurate,
efficient, and easy to implement based on existing FDTD codes with only a few
modifications.


title: How Fast Can We Play Tetris Greedily With Rectangular Pieces?

abstract:
  Consider a variant of Tetris played on a board of width $w$ and infinite
height, where the pieces are axis-aligned rectangles of arbitrary integer
dimensions, the pieces can only be moved before letting them drop, and a row
does not disappear once it is full. Suppose we want to follow a greedy
strategy: let each rectangle fall where it will end up the lowest given the
current state of the board. To do so, we want a data structure which can always
suggest a greedy move. In other words, we want a data structure which maintains
a set of $O(n)$ rectangles, supports queries which return where to drop the
rectangle, and updates which insert a rectangle dropped at a certain position
and return the height of the highest point in the updated set of rectangles. We
show via a reduction to the Multiphase problem [P\u{a}tra\c{s}cu, 2010] that on
a board of width $w=\Theta(n)$, if the OMv conjecture [Henzinger et al., 2015]
is true, then both operations cannot be supported in time $O(n^{1/2-\epsilon})$
simultaneously. The reduction also implies polynomial bounds from the 3-SUM
conjecture and the APSP conjecture. On the other hand, we show that there is a
data structure supporting both operations in $O(n^{1/2}\log^{3/2}n)$ time on
boards of width $n^{O(1)}$, matching the lower bound up to a $n^{o(1)}$ factor.


title: Gravitational Casimir Effect in Inspiralling Neutron Star Binary

abstract:
  Currently, the discussions and investigations for the vacuum energy is
drawing great both theoretical and experimental attention. The vacuum states of
variety of fields, subject to special boundary conditions, may contribute to
non-trivial macroscopic vacuum energy, i.e. the Casimir effect, which become an
interdisciplinary subject and plays an important role in a variety of fields of
physics. We adopt Schwinger's source theory and study the Casimir effect due to
the quantization of gravitation, i.e. the gravitational Casimir effect (GCE),
in inspiraling neutron star (NS) binaries with wide separation of $10^9 \rm m$.
By considering gravitoelectromagnetism (GEM) arising from the spiral-in orbital
motion and evaluating the contributions of GEM to the vacuum energy of
gravitons radiated during the orbital decay, we demonstrate that, when the
orbital separation of the binary decay a distance of $L$ in radial direction,
the GEM results in a small Casimir correction to the gravitational vacuum
energy, which contributes to an attractive gravitational Casimir force to the
binary, in addition the gravitational force. The gravitational waves (GWs),
emitted from wide inspiraling NS binaries, locate in the low-frequency band of
$10^{-4}-1$ Hz. For a characteristic GW frequency of $10^{-3}$ Hz, the
gravitational Casimir correction to the signals is estimated as of the order of
$\sim10^{-24}$, which corresponds to a force of $10^{-20}$ N. By considering
that the sensitivity of space-based gravitational wave observatory, LISA/eLISA
and Taiji, can be reduced to $10^{-24}$, we would expect that LISA/eLISA and
Taiji with sensitivity improvements give the powerful tool to detect GCE in the
near future.


title:
Deep learning based domain adaptation for mitochondria segmentation on
  EM volumes

abstract:
  Accurate segmentation of electron microscopy (EM) volumes of the brain is
essential to characterize neuronal structures at a cell or organelle level.
While supervised deep learning methods have led to major breakthroughs in that
direction during the past years, they usually require large amounts of
annotated data to be trained, and perform poorly on other data acquired under
similar experimental and imaging conditions. This is a problem known as domain
adaptation, since models that learned from a sample distribution (or source
domain) struggle to maintain their performance on samples extracted from a
different distribution or target domain. In this work, we address the complex
case of deep learning based domain adaptation for mitochondria segmentation
across EM datasets from different tissues and species. We present three
unsupervised domain adaptation strategies to improve mitochondria segmentation
in the target domain based on (1) state-of-the-art style transfer between
images of both domains; (2) self-supervised learning to pre-train a model using
unlabeled source and target images, and then fine-tune it only with the source
labels; and (3) multi-task neural network architectures trained end-to-end with
both labeled and unlabeled images. Additionally, we propose a new training
stopping criterion based on morphological priors obtained exclusively in the
source domain. We carried out all possible cross-dataset experiments using
three publicly available EM datasets. We evaluated our proposed strategies on
the mitochondria semantic labels predicted on the target datasets. The methods
introduced here outperform the baseline methods and compare favorably to the
state of the art. In the absence of validation labels, monitoring our proposed
morphology-based metric is an intuitive and effective way to stop the training
process and select in average optimal models.


title:
Social Computational Design Method for Generating Product Shapes with
  GAN and Transformer Models

abstract:
  A social computational design method is established, aiming at taking
advantages of the fast-developing artificial intelligence technologies for
intelligent product design. Supported with multi-agent system, shape grammar,
Generative adversarial network, Bayesian network, Transformer, etc., the method
is able to define the design solution space, prepare training samples, and
eventually acquire an intelligent model that can recommend design solutions
according to incomplete solutions for given design tasks. Product shape design
is used as entry point to demonstrate the method, however, the method can be
applied to tasks rather than shape design when the solutions can be properly
coded.


title: Totally geodesic submanifolds in exceptional symmetric spaces

abstract:
  We classify maximal totally geodesic submanifolds in exceptional symmetric
spaces up to isometry. Moreover, we introduce an invariant for certain totally
geodesic embeddings of semisimple symmetric spaces, which we call the Dynkin
index. We prove a result analogous to the index conjecture: for every
irreducible symmetric space of non-compact type, there exists a totally
geodesic submanifold which is of minimal codimension and whose non-flat
irreducible factors have Dynkin index equal to one.


title:
XtraLibD: Detecting Irrelevant Third-Party libraries in Java and Python
  Applications

abstract:
  Software development comprises the use of multiple Third-Party Libraries
(TPLs). However, the irrelevant libraries present in software application's
distributable often lead to excessive consumption of resources such as CPU
cycles, memory, and modile-devices' battery usage. Therefore, the
identification and removal of unused TPLs present in an application are
desirable. We present a rapid, storage-efficient, obfuscation-resilient method
to detect the irrelevant-TPLs in Java and Python applications. Our approach's
novel aspects are i) Computing a vector representation of a .class file using a
model that we call Lib2Vec. The Lib2Vec model is trained using the Paragraph
Vector Algorithm. ii) Before using it for training the Lib2Vec models, a .class
file is converted to a normalized form via semantics-preserving
transformations. iii) A eXtra Library Detector (XtraLibD) developed and tested
with 27 different language-specific Lib2Vec models. These models were trained
using different parameters and >30,000 .class and >478,000 .py files taken from
>100 different Java libraries and 43,711 Python available at MavenCentral.com
and Pypi.com, respectively. XtraLibD achieves an accuracy of 99.48% with an F1
score of 0.968 and outperforms the existing tools, viz., LibScout, LiteRadar,
and LibD with an accuracy improvement of 74.5%, 30.33%, and 14.1%,
respectively. Compared with LibD, XtraLibD achieves a response time improvement
of 61.37% and a storage reduction of 87.93% (99.85% over JIngredient). Our
program artifacts are available at https://www.doi.org/10.5281/zenodo.5179747.


title: Continuous Speech for Improved Learning Pathological Voice Disorders

abstract:
  Goal: Numerous studies had successfully differentiated normal and abnormal
voice samples. Nevertheless, further classification had rarely been attempted.
This study proposes a novel approach, using continuous Mandarin speech instead
of a single vowel, to classify four common voice disorders (i.e. functional
dysphonia, neoplasm, phonotrauma, and vocal palsy). Methods: In the proposed
framework, acoustic signals are transformed into mel-frequency cepstral
coefficients, and a bi-directional long-short term memory network (BiLSTM) is
adopted to model the sequential features. The experiments were conducted on a
large-scale database, wherein 1,045 continuous speech were collected by the
speech clinic of a hospital from 2012 to 2019. Results: Experimental results
demonstrated that the proposed framework yields significant accuracy and
unweighted average recall improvements of 78.12-89.27% and 50.92-80.68%,
respectively, compared with systems that use a single vowel. Conclusions: The
results are consistent with other machine learning algorithms, including gated
recurrent units, random forest, deep neural networks, and LSTM. The
sensitivities for each disorder were also analyzed, and the model capabilities
were visualized via principal component analysis. An alternative experiment
based on a balanced dataset again confirms the advantages of using continuous
speech for learning voice disorders.


title:
Toward Functionalized Ultrathin Oxide Films: the Impact of Surface
  Apical Oxygen

abstract:
  Thin films of transition metal oxides open up a gateway to nanoscale
electronic devices beyond silicon characterized by novel electronic
functionalities. While such films are commonly prepared in an oxygen
atmosphere, they are typically considered to be ideally terminated with the
stoichiometric composition. Using the prototypical correlated metal SrVO$_3$ as
an example, it is demonstrated that this idealized description overlooks an
essential ingredient: oxygen adsorbing at the surface apical sites. The oxygen
adatoms, which persist even in an ultrahigh vacuum environment, are shown to
severely affect the intrinsic electronic structure of a transition metal oxide
film. Their presence leads to the formation of an electronically dead surface
layer but also alters the band filling and the electron correlations in the
thin films. These findings highlight that it is important to take into account
surface apical oxygen or -- mutatis mutandis -- the specific oxygen
configuration imposed by a capping layer to predict the behavior of ultrathin
films of transition metal oxides near the single unit-cell limit.


title:
SHK22.h: Neural Network QCD analysis of charged hadron Fragmentation
  Functions in the presence of SIDIS data

abstract:
  In this paper, we present a QCD analysis to extract the Fragmentation
Functions (FFs) of unidentified light charged hadron entitled as SHK22.h from
high-energy lepton-lepton annihilation and lepton-hadron scattering data sets.
This analysis includes the data from all available single inclusive
electron-positron annihilation (SIA) processes and semi-inclusive
deep-inelastic scattering (SIDIS) measurements for the unidentified light
charged hadron productions. The SIDIS data which has been measured by the
COMPASS experiment could allow the flavor dependence of the FFs to be well
constrained. We exploit the analytic derivative of the Neural Network (NN) for
the parametrization of FFs at next-to-leading-order (NLO) accuracy in the
perturbative QCD (pQCD). The Monte Carlo method is implied for all sources of
experimental uncertainties and the Parton distribution functions (PDFs) as
well. Very good agreements are achieved between the SHK22.h FFs set and the
most recent QCD fits available in literature, namely JAM20 and NNFF1.1h. In
addition, we discuss the impact arising from the inclusion of SIDIS data on the
extracted light-charged hadron FFs. The global QCD resulting at NLO for charged
hadron FFs provides valuable insights for applications in the present and
future high-energy measurement of charged hadron final state processes.


title: North Ecliptic Pole merging galaxy catalogue

abstract:
  We aim to generate a catalogue of merging galaxies within the 5.4 sq. deg.
North Ecliptic Pole over the redshift range $0.0 < z < 0.3$. To do this,
imaging data from the Hyper Suprime-Cam are used along with morphological
parameters derived from these same data.
  The catalogue was generated using a hybrid approach. Two neural networks were
trained to perform binary merger non-merger classifications: one for galaxies
with $z < 0.15$ and another for $0.15 \leq z < 0.30$. Each network used the
image and morphological parameters of a galaxy as input. The galaxies that were
identified as merger candidates by the network were then visually checked by
experts. The resulting mergers will be used to calculate the merger fraction as
a function of redshift and compared with literature results.
  We found that 86.3% of galaxy mergers at $z < 0.15$ and 79.0% of mergers at
$0.15 \leq z < 0.30$ are expected to be correctly identified by the networks.
Of the 34 264 galaxies classified by the neural networks, 10 195 were found to
be merger candidates. Of these, 2109 were visually identified to be merging
galaxies. We find that the merger fraction increases with redshift, consistent
with literature results from observations and simulations, and that there is a
mild star-formation rate enhancement in the merger population of a factor of
$1.102 \pm 0.084$.


title:
A steady state approach for studying valley relaxation using optical
  vortex beam

abstract:
  Spin-valley coupling in monolayer transition metal dichalcogenides gives rise
to valley polarization and coherence effect, limited by intervalley scattering
caused by exciton-phonon, exciton-impurity, and electron-hole exchange
interaction (EHEI). We explore an approach to tune the EHEI by controlling
excitons center of mass momentum (COM) utilizing the photon distribution of
higher-order optical vortex beam. By virtue of this, we have observed excitons
COM-dependent valley depolarization and decoherence which gives us the ability
to measure the timescale associated with valley dynamics in the steady-state
measurement. Our steady-state technique to probe the valley dynamics can open
up a new paradigm to explore the physics of excitons in two-dimensional
systems.


title:
Pad\'e approximation for a class of hypergeometric functions and
  parametric geometry of numbers

abstract:
  In this article we obtain new irrationality measures for values of functions
which belong to a certain class of hypergeometric functions including shifted
logarithmic functions, binomial functions and shifted exponential functions. We
explicitly construct Pad\'e approximations by using a formal method and show
that the associated sequences satisfy a Poincar\'e-type recurrence. To study
precisely the asymptotic behavior of those sequences, we establish an
\emph{effective} version of the Poincar\'e-Perron theorem. As a consequence we
obtain, among others, effective irrationality measures for values of binomial
functions at rational numbers, which might have useful arithmetic applications.
A general theorem on simultaneous rational approximations that we need is
proven by using new arguments relying on parametric geometry of numbers.


title:
A passive admittance controller to enforce Remote Center of Motion and
  Tool Spatial constraints with application in hands-on surgical procedures

abstract:
  The restriction of feasible motions of a manipulator link constrained to move
through an entry port is a common problem in minimum invasive surgery
procedures. Additional spatial restrictions are required to ensure the safety
of sensitive regions from unintentional damage. In this work, we design a
target admittance model that is proved to enforce robot tool manipulation by a
human through a remote center of motion and to guarantee that the tool will
never enter or touch forbidden regions. The control scheme is proved passive
under the exertion of a human force ensuring manipulation stability, and smooth
natural motion in hands-on surgical procedures enhancing the user's feeling of
control over the task. Its performance is demonstrated by experiments with a
setup mimicking a hands-on surgical procedure comprising a KUKA LWR4+ and a
virtual intraoperative environment.


title: RuCLIP -- new models and experiments: a technical report

abstract:
  In the report we propose six new implementations of ruCLIP model trained on
our 240M pairs. The accuracy results are compared with original CLIP model with
Ru-En translation (OPUS-MT) on 16 datasets from different domains. Our best
implementations outperform CLIP + OPUS-MT solution on most of the datasets in
few-show and zero-shot tasks. In the report we briefly describe the
implementations and concentrate on the conducted experiments. Inference
execution time comparison is also presented in the report.


title:
Comparing the latest ranking techniques: pros and cons of flexible
  skylines, regret minimization and skyline ranking queries

abstract:
  Long-established ranking approaches, such as top-k and skyline queries, have
been thoroughly discussed and their drawbacks are well acknowledged. New
techniques have been developed in recent years that try to combine traditional
ones to overcome their limitations. In this paper we focus our attention on
some of them: flexible skylines, regret minimization and skyline ranking
queries, because, while these new methods are promising and have shown
interesting results, a comparison between them is still not available. After a
short introduction of each approach, we discuss analogies and differences
between them with the advantages and disadvantages of every technique debated.


title: A Hybrid Cloud ERP Framework For Processing Purchasing Data

abstract:
  Cloud-based enterprise resource planning (cloud ERP) systems have existed in
the business market for around ten years. Cloud ERP supports enterprises' daily
activities by integrating organizational back-end systems in the cloud
environment. One of the critical functions that cloud ERP offers is the
purchasing application. The purchasing function of cloud ERP enables
enterprises to streamline all the online purchasing transactions in real-time
automatically. Even cloud ERP is deployed quite often these days, organizations
somehow still lack the knowledge of it; to be specific, there are many issues
attached to cloud ERP implementation yet to be solved. Hence, this paper
compares four leading cloud ERP platforms in Australia and proposes a hybrid
cloud ERP framework to process online purchasing transactions. By adopting a
case study approach, a purchasing web-based application is designed and
presented in this paper. In general, the proposed hybrid cloud ERP framework
and the integrated web-based purchasing application allow user companies to
process online purchasing transactions with short operation time and increased
business efficiency; in the meantime, the proposed framework also reduces
security risks attached to the public cloud.


title: VU-BERT: A Unified framework for Visual Dialog

abstract:
  The visual dialog task attempts to train an agent to answer multi-turn
questions given an image, which requires the deep understanding of interactions
between the image and dialog history. Existing researches tend to employ the
modality-specific modules to model the interactions, which might be troublesome
to use. To fill in this gap, we propose a unified framework for image-text
joint embedding, named VU-BERT, and apply patch projection to obtain vision
embedding firstly in visual dialog tasks to simplify the model. The model is
trained over two tasks: masked language modeling and next utterance retrieval.
These tasks help in learning visual concepts, utterances dependence, and the
relationships between these two modalities. Finally, our VU-BERT achieves
competitive performance (0.7287 NDCG scores) on VisDial v1.0 Datasets.


title: Explicit Regularization via Regularizer Mirror Descent

abstract:
  Despite perfectly interpolating the training data, deep neural networks
(DNNs) can often generalize fairly well, in part due to the "implicit
regularization" induced by the learning algorithm. Nonetheless, various forms
of regularization, such as "explicit regularization" (via weight decay), are
often used to avoid overfitting, especially when the data is corrupted. There
are several challenges with explicit regularization, most notably unclear
convergence properties. Inspired by convergence properties of stochastic mirror
descent (SMD) algorithms, we propose a new method for training DNNs with
regularization, called regularizer mirror descent (RMD). In highly
overparameterized DNNs, SMD simultaneously interpolates the training data and
minimizes a certain potential function of the weights. RMD starts with a
standard cost which is the sum of the training loss and a convex regularizer of
the weights. Reinterpreting this cost as the potential of an "augmented"
overparameterized network and applying SMD yields RMD. As a result, RMD
inherits the properties of SMD and provably converges to a point "close" to the
minimizer of this cost. RMD is computationally comparable to stochastic
gradient descent (SGD) and weight decay, and is parallelizable in the same
manner. Our experimental results on training sets with various levels of
corruption suggest that the generalization performance of RMD is remarkably
robust and significantly better than both SGD and weight decay, which
implicitly and explicitly regularize the $\ell_2$ norm of the weights. RMD can
also be used to regularize the weights to a desired weight vector, which is
particularly relevant for continual learning.


title: Decomposing random permutations into order-isomorphic subpermutations

abstract:
  Two permutations $s$ and $t$ are $k$-similar if they can be decomposed into
subpermutations $s^1, \ldots, s^k$ and $t^1, \ldots, t^k$ such that $s^i$ is
order-isomorphic to $t^i$ for all $i$. Recently, Dudek, Grytczuk and Ruci\'nski
posed the problem of determining the minimum $k$ for which two permutations
chosen independently and uniformly at random are $k$-similar. We show that two
such permutations are $O(n^{1/3}\log^{11/6}(n))$-similar with high probability,
which is tight up to a polylogarithmic factor. Our result also generalises to
simultaneous decompositions of multiple permutations.