title: Near Instance-Optimal PAC Reinforcement Learning for Deterministic MDPs

abstract:
  In probably approximately correct (PAC) reinforcement learning (RL), an agent
is required to identify an $\epsilon$-optimal policy with probability
$1-\delta$. While minimax optimal algorithms exist for this problem, its
instance-dependent complexity remains elusive in episodic Markov decision
processes (MDPs). In this paper, we propose the first (nearly) matching upper
and lower bounds on the sample complexity of PAC RL in deterministic episodic
MDPs with finite state and action spaces. In particular, our bounds feature a
new notion of sub-optimality gap for state-action pairs that we call the
deterministic return gap. While our instance-dependent lower bound is written
as a linear program, our algorithms are very simple and do not require solving
such an optimization problem during learning. Their design and analyses employ
novel ideas, including graph-theoretical concepts such as minimum flows and
maximum cuts, which we believe to shed new light on this problem.


title:
Study of quantum gravity correction to the accretion onto the black hole
  in the GUP framework

abstract:
  We study the quantum gravity correction to the accretion onto the modified
Schwarzschild black hole in the GUP framework. The GUP which is associated with
the minimum measurable length modifies the Schwarzschild black hole which gets
endowed with quantum gravity correction. We examine the possible positions of
critical points and obtain the critical velocity. For polytropic matter, we
compute the correction to the temperature and the total integrated flux at the
event horizon with reference to the minimum measurable length. We observe that
accretion has a crucial dependence on the quantum effect of gravity marked by
the GUP parameter.


title: Visualizing Riemannian data with Rie-SNE

abstract:
  Faithful visualizations of data residing on manifolds must take the
underlying geometry into account when producing a flat planar view of the data.
In this paper, we extend the classic stochastic neighbor embedding (SNE)
algorithm to data on general Riemannian manifolds. We replace standard Gaussian
assumptions with Riemannian diffusion counterparts and propose an efficient
approximation that only requires access to calculations of Riemannian distances
and volumes. We demonstrate that the approach also allows for mapping data from
one manifold to another, e.g. from a high-dimensional sphere to a
low-dimensional one.


title:
On Flag-Transitive $2$-$(k^{2}, k, \lambda)$ Designs with $\lambda \mid
  k$

abstract:
  It is shown that, apart from the smallest Ree group, a flag-transitive
automorphism group $G$ of a $2$-$(k^{2}, k, \lambda)$ design D, with $\lambda
\mid k$, is either an affine group or an almost simple classical group.
Moreover, when $G$ is the smallest Ree group, $\mathcal{D}$ is isomorphic
either to the $2$-$(62, 6, 2)$ design or to one of the three $2$- $(62, 6, 6)$
designs constructed in this paper. All the four $2$-designs have the $36$
secants of a nondegenerate conic $\mathcal{C}$ of $PG_{2}(8)$ as a point set
and 6-sets of secants in a remarkable configuration as a block set.


title:
On the Spectral Bias of Convolutional Neural Tangent and Gaussian
  Process Kernels

abstract:
  We study the properties of various over-parametrized convolutional neural
architectures through their respective Gaussian process and neural tangent
kernels. We prove that, with normalized multi-channel input and ReLU
activation, the eigenfunctions of these kernels with the uniform measure are
formed by products of spherical harmonics, defined over the channels of the
different pixels. We next use hierarchical factorizable kernels to bound their
respective eigenvalues. We show that the eigenvalues decay polynomially,
quantify the rate of decay, and derive measures that reflect the composition of
hierarchical features in these networks. Our results provide concrete
quantitative characterization of over-parameterized convolutional network
architectures.


title: The bondage number of chordal graphs

abstract:
  A set $S\subseteq V(G)$ of a graph $G$ is a dominating set if each vertex has
a neighbor in $S$ or belongs to $S$. Let $\gamma(G)$ be the cardinality of a
minimum dominating set in $G$. The bondage number $b(G)$ of a graph $G$ is the
smallest cardinality of a set edges $A\subseteq E(G)$ such that
$\gamma(G-A)=\gamma(G)+1$. A chordal graph is a graph with no induced cycle of
length four or more. In this paper, we prove that the bondage number of a
chordal graph $G$ is at most the order of its maximum clique, that is,
$b(G)\leq \omega(G)$. We show that this bound is best possible.


title: Contrastive Learning for Cross-Domain Open World Recognition

abstract:
  The ability to evolve is fundamental for any valuable autonomous agent whose
knowledge cannot remain limited to that injected by the manufacturer. Consider
for example a home assistant robot: it should be able to incrementally learn
new object categories when requested, but also to recognize the same objects in
different environments (rooms) and poses (hand-held/on the floor/above
furniture), while rejecting unknown ones. Despite its importance, this scenario
has started to raise interest in the robotic community only recently and the
related research is still in its infancy, with existing experimental testbeds
but no tailored methods. With this work, we propose the first learning approach
that deals with all the previously mentioned challenges at once by exploiting a
single contrastive objective. We show how it learns a feature space perfectly
suitable to incrementally include new classes and is able to capture knowledge
which generalizes across a variety of visual domains. Our method is endowed
with a tailored effective stopping criterion for each learning episode and
exploits a novel self-paced thresholding strategy that provides the classifier
with a reliable rejection option. Both these contributions are based on the
observation of the data statistics and do not need manual tuning. An extensive
experimental analysis confirms the effectiveness of the proposed approach
establishing the new state-of-the-art. The code is available at
https://github.com/FrancescoCappio/Contrastive_Open_World.


title: Explainability in Graph Neural Networks: An Experimental Survey

abstract:
  Graph neural networks (GNNs) have been extensively developed for graph
representation learning in various application domains. However, similar to all
other neural networks models, GNNs suffer from the black-box problem as people
cannot understand the mechanism underlying them. To solve this problem, several
GNN explainability methods have been proposed to explain the decisions made by
GNNs. In this survey, we give an overview of the state-of-the-art GNN
explainability methods and how they are evaluated. Furthermore, we propose a
new evaluation metric and conduct thorough experiments to compare GNN
explainability methods on real world datasets. We also suggest future
directions for GNN explainability.


title:
Emittance growth of kicked and mismatched beams due to
  amplitude-dependent tune shift

abstract:
  We derive evolution equations for the first and second moments of an
initially mismatched, coupled, and displaced arbitrary Gaussian phase-space
distribution under the influence of decoherence due to amplitude-dependent tune
shift. Moreover, we find expressions for the asymptotic values of the beam
matrix and the emittance and use them to evaluate error tolerances for
injection.


title:
Submillimetre galaxies in two massive protoclusters at z = 2.24:
  witnessing the enrichment of extreme starbursts in the outskirts of HAE
  density peaks

abstract:
  Submillimetre galaxies represent a rapid growth phase of both star formation
and massive galaxies. Mapping SMGs in galaxy protoclusters provides key
insights into where and how these extreme starbursts take place in connections
with the assembly of the large-scale structure in the early Universe. We search
for SMGs at 850$\,\mu m$ using JCMT/SCUBA-2 in two massive protoclusters at
$z=2.24$, BOSS1244 and BOSS1542, and detect 43 and 54 sources with
$S_{850}>4\,$mJy at the $4\sigma$ level within an effective area of
264$\,$arcmin$^2$, respectively. We construct the intrinsic number counts and
find that the abundance of SMGs is $2.0\pm0.3$ and $2.1\pm0.2$ times that of
the general fields, confirming that BOSS1244 and BOSS1542 contain a higher
fraction of dusty galaxies with strongly enhanced star formation. The volume
densities of the SMGs are estimated to be $\sim15-$30 times the average,
significantly higher than the overdensity factor ($\sim 6$) traced by H$\alpha$
emission-line galaxies (HAEs). More importantly, we discover a prominent offset
between the spatial distributions of the two populations in these two
protoclusters -- SMGs are mostly located around the high-density regions of
HAEs, and few are seen inside these regions. This finding may have revealed for
the first time the occurrence of violent star formation enhancement in the
outskirts of the HAE density peaks, likely driven by the boosting of gas
supplies and/or starburst triggering events. Meanwhile, the lack of SMGs inside
the most overdense regions at $z\sim2$ implies a transition to the environment
disfavouring extreme starbursts.


title:
Flag-transitive, point-imprimitive symmetric $2$-$(v,k,\lambda )$
  designs with $k>\lambda \left(\lambda-3 \right)/2$

abstract:
  Let $\mathcal{D}=\left(\mathcal{P},\mathcal{B} \right)$ be a symmetric
$2$-$(v,k,\lambda )$ design admitting a flag-transitive, point-imprimitive
automorphism group $G$ that leaves invariant a non-trivial partition $\Sigma$
of $\mathcal{P}$. Praeger and Zhou \cite{PZ} have shown that, there is a
constant $k_{0}$ such that, for each $B \in \mathcal{B}$ and $\Delta \in
\Sigma$, the size of $\left\vert B \cap \Delta \right \vert$ is either $0$ or
$k_{0}$. In the present paper we show that, if $k>\lambda \left(\lambda-3
\right)/2$ and $k_{0} \geq 3$, $\mathcal{D}$ is isomorphic to one of the known
flag-transitive, point-imprimitive symmetric $2$-designs with parameters
$(45,12,3)$ or $(96,20,4)$.


title: An inverse problem for the Riemannian minimal surface equation

abstract:
  In this paper we consider determining a minimal surface embedded in a
Riemannian manifold $\Sigma\times \mathbb{R}$. We show that if $\Sigma$ is a
two dimensional Riemannian manifold with boundary, then the knowledge of the
associated Dirichlet-to-Neumann map for the minimal surface equation determine
$\Sigma$ up to an isometry.


title:
Generalized Ellis-Bronikov traversable wormholes in $f(R)$ gravity with
  anisotropic dark matter

abstract:
  This paper studies generalized Ellis-Bronikov (E-B) traversable wormholes in
$f(R)$ extended gravity. We assume that these wormholes are supported by
anisotropic dark matter (DM) according to the most often used phenomenological
models, namely those of Navarro-Frenk-White (NFW), Thomas-Fermi (T-F), and
Pseudo-isothermal (PI). Initially, we obtain the field equations in a general
scenario of $f(R)$ theories in the metric formalism for the static and
spherically symmetric Morris-Thorne spacetime. Then we particularize to a
$f(R)$ model with power-law, including the Starobinky modified gravity.
Following, we analyze the energy conditions which are not dependent on the DM
models (Null and Weak Energy Conditions -- NEC and WEC) and those which are
model-dependent (Strong and Dominant Energy Conditions SEC and DEC). Finally,
we compare some E-B wormhole solutions and the mentioned DM models, discussing
the feasibility of the wormhole-dark matter system in different scenarios.


title: Number of zero-energy eigenstates in the PXP model

abstract:
  The PXP model is paradigmatic in the field of quantum many-body scars. This
model has a number of zero-energy eigenstates that is exponentially large in
system size. Lower bounds on the number of zero-energy eigenstates are obtained
for both open and periodic (zero and $\pi$-momentum sectors) boundary
conditions. These bounds are found to be tight up to system sizes accessible by
numerical exact diagonalization, and can be expected to be tight in general. In
addition to previous results, separate lower bounds are obtained for the
spatial inversion-symmetric and inversion-antisymmetric symmetry sectors.
Furthermore, the derivations improve on previous ones as these are free of
assumptions.


title:
Shift invariance and reflexivity of compressions of multiplication
  operators

abstract:
  The property of being shift invariant and being reflexive or transitive in
the case of the space of (asymmetric) truncated Toeplitz operators, and the
space of (asymmetric) dual truncated operators is investigated. Most of the
results obtained are new even for the symmetric case. A characterization of
asymmetric dual truncated Toeplitz operators is also given.


title: The role of density perturbation on planet formation by pebble accretion

abstract:
  Protoplanetary discs exhibit a diversity of gaps and rings of dust material,
believed to be a manifestation of pressure maxima commonly associated with an
ongoing planet formation and several other physical processes. Hydrodynamic
disc simulations further suggest that multiple dust ring-like structures may be
ubiquitous in discs. In the recent past, it has been shown that dust rings may
provide a suitable avenue for planet formation. We study how a globally
perturbed disc affects dust evolution and core growth by pebble accretion. We
performed global disc simulations featuring a Gaussian pressure profile, in
tandem with global perturbations of the gas density, mimicking wave-like
structures, and simulated planetary core formation at pressure minima and
maxima. With Gaussian pressure profiles, grains in the inside disc regions were
extremely depleted in the first 0.1 Myrs of disc lifetime. The global pressure
bumps confined dust material for several million years, depending on the
strength of perturbations. A variety of cores formed in bumpy discs, with
massive cores at locations where core growth was not feasible in a smooth disc,
and small cores at locations where massive cores could form in a smooth disc.
We conclude that pressure bumps generated by a planet and/or other physical
phenomena can completely thwart planet formation from the inside parts of the
disc. While inner disc parts are most favourable for pebble accretion in a
smooth disc, multiple wave-like pressure bumps can promote rapid planet
formation by pebble accretion in broad areas of the disc.


title:
Classification of the non-trivial $2$-$(k^{2},k,\lambda )$ designs, with
  $\lambda \mid k$, admitting a flag-transitive almost simple automorphism
  group

abstract:
  Non-trivial $2$-$(k^{2},k,\lambda )$ designs, with $\lambda \mid k$,
admitting a flag-transitive almost simple automorphism group are classified.


title:
Progressive Subsampling for Oversampled Data -- Application to
  Quantitative MRI

abstract:
  We present PROSUB: PROgressive SUBsampling, a deep learning based, automated
methodology that subsamples an oversampled data set (e.g. multi-channeled 3D
images) with minimal loss of information. We build upon a recent dual-network
approach that won the MICCAI MUlti-DIffusion (MUDI) quantitative MRI
measurement sampling-reconstruction challenge, but suffers from deep learning
training instability, by subsampling with a hard decision boundary. PROSUB uses
the paradigm of recursive feature elimination (RFE) and progressively
subsamples measurements during deep learning training, improving optimization
stability. PROSUB also integrates a neural architecture search (NAS) paradigm,
allowing the network architecture hyperparameters to respond to the subsampling
process. We show PROSUB outperforms the winner of the MUDI MICCAI challenge,
producing large improvements >18% MSE on the MUDI challenge sub-tasks and
qualitative improvements on downstream processes useful for clinical
applications. We also show the benefits of incorporating NAS and analyze the
effect of PROSUB's components. As our method generalizes to other problems
beyond MRI measurement selection-reconstruction, our code is
https://github.com/sbb-gh/PROSUB


title:
Quasi-solid-state electrolyte for ultra-high safety and cycle stability
  battery

abstract:
  All-solid-state lithium batteries (ASSLB) have been regarded as the most
promising candidate to achieve the next generation energy storage with high
energy and high safety. However, some bottlenecks, including high interfacial
resistance, bad electrochemical stability, and low conductivity, have hindered
its further development. Here, we developed a Pyr13FSI/LiFSI-based gel
electrolyte and used it in the LFP/LTO full battery system to achieve a
lithium-ion battery with high safety and cycle stability. The presence of ionic
liquid in the electrolyte reduces the crystallinity of PVDF-HFP polymer matrix,
increases the ion conductivity of the electrolyte, and greatly improves the
electrode-electrolyte interface contact. These advantages enable the battery to
work at room temperature and reach a specific capacity of 123mAh/g at the
current of 1C. The slightly change in interfacial resistances between the gel
electrolyte and electrodes with the increase of the cycle numbers is confirmed
through electrochemical impedance spectroscopy. The high electrochemical
stability of the electrolyte in the LFP/LTO system makes the battery exhibit
good cycle stability, and the battery maintains 80% of its initial capacity
after 2000 cycles at the current of 1C. In addition, benefitting from the
excellent properties of ionic liquids, such as non-flammability, negligible
vapour pressure, and high conductivity, the obtained gel electrolyte based
LFP/LTO pouch battery exhibits high safety and cycle stability.


title:
Mixing Up Contrastive Learning: Self-Supervised Representation Learning
  for Time Series

abstract:
  The lack of labeled data is a key challenge for learning useful
representation from time series data. However, an unsupervised representation
framework that is capable of producing high quality representations could be of
great value. It is key to enabling transfer learning, which is especially
beneficial for medical applications, where there is an abundance of data but
labeling is costly and time consuming. We propose an unsupervised contrastive
learning framework that is motivated from the perspective of label smoothing.
The proposed approach uses a novel contrastive loss that naturally exploits a
data augmentation scheme in which new samples are generated by mixing two data
samples with a mixing component. The task in the proposed framework is to
predict the mixing component, which is utilized as soft targets in the loss
function. Experiments demonstrate the framework's superior performance compared
to other representation learning approaches on both univariate and multivariate
time series and illustrate its benefits for transfer learning for clinical time
series.


title: A flexible solution to embrace Ranking and Skyline queries approaches

abstract:
  The multi-objective optimization problem has always been the main objective
of the principal traditional approaches, such as Ranking queries and Skyline
queries. The conventional idea was to either use one or the other, trying to
exploit both ranking queries advantages when it comes to taking into account
user preferences, and skyline queries points of strength when the main
objective was to obtain interesting results from a dataset in a simple, yet
effective fashion, both of them showing limitations when entering specific
fields of interest.


title: An inverse problem for the minimal surface equation

abstract:
  We use the method of higher order linearization to study an inverse boundary
value problem for the minimal surface equation on a Riemannian manifold
$(\mathbb{R}^{n},g)$, where the metric $g$ is conformally Euclidean. In
particular we show that with the knowledge of Dirichlet-to-Neumann map
associated to the minimal surface equation, one can determine the Taylor series
of the conformal factor $c(x)$ at $x_n=0$.


title:
On the solution of Warring Problem with a multiplicative error term:
  dimension free estimates

abstract:
  We give a relation between the radius and the dimension in which the
asymptotic formula in the Warring problem holds in a multiplicative and
dimension-free fashion.


title: Almost complex Hodge theory

abstract:
  We review the recent development of Hodge theory for almost complex
manifolds. This includes the determination of whether the Hodge numbers defined
by $\bar\partial$-Laplacian are almost complex, almost K\"ahler, or birational
invariants in dimension four.


title:
ART-SS: An Adaptive Rejection Technique for Semi-Supervised restoration
  for adverse weather-affected images

abstract:
  In recent years, convolutional neural network-based single image adverse
weather removal methods have achieved significant performance improvements on
many benchmark datasets. However, these methods require large amounts of
clean-weather degraded image pairs for training, which is often difficult to
obtain in practice. Although various weather degradation synthesis methods
exist in the literature, the use of synthetically generated weather degraded
images often results in sub-optimal performance on the real weather degraded
images due to the domain gap between synthetic and real-world images. To deal
with this problem, various semi-supervised restoration (SSR) methods have been
proposed for deraining or dehazing which learn to restore the clean image using
synthetically generated datasets while generalizing better using unlabeled
real-world images. The performance of a semi-supervised method is essentially
based on the quality of the unlabeled data. In particular, if the unlabeled
data characteristics are very different from that of the labeled data, then the
performance of a semi-supervised method degrades significantly. We
theoretically study the effect of unlabeled data on the performance of an SSR
method and develop a technique that rejects the unlabeled images that degrade
the performance. Extensive experiments and ablation study show that the
proposed sample rejection method increases the performance of existing SSR
deraining and dehazing methods significantly. Code is available at
:https://github.com/rajeevyasarla/ART-SS


title: Stochastic and Private Nonconvex Outlier-Robust PCA

abstract:
  We develop theoretically guaranteed stochastic methods for outlier-robust
PCA. Outlier-robust PCA seeks an underlying low-dimensional linear subspace
from a dataset that is corrupted with outliers. We are able to show that our
methods, which involve stochastic geodesic gradient descent over the
Grassmannian manifold, converge and recover an underlying subspace in various
regimes through the development of a novel convergence analysis. The main
application of this method is an effective differentially private algorithm for
outlier-robust PCA that uses a Gaussian noise mechanism within the stochastic
gradient method. Our results emphasize the advantages of the nonconvex methods
over another convex approach to solving this problem in the differentially
private setting. Experiments on synthetic and stylized data verify these
results.


title:
Beauty and the beast: A case study on performance prototyping of
  data-intensive containerized cloud applications

abstract:
  Data-intensive container-based cloud applications have become popular with
the increased use cases in the Internet of Things domain. Challenges arise when
engineering such applications to meet quality requirements, both classical ones
like performance and emerging ones like elasticity and resilience. There is a
lack of reference use cases, applications, and experiences when prototyping
such applications that could benefit the research community. Moreover, it is
hard to generate realistic and reliable workloads that exercise the resources
according to a specification. Hence, designing reference applications that
would exhibit similar performance behavior in such environments is hard. In
this paper, we present a work in progress towards a reference use case and
application for data-intensive containerized cloud applications having an
industrial motivation. Moreover, to generate reliable CPU workloads we make use
of ProtoCom, a well-known library for the generation of resource demands, and
report the performance under various quality requirements in a Kubernetes
cluster of moderate size. Finally, we present the scalability of the current
solution assuming a particular autoscaling policy. Results of the calibration
show high variability of the ProtoCom library when executed in a cloud
environment. We observe a moderate association between the occupancy of node
and the relative variability of execution time.


title:
Confidence Calibration for Intent Detection via Hyperspherical Space and
  Rebalanced Accuracy-Uncertainty Loss

abstract:
  Data-driven methods have achieved notable performance on intent detection,
which is a task to comprehend user queries. Nonetheless, they are controversial
for over-confident predictions. In some scenarios, users do not only care about
the accuracy but also the confidence of model. Unfortunately, mainstream neural
networks are poorly calibrated, with a large gap between accuracy and
confidence. To handle this problem defined as confidence calibration, we
propose a model using the hyperspherical space and rebalanced
accuracy-uncertainty loss. Specifically, we project the label vector onto
hyperspherical space uniformly to generate a dense label representation matrix,
which mitigates over-confident predictions due to overfitting sparce one-hot
label matrix. Besides, we rebalance samples of different accuracy and
uncertainty to better guide model training. Experiments on the open datasets
verify that our model outperforms the existing calibration methods and achieves
a significant improvement on the calibration metric.


title:
Transfer learning for cross-modal demand prediction of bike-share and
  public transit

abstract:
  The urban transportation system is a combination of multiple transport modes,
and the interdependencies across those modes exist. This means that the travel
demand across different travel modes could be correlated as one mode may
receive demand from or create demand for another mode, not to mention natural
correlations between different demand time series due to general demand flow
patterns across the network. It is expectable that cross-modal ripple effects
become more prevalent, with Mobility as a Service. Therefore, by propagating
demand data across modes, a better demand prediction could be obtained. To this
end, this study explores various machine learning models and transfer learning
strategies for cross-modal demand prediction. The trip data of bike-share,
metro, and taxi are processed as the station-level passenger flows, and then
the proposed prediction method is tested in the large-scale case studies of
Nanjing and Chicago. The results suggest that prediction models with transfer
learning perform better than unimodal prediction models. Furthermore, stacked
Long Short-Term Memory model performs particularly well in cross-modal demand
prediction. These results verify our combined method's forecasting improvement
over existing benchmarks and demonstrate the good transferability for
cross-modal demand prediction in multiple cities.


title:
Knowledge Graph Embedding Methods for Entity Alignment: An Experimental
  Review

abstract:
  In recent years, we have witnessed the proliferation of knowledge graphs (KG)
in various domains, aiming to support applications like question answering,
recommendations, etc. A frequent task when integrating knowledge from different
KGs is to find which subgraphs refer to the same real-world entity. Recently,
embedding methods have been used for entity alignment tasks, that learn a
vector-space representation of entities which preserves their similarity in the
original KGs. A wide variety of supervised, unsupervised, and semi-supervised
methods have been proposed that exploit both factual (attribute based) and
structural information (relation based) of entities in the KGs. Still, a
quantitative assessment of their strengths and weaknesses in real-world KGs
according to different performance metrics and KG characteristics is missing
from the literature. In this work, we conduct the first meta-level analysis of
popular embedding methods for entity alignment, based on a statistically sound
methodology. Our analysis reveals statistically significant correlations of
different embedding methods with various meta-features extracted by KGs and
rank them in a statistically significant way according to their effectiveness
across all real-world KGs of our testbed. Finally, we study interesting
trade-offs in terms of methods' effectiveness and efficiency.


title:
Ranking of Communities in Multiplex Spatiotemporal Models of Brain
  Dynamics

abstract:
  As a relatively new field, network neuroscience has tended to focus on
aggregate behaviours of the brain averaged over many successive experiments or
over long recordings in order to construct robust brain models. These models
are limited in their ability to explain dynamic state changes in the brain
which occurs spontaneously as a result of normal brain function. Hidden Markov
Models (HMMs) trained on neuroimaging time series data have since arisen as a
method to produce dynamical models that are easy to train but can be difficult
to fully parametrise or analyse. We propose an interpretation of these neural
HMMs as multiplex brain state graph models we term Hidden Markov Graph Models
(HMGMs). This interpretation allows for dynamic brain activity to be analysed
using the full repertoire of network analysis techniques. Furthermore, we
propose a general method for selecting HMM hyperparameters in the absence of
external data, based on the principle of maximum entropy, and use this to
select the number of layers in the multiplex model. We produce a new tool for
determining important communities of brain regions using a spatiotemporal
random walk-based procedure that takes advantage of the underlying Markov
structure of the model. Our analysis of real multi-subject fMRI data provides
new results that corroborate the modular processing hypothesis of the brain at
rest as well as contributing new evidence of functional overlap between and
within dynamic brain state communities. Our analysis pipeline provides a way to
characterise dynamic network activity of the brain under novel behaviours or
conditions.


title: $Z_3$ Scalar Dark Matter with Strong Gamma-ray and Positron Fluxes

abstract:
  We explore a new class of simplified extensions to the Standard Model
containing a complex singlet scalar as a dark matter candidate accompanied by a
vector-like lepton as a mediator, both charged under a $Z_3$ symmetry. In its
simplest form, the new physics couples only to right-handed electrons, and the
model is able to accommodate the correct dark matter relic abundance around the
electroweak scale up to several TeV evading the strongest constraints from
perturbativity, collider and dark matter searches. Furthermore, the model is
capable to enhance naturally positron fluxes by several orders of magnitude
presenting box-shape spectra, and keeping sizable signals of gamma-rays. This
framework opens up a lot of phenomenological possibilities depending on the
quantum charge assignments of the new fields.


title: PanoFormer: Panorama Transformer for Indoor 360{\deg} Depth Estimation

abstract:
  Existing panoramic depth estimation methods based on convolutional neural
networks (CNNs) focus on removing panoramic distortions, failing to perceive
panoramic structures efficiently due to the fixed receptive field in CNNs. This
paper proposes the panorama transformer (named PanoFormer) to estimate the
depth in panorama images, with tangent patches from spherical domain, learnable
token flows, and panorama specific metrics. In particular, we divide patches on
the spherical tangent domain into tokens to reduce the negative effect of
panoramic distortions. Since the geometric structures are essential for depth
estimation, a self-attention module is redesigned with an additional learnable
token flow. In addition, considering the characteristic of the spherical
domain, we present two panorama-specific metrics to comprehensively evaluate
the panoramic depth estimation models' performance. Extensive experiments
demonstrate that our approach significantly outperforms the state-of-the-art
(SOTA) methods. Furthermore, the proposed method can be effectively extended to
solve semantic panorama segmentation, a similar pixel2pixel task. Code will be
available.


title: FUSED-PAGERANK: Loop-Fusion based Approximate PageRank

abstract:
  PageRank is a graph centrality metric that gives the importance of each node
in a given graph. The PageRank algorithm provides important insights to
understand the behavior of nodes through the connections they form with other
nodes. It is an iterative algorithm that ranks the nodes in each iteration
until all the node values converge. The PageRank algorithm is implemented using
sparse storage format, which results in irregular memory accesses in the code.
This key feature inhibits optimizations to improve its performance, and makes
optimizing the PageRank algorithm a non-trivial problem. In this work we
improve the performance of PageRank algorithm by reducing its irregular memory
accesses. In this paper, we propose FUSED-PAGERANK algorithm, a compiler
optimization oriented approximate technique that reduces the number of
irregular memory accesses in the PageRank algorithm, improving its locality
while making the convergence of the algorithm faster with better accuracy in
results. In particular, we propose an approximate PageRank algorithm using
Loop-Fusion. We believe that ours is the first work that formally applies
traditional compiler optimization techniques for irregular memory access in the
PageRank algorithm. We have verified our method by performing experiments on a
variety of datasets: LAW graphs, SNAP datasets and synthesized datasets. On
these benchmarks, we have achieved a maximum speedup (vs. -O3 optimization) of
2.05X, 2.23X, 1.74X with sequential version, and ~4.4X, ~2.61X, ~4.22X with
parallel version of FUSED-PAGERANK algorithm in comparison with Edge-centric
version of PageRank algorithm.


title: Diffeomorphism groups of convex polytopes

abstract:
  Let $E$ be a finite-dimensional real vector space and $M\subseteq E$ be a
convex polytope with non-empty interior. We turn the group of all
$C^\infty$-diffeomorphisms of $M$ into a regular Lie group.


title: How to Write Beautiful Process-and-Data-Science Papers?

abstract:
  After 25 years of PhD supervision, the author noted typical recurring
problems that make papers look sloppy, difficult to read, and incoherent. The
goal is not to write a paper for the sake of writing a paper, but to convey a
valuable message that is clear and precise. The goal is to write papers that
have an impact and are still understandable a couple of decades later. Our
mission should be to create papers of high quality that people want to read and
that can stand the test of time. We use Dijkstra's adagium "Beauty Is Our
Business" to stress the importance of simplicity, correctness, and cleanness.


title: HybridCap: Inertia-aid Monocular Capture of Challenging Human Motions

abstract:
  Monocular 3D motion capture (mocap) is beneficial to many applications. The
use of a single camera, however, often fails to handle occlusions of different
body parts and hence it is limited to capture relatively simple movements. We
present a light-weight, hybrid mocap technique called HybridCap that augments
the camera with only 4 Inertial Measurement Units (IMUs) in a
learning-and-optimization framework. We first employ a weakly-supervised and
hierarchical motion inference module based on cooperative Gated Recurrent Unit
(GRU) blocks that serve as limb, body and root trackers as well as an inverse
kinematics solver. Our network effectively narrows the search space of
plausible motions via coarse-to-fine pose estimation and manages to tackle
challenging movements with high efficiency. We further develop a hybrid
optimization scheme that combines inertial feedback and visual cues to improve
tracking accuracy. Extensive experiments on various datasets demonstrate
HybridCap can robustly handle challenging movements ranging from fitness
actions to Latin dance. It also achieves real-time performance up to 60 fps
with state-of-the-art accuracy.


title: Efficiently Enumerating Answers to Ontology-Mediated Queries

abstract:
  We study the enumeration of answers to ontology-mediated queries (OMQs) where
the ontology is a set of guarded TGDs or formulated in the description logic
ELI and the query is a conjunctive query (CQ). In addition to the traditional
notion of an answer, we propose and study two novel notions of partial answers
that can take into account nulls generated by existential quantifiers in the
ontology. Our main result is that enumeration of the traditional complete
answers and of both kinds of partial answers is possible with linear-time
preprocessing and constant delay for OMQs that are both acyclic and free-connex
acyclic. We also provide partially matching lower bounds. Similar results are
obtained for the related problems of testing a single answer in linear time and
of testing multiple answers in constant time after linear time preprocessing.
In both cases, the border between tractability and intractability is
characterized by similar, but slightly different acyclicity properties.


title:
PiDAn: A Coherence Optimization Approach for Backdoor Attack Detection
  and Mitigation in Deep Neural Networks

abstract:
  Backdoor attacks impose a new threat in Deep Neural Networks (DNNs), where a
backdoor is inserted into the neural network by poisoning the training dataset,
misclassifying inputs that contain the adversary trigger. The major challenge
for defending against these attacks is that only the attacker knows the secret
trigger and the target class. The problem is further exacerbated by the recent
introduction of "Hidden Triggers", where the triggers are carefully fused into
the input, bypassing detection by human inspection and causing backdoor
identification through anomaly detection to fail. To defend against such
imperceptible attacks, in this work we systematically analyze how
representations, i.e., the set of neuron activations for a given DNN when using
the training data as inputs, are affected by backdoor attacks. We propose
PiDAn, an algorithm based on coherence optimization purifying the poisoned
data. Our analysis shows that representations of poisoned data and authentic
data in the target class are still embedded in different linear subspaces,
which implies that they show different coherence with some latent spaces. Based
on this observation, the proposed PiDAn algorithm learns a sample-wise weight
vector to maximize the projected coherence of weighted samples, where we
demonstrate that the learned weight vector has a natural "grouping effect" and
is distinguishable between authentic data and poisoned data. This enables the
systematic detection and mitigation of backdoor attacks. Based on our
theoretical analysis and experimental results, we demonstrate the effectiveness
of PiDAn in defending against backdoor attacks that use different settings of
poisoned samples on GTSRB and ILSVRC2012 datasets. Our PiDAn algorithm can
detect more than 90% infected classes and identify 95% poisoned samples.


title: New estimates for some integrals of functions defined over primes

abstract:
  In this paper we give new estimates for integrals involving some arithmetic
functions defined over prime numbers. The main focus here is on the prime
counting function $\pi(x)$ and the Chebyshev $\vartheta$-function. Some of
these estimates depend on the correctness of the Riemann hypothesis on the
nontrivial zeros of the Riemann zeta function $\zeta(s)$.


title: Convergence of the free energy for spherical spin glasses

abstract:
  We prove that the free energy of any spherical mixed $p$-spin model converges
as the dimension $N$ tends to infinity. While the convergence is a consequence
of the Parisi formula, the proof we give is independent of the formula and uses
the well-known Guerra-Toninelli interpolation method. The latter was invented
for models with Ising spins to prove that the free energy is super-additive and
therefore (normalized by $N$) converges. In the spherical case, however, the
configuration space is not a product space and the interpolation cannot be
applied directly. We first relate the free energy on the sphere of dimension
$N+M$ to a free energy defined on the product of spheres in dimensions $N$ and
$M$ to which we then apply the interpolation method. This yields an approximate
super-additivity which is sufficient to prove the convergence.


title:
A domain specific language for data-centric infographics: technical
  report

abstract:
  The production process of data-centric infographics entails problems related
to a disconnection between the supporting software environments. We investigate
those problems and redesigns this process following the model-driven paradigm.
We have designed a domain-specific language to model infographics, and
implemented an interpreter that generates the infographics automatically. To
inform the engineering of the DSL and interpreter, we have analysed 58
infographics reporting on ethical, social and environmental accounting, and 10
infographic design tools. We then have tested our proposal by means of
modelling a sample of 10 infographics and generating them. We have also
conducted a controlled experiment to assess the extent to which the generated
infograhics are as visually attractive as the original ones and whether the
participants can identify whether the infographic they see is original or
generated. This report provides the details of this research project:
conceptual model, collected data, analysis, and supporting instruments.


title:
PreTR: Spatio-Temporal Non-Autoregressive Trajectory Prediction
  Transformer

abstract:
  Nowadays, our mobility systems are evolving into the era of intelligent
vehicles that aim to improve road safety. Due to their vulnerability,
pedestrians are the users who will benefit the most from these developments.
However, predicting their trajectory is one of the most challenging concerns.
Indeed, accurate prediction requires a good understanding of multi-agent
interactions that can be complex. Learning the underlying spatial and temporal
patterns caused by these interactions is even more of a competitive and open
problem that many researchers are tackling. In this paper, we introduce a model
called PRediction Transformer (PReTR) that extracts features from the
multi-agent scenes by employing a factorized spatio-temporal attention module.
It shows less computational needs than previously studied models with
empirically better results. Besides, previous works in motion prediction suffer
from the exposure bias problem caused by generating future sequences
conditioned on model prediction samples rather than ground-truth samples. In
order to go beyond the proposed solutions, we leverage encoder-decoder
Transformer networks for parallel decoding a set of learned object queries.
This non-autoregressive solution avoids the need for iterative conditioning and
arguably decreases training and testing computational time. We evaluate our
model on the ETH/UCY datasets, a publicly available benchmark for pedestrian
trajectory prediction. Finally, we justify our usage of the parallel decoding
technique by showing that the trajectory prediction task can be better solved
as a non-autoregressive task.


title:
A Differentiable Two-stage Alignment Scheme for Burst Image
  Reconstruction with Large Shift

abstract:
  Denoising and demosaicking are two essential steps to reconstruct a clean
full-color image from the raw data. Recently, joint denoising and demosaicking
(JDD) for burst images, namely JDD-B, has attracted much attention by using
multiple raw images captured in a short time to reconstruct a single
high-quality image. One key challenge of JDD-B lies in the robust alignment of
image frames. State-of-the-art alignment methods in feature domain cannot
effectively utilize the temporal information of burst images, where large
shifts commonly exist due to camera and object motion. In addition, the higher
resolution (e.g., 4K) of modern imaging devices results in larger displacement
between frames. To address these challenges, we design a differentiable
two-stage alignment scheme sequentially in patch and pixel level for effective
JDD-B. The input burst images are firstly aligned in the patch level by using a
differentiable progressive block matching method, which can estimate the offset
between distant frames with small computational cost. Then we perform implicit
pixel-wise alignment in full-resolution feature domain to refine the alignment
results. The two stages are jointly trained in an end-to-end manner. Extensive
experiments demonstrate the significant improvement of our method over existing
JDD-B methods. Codes are available at https://github.com/GuoShi28/2StageAlign.


title:
Assessing Progress of Parkinson s Disease Using Acoustic Analysis of
  Phonation

abstract:
  This paper deals with a complex acoustic analysis of phonation in patients
with Parkinson's disease (PD) with a special focus on estimation of disease
progress that is described by 7 different clinical scales ,e. g. Unified
Parkinson's disease rating scale or Beck depression inventory. The analysis is
based on parametrization of 5 Czech vowels pronounced by 84 PD patients. Using
classification and regression trees we estimated all clinical scores with
maximal error lower or equal to 13 %. Best estimation was observed in the case
of Mini-mental state examination (MAE = 0.77, estimation error 5.50 %. Finally,
we proposed a binary classification based on random forests that is able to
identify Parkinson's disease with sensitivity SEN = 92.86 % (SPE = 85.71 %).
The parametrization process was based on extraction of 107 speech features
quantifying different clinical signs of hypokinetic dysarthria present in PD.


title:
Field Calculus: quantum and statistical field theory without the Feynman
  diagrams

abstract:
  For a given base space $M$ (spacetime), we consider the Guichardet space over
the Guichardet space over $M$. Here we develop a "field calculus" based on the
Guichardet integral. This is the natural setting in which to describe Green
function relations for Boson systems. Here we can follow the suggestion of
Schwinger and develop a differential (local field) approach rather than the
integral one pioneered by Feynman. This is helped by a DEFG
(Dyson-Einstein-Feynman-Guichardet) shorthand which greatly simplifies
expressions. This gives a convenient framework for the formal approach of
Schwinger and Tomonaga as opposed to Feynman diagrams The Dyson-Schwinger is
recast in this language with the help of bosonic creation/annihilation
operators. We also give the combinatorial approach to tree-expansions.


title: Spin-orbit gravitational locking -- an effective potential approach

abstract:
  The objective of this paper is to study the tidally locked 3:2 spin-orbit
resonance of Mercury around the Sun. In order to achieve this goal, the
effective potential energy that determines the spinning motion of an
ellipsoidal planet around its axis is considered. By studying the rotational
potential energy of an ellipsoidal planet orbiting a spherical star on an
elliptic orbit with fixed eccentricity and semi-major axis, it is shown that
the system presents an infinite number of metastable equilibrium
configurations. These states correspond to local minima of the rotational
potential energy averaged over an orbit, where the ratio between the rotational
period of the planet around its axis and the revolution period around the star
is fixed. The configurations in which this ratio is an integer or an half
integer are of particular interest. Among these configurations, the deepest
minimum in the average potential energy corresponds to a situation where the
rotational and orbital motion of the planet are synchronous, and the system is
tidally locked. The next-to-the deepest minimum corresponds to the case in
which the planet rotates three times around its axis in the time that it needs
to complete two orbits around the Sun. The latter is indeed the case that
describes Mercury's motion. The method discussed in this work allows one to
identify the integer and half-integer ratios that correspond to spin-orbit
resonances and to describe the motion of the planet in the resonant orbit.


title: Weak error rates of numerical schemes for rough volatility

abstract:
  Simulation of rough volatility models involves discretization of stochastic
integrals where the integrand is a function of a (correlated) fractional
Brownian motion of Hurst index $H \in (0,1/2)$. We obtain results on the rate
of convergence for the weak error of such approximations, in the special cases
when either the integrand is the fBm itself, or the test function is cubic. Our
result states that the convergence is of order $(3H+ \frac{1}{2}) \wedge 1$ for
the Cholesky scheme, and of order $H+\frac{1}{2}$ for the hybrid scheme with
well-chosen weights.


title: Proceedings Fifth Workshop on Models for Formal Analysis of Real Systems

abstract:
  This volume contains the proceedings of MARS 2022, the fifth workshop on
Models for Formal Analysis of Real Systems, held as part of ETAPS 2022, the
European Joint Conferences on Theory and Practice of Software. The MARS
workshops bring together researchers from different communities who are
developing formal models of real systems in areas where complex models occur,
such as networks, cyber-physical systems, hardware/software co-design, biology,
etc. The motivation and aim for MARS stem from the following two observations:
  * Large case studies are essential to show that specification formalisms and
modelling techniques are applicable to real systems, whereas many research
papers only consider toy examples or tiny case studies.
  * Developing an accurate model of a real system takes a large amount of time,
often months or years. In most scientific papers, however, salient details of
the model need to be skipped due to lack of space, and to leave room for formal
verification methodologies and results.
  The MARS workshops aim at remedying these issues, emphasising modelling over
verification, so as to retain lessons learnt from formal modelling, which are
not usually discussed elsewhere.


title:
Core structures of vortices in Ginzburg-Landau theory for neutron
  $^3P_2$ superfluids

abstract:
  We investigate vortex solutions in the Ginzburg-Landau theory for neutron
$^3P_2$ superfluids relevant for neutron star cores in which neutron pairs
possess the total angular momentum $J=2$ with spin-triplet and $P$ wave, in the
presence of the magnetic field parallel to the angular momentum of vortices.
The ground state is known to be in the uniaxial nematic (UN) phase in the
absence of magnetic field, while it is in the $D_2$ ($D_4$) biaxial nematic
(BN) phase in the presence of the magnetic field below (above) the critical
value. We find that a singly quantized vortex always splits into two
half-quantized non-Abelian vortices connected by soliton(s) as a vortex
molecule with any strength of the magnetic field. In the UN phase, two
half-quantized vortices with ferromagnetic cores are connected by a linear
soliton with the $D_4$ BN order. In the $D_2$ ($D_4$) BN phase, two
half-quantized vortices with cyclic cores are connected by three linear
solitons with the $D_4$ ($D_2$) BN order. The energy of the vortex molecule
monotonically increases and the distance between the two half-quantized
vortices decreases with the magnetic field increases, except for a
discontinuously increasing jump of the distance at the critical magnetic field.
We also construct an isolated half-quantized non-Abelian vortex in the $D_4$ BN
phase.


title: One-Shot Adaptation of GAN in Just One CLIP

abstract:
  There are many recent research efforts to fine-tune a pre-trained generator
with a few target images to generate images of a novel domain. Unfortunately,
these methods often suffer from overfitting or under-fitting when fine-tuned
with a single target image. To address this, here we present a novel
single-shot GAN adaptation method through unified CLIP space manipulations.
Specifically, our model employs a two-step training strategy: reference image
search in the source generator using a CLIP-guided latent optimization,
followed by generator fine-tuning with a novel loss function that imposes CLIP
space consistency between the source and adapted generators. To further improve
the adapted model to produce spatially consistent samples with respect to the
source generator, we also propose contrastive regularization for patchwise
relationships in the CLIP space. Experimental results show that our model
generates diverse outputs with the target texture and outperforms the baseline
models both qualitatively and quantitatively. Furthermore, we show that our
CLIP space manipulation strategy allows more effective attribute editing.


title: Change of Basis from Bernstein to Zernike

abstract:
  We increase the scope of previous work on change of basis between finite
bases of polynomials by defining ascending and descending bases and introducing
three techniques for defining them from known ones.
  The minimum degrees of polynomials in an ascending basis can increase such as
with bases of Bernstein and Zernike Radial polynomials. They have applications
in computer-aided design and optics.
  We give coefficient functions for mappings from the monomials to descending
bases of Bernstein polynomials, and ascending ones of Zernike Radial
polynomials and prove their correctness.
  Allowing for parity, we define eight general change of basis matrices and the
related equations for composing their coefficient functions.
  A main example is the change of basis from shifted Legendre polynomials to
Bernstein polynomials considered by R Farouki [7]. The analysis enables us to
find a more general hypergeometric function for the coefficient function, and
recurrences for finding coefficient functions by matrix row and column. We show
the column coefficient functions are equivalent to the Lagrange interpolation
polynomials of their elements. We also provide a solution to an open problem
[7] by showing there is no general closed form expression for the coefficient
function from Gosper's Algorithm.
  Truncation, alternation and superposition increase the scope further.
Alternation enables, for example, a change of basis between Bernstein and
Zernike Radial polynomials. A summary shows that the groupoid of change of
basis matrices is a small category, and triangular and alternating change of
basis matrices are morphisms in full subcategories of it. Truncation is a
covariant functor.


title: Video Prediction at Multiple Scales with Hierarchical Recurrent Networks

abstract:
  Autonomous systems not only need to understand their current environment, but
should also be able to predict future actions conditioned on past states, for
instance based on captured camera frames. For certain tasks, detailed
predictions such as future video frames are required in the near future,
whereas for others it is beneficial to also predict more abstract
representations for longer time horizons. However, existing video prediction
models mainly focus on forecasting detailed possible outcomes for short
time-horizons, hence being of limited use for robot perception and spatial
reasoning. We propose Multi-Scale Hierarchical Prediction (MSPred), a novel
video prediction model able to forecast future possible outcomes of different
levels of granularity at different time-scales simultaneously. By combining
spatial and temporal downsampling, MSPred is able to efficiently predict
abstract representations such as human poses or object locations over long time
horizons, while still maintaining a competitive performance for video frame
prediction. In our experiments, we demonstrate that our proposed model
accurately predicts future video frames as well as other representations (e.g.
keypoints or positions) on various scenarios, including bin-picking scenes or
action recognition datasets, consistently outperforming popular approaches for
video frame prediction. Furthermore, we conduct an ablation study to
investigate the importance of the different modules and design choices in
MSPred. In the spirit of reproducible research, we open-source VP-Suite, a
general framework for deep-learning-based video prediction, as well as
pretrained models to reproduce our results.


title:
Differential geometric global smoothings of simple normal crossing
  complex surfaces with trivial canonical bundle

abstract:
  Let $X$ be a simple normal crossing compact complex surface with trivial
canonical bundle which includes at most triple intersections. We prove that if
$X$ is $d$-semistable, then there exists a family of smoothings in a
differential geometric sense. This extends the smoothability result of R.
Friedman to cases where not all irreducible components of $X$ are K\"{a}hlerian
or $H^1(X, \mathcal{O}_X)$ does not vanish. The proof is based on an explicit
construction of local smoothings around the singular locus of $X$, and the
first author's existence result of holomorphic volume forms on global
smoothings of $X$. In particular, these volume forms are given as solutions of
a nonlinear elliptic partial differential equation.
  As an application, we provide several examples of $d$-semistable simple
normal crossing complex surfaces with trivial bundle including double curves,
which are smoothable to complex tori, primary Kodaira surfaces and $K3$
surfaces. We also provide several examples of such complex surfaces including
triple points, which are smoothable to $K3$ surfaces.


title: Conjugate heat transfer effects on flow boiling in microchannels

abstract:
  This article presents a computational study of saturated flow boiling in
non-circular microchannels. The unit channel of a multi-microchannel
evaporator, consisting of the fluidic channel and surrounding evaporator walls,
is emulated and the conjugate heat transfer problem is solved. Simulations are
performed using OpenFOAM v2106 and the built-in geometric Volume Of Fluid
method, augmented with self-developed libraries to include liquid-vapour
phase-change and improve the surface tension force calculation. A systematic
study is conducted by employing water at atmospheric pressure, a channel
hydraulic diameter of Dh = 229 um, a uniform base heat flux of qb = 100kW/m2,
and by varying the channel width-to-height aspect-ratio and channel fin
thickness in the range AR = 0.25-4 and Wf = Dh/8-Dh, respectively. The effects
of conjugate heat transfer and channel aspect-ratio on the bubble and
evaporative film dynamics, heat transfer, and evaporator temperature are
investigated in detail. This study reveals that, when the flow is single-phase,
higher Nusselt numbers and lower evaporator temperatures are achieved for AR <
1. In the two-phase flow regime, the trends of the Nusselt number versus the
aspect-ratio are mixed, although for smaller channel fins an ascending trend of
Nu for increasing aspect-ratios is apparent. Nonetheless, due to conjugate heat
transfer, Nusselt numbers and evaporator base temperatures follow different
trends when varying the aspect-ratio, and channels with AR < 1 seem to promote
lower evaporator temperatures than higher aspect-ratio conduits, despite
exhibiting slightly worse two-phase convective heat transfer performances.


title: Finding Structural Knowledge in Multimodal-BERT

abstract:
  In this work, we investigate the knowledge learned in the embeddings of
multimodal-BERT models. More specifically, we probe their capabilities of
storing the grammatical structure of linguistic data and the structure learned
over objects in visual data. To reach that goal, we first make the inherent
structure of language and visuals explicit by a dependency parse of the
sentences that describe the image and by the dependencies between the object
regions in the image, respectively. We call this explicit visual structure the
\textit{scene tree}, that is based on the dependency tree of the language
description. Extensive probing experiments show that the multimodal-BERT models
do not encode these scene trees.Code available at
\url{https://github.com/VSJMilewski/multimodal-probes}.


title: Fractional Analytic QCD beyond Leading Order

abstract:
  Fractional analytic QCD is constructed beyond leading order. We present four
different views, details of their production, and show their applicability.


title: Few-Shot Learning on Graphs: A Survey

abstract:
  Graph representation learning has attracted tremendous attention due to its
remarkable performance in many real-world applications. However, prevailing
(semi-)supervised graph representation learning models for specific tasks often
suffer from label sparsity issue as data labeling is always time and resource
consuming. In light of this, few-shot learning on graphs (FSLG), which combines
the strengths of graph representation learning and few-shot learning together,
has been proposed to tackle the performance degradation in face of limited
annotated data challenge. There have been many studies working on FSLG
recently. In this paper, we comprehensively survey these work in the form of a
series of methods and applications. Specifically, we first introduce FSLG
challenges and bases, then categorize and summarize existing work of FSLG in
terms of three major graph mining tasks at different granularity levels, i.e.,
node, edge, and graph. Finally, we share our thoughts on some future research
directions of FSLG. The authors of this survey have contributed significantly
to the AI literature on FSLG over the last few years.


title:
Comparing Lensing and Stellar Orbital Models of a Nearby Massive
  Strong-Lens Galaxy

abstract:
  Exploiting the relative proximity of the nearby strong-lens galaxy SNL-1, we
present a critical comparison of the mass estimates derived from independent
modelling techniques. We fit triaxial orbit-superposition dynamical models to
spatially-resolved stellar kinematics, and compare to the constraints derived
from lens modelling of high-resolution photometry. From the dynamical model, we
measure the total (dynamical) mass enclosed within a projected aperture of
radius the Einstein radius to be $\log_{10} M_{\mathrm{Ein.}} = 11.00 \pm
0.02$, which agrees with previous measurements from lens modelling to within
$5\%$. We then explore the intrinsic (de-projected) properties of the
best-fitting dynamical model. We find that SNL-1 has approximately-constant,
intermediate triaxiality at all radii. It is oblate-like in the inner regions
(around the Einstein radius) and tends towards spherical at larger radii. The
stellar velocity ellipsoid gradually transforms from isotropic in the very
central regions to radially-biased in the outskirts. We find that SNL-1 is
dynamically consistent with the broader galaxy population, as measured by the
relative fraction of orbit `temperatures' compared to the CALIFA survey. On the
mass--size plane, SNL-1 occupies the most-compact edge given its mass, compared
to both the MaNGA and SAMI surveys. Finally, we explore how the observed
lensing configuration is affected by the orientation of the lens galaxy. We
discuss the implications of such detailed models on future combined lensing and
dynamical analyses.


title:
In situ characterization of vacancy ordering in Ge-Sb-Te phase-change
  memory alloys

abstract:
  Tailoring the degree of structural disorder in Ge-Sb-Te alloys is important
for the development of non-volatile phase-change memory and neuro-inspired
computing. Upon crystallization from the amorphous phase, these alloys form a
cubic rocksalt-like structure with a high content of intrinsic vacancies.
Further thermal annealing results in a gradual structural transition towards a
layered structure and an insulator-to-metal transition. In this work, we
elucidate the atomic-level details of the structural transition in crystalline
GeSb2Te4 by in situ high-resolution transmission electron microscopy (HRTEM)
experiments and ab initio density functional theory (DFT) calculations,
providing a comprehensive real-time and real-space view of the vacancy ordering
process. We also discuss the impact of vacancy ordering on altering the
electronic and optical properties of GeSb2Te4, which is relevant to multilevel
storage applications. The phase evolution paths in Ge-Sb-Te alloys are
illustrated using a summary diagram, which serves as a guide for designing
phase-change memory devices.


title: A recursive function coding number theoretic functions

abstract:
  We show that there exists a fixed recursive function $e$ such that for all
functions $h\colon \mathbb{N}\to \mathbb{N}$, there exists an injective
function $c_h\colon \mathbb{N}\to \mathbb{N}$ such that $c_h(h(n))=e(c_h(n))$,
i.e., $h=c_h^{-1}ec_h$.


title:
Certifiably Optimal Mutual Localization with Anonymous Bearing
  Measurements

abstract:
  Mutual localization is essential for coordination and cooperation in
multi-robot systems. Previous works have tackled this problem by assuming
available correspondences between measurements and received odometry
estimations, which are difficult to acquire, especially for unified robot
teams. Furthermore, most local optimization methods ask for initial guesses and
are sensitive to their quality. In this paper, we present a certifiably optimal
algorithm that uses only anonymous bearing measurements to formulate a novel
mixed-integer quadratically constrained quadratic problem (MIQCQP). Then, we
relax the original nonconvex problem into a semidefinite programming (SDP)
problem and obtain a certifiably global optimum using with off-the-shelf
solvers. As a result, our method can determine bearing-pose correspondences and
furthermore recover the initial relative poses between robots under a certain
condition. We compare the performance with local optimization methods on
extensive simulations under different noise levels to show our advantage in
global optimality and robustness. Real-world experiments are conducted to show
the practicality and robustness.


title:
EVA2.0: Investigating Open-Domain Chinese Dialogue Systems with
  Large-Scale Pre-Training

abstract:
  Large-scale pre-training has shown remarkable performance in building
open-domain dialogue systems. However, previous works mainly focus on showing
and evaluating the conversational performance of the released dialogue model,
ignoring the discussion of some key factors towards a powerful human-like
chatbot, especially in Chinese scenarios. In this paper, we conduct extensive
experiments to investigate these under-explored factors, including data quality
control, model architecture designs, training approaches, and decoding
strategies. We propose EVA2.0, a large-scale pre-trained open-domain Chinese
dialogue model with 2.8 billion parameters, and make our models and code
publicly available. To our knowledge, EVA2.0 is the largest open-source Chinese
dialogue model. Automatic and human evaluations show that our model
significantly outperforms other open-source counterparts. We also discuss the
limitations of this work by presenting some failure cases and pose some future
directions.


title:
The Sparse Grids Matlab kit -- a Matlab implementation of sparse grids
  for high-dimensional function approximation and uncertainty quantification

abstract:
  The Sparse Grids Matlab kit is a collection of Matlab functions for
high-dimensional interpolation and quadrature, based on the combination
technique form of sparse grids. It is lightweight, high-level and easy to use,
good for quick prototyping and teaching. It is somehow geared towards
Uncertainty Quantification (UQ), but it is flexible enough for other purposes.
The goal of this paper is to give an overview of the implementation structure
of the Sparse Grids Matlab kit and to showcase its potentialities, guided by
some illustrative tests and a final comprehensive example on forward and
inverse UQ analysis.


title:
Heuristics and Uncertainty Quantification in Rational and Inverse
  Compound and Catalyst Design

abstract:
  The goal of inverse (quantum) approaches is to devise methods and approaches
capable of efficiently searching chemical space in such a way that the design
of novel materials and compounds with specific properties is as direct and
efficient as possible. Here, we review the current state of the field with a
focus on the most recent developments. We discuss the importance of heuristic
rules and human intuition for rational compound design. Moreover, we elaborate
on options for reliable uncertainty quantification for computational results,
which is crucial for a truly predictive application of any in silico method.


title:
The mutually normalizing regular subgroups of the holomorph of a cyclic
  group of prime power order

abstract:
  Let $G=C_{p^n}$ be a finite cyclic p-group, and let $Hol(G)$ denote its
holomorph. In this work, we find and characterize the regular subgroups of
$Hol(G)$ that are mutually normalizing each other in the permutation group
$Sym(G)$. We represent such regular subgroups as vertices of a graph, and we
connect a pair of them by an edge when they mutually normalize each other. The
approach to construct this local normalizing graph relies on the theory of
gamma functions, and the final result will contain all the information about
the regular subgroups of $Hol(G)$ in a compact form.


title: Quasi-static crack front deformations in cohesive materials

abstract:
  When a crack interacts with material heterogeneities, its front distorts and
adopts complex tortuous configurations that are reminiscent of the energy
barriers encountered during crack propagation. As such, the study of crack
front deformations is key to rationalize the effective failure properties of
micro-structured solids and interfaces. Yet, the impact of a localized
dissipation in a finite region behind the crack front, called the process zone,
has often been overlooked. In this work, we derive the equation ruling 3D
coplanar crack propagation in heterogeneous cohesive materials where the
opening of the crack is resisted by some traction in its wake. We show that the
presence of a process zone results in two competing effects on the deformation
of crack fronts: (i) it makes the front more compliant to small-wavelength
perturbations, and (ii) it smooths out local fluctuations of strength and
process zone size, from which emerge heterogeneities of fracture energy. Their
respective influence on front deformations is shown to strongly impact the
stability of perturbed crack fronts, as well as their stationary shapes when
interacting with arrays of tough obstacles. Overall, our theory provides a
unified framework to predict the variety of front profiles observed in
experiments, even when the small-scale yielding hypothesis of linear elastic
fracture mechanics breaks down.


title: A New Analytical Approximation of the Fluid Antenna System Channel

abstract:
  Fluid antenna systems (FAS) are an emerging technology that promises a
significant diversity gain even in the smallest spaces. Motivated by the
groundbreaking potentials of liquid antennas, researchers in the wireless
communication community are investigating a novel antenna system where a single
antenna can freely switch positions along a small linear space to pick the
strongest received signal. However, the FAS positions do not necessarily follow
the ever-existing rule separating them by at least half the radiation
wavelength. Previous work in the literature parameterized the channels of the
FAS ports simply enough to provide a single-integral expression of the
probability of outage and various insights on the achievable performance.
Nevertheless, this channel model may not accurately capture the correlation
between the ports, given by Jake's model. This work builds on the
state-of-the-art and accurately approximates the FAS channel while maintaining
analytical tractability. The approximation is performed in two stages. The
first stage approximation considerably reduces the number of multi-fold
integrals in the probability of outage expression, while the second stage
approximation provides a single integral representation of the FAS probability
of outage. Further, the performance of such innovative technology is
investigated under a less-idealized correlation model. Numerical results
validate our approximations of the FAS channel model and demonstrate a limited
performance gain under realistic assumptions. Further, our work opens the door
for future research to investigate scenarios in which the FAS provides a
performance gain compared to the current multiple antennas solutions.


title: Rational tangle replacements and knot Floer homology

abstract:
  From the link Floer complex of a link $K$, we extract a lower bound $t_q'(K)$
for the rational unknotting number of $K$ (i.e. the minimum number of rational
replacements required to unknot $K$). Moreover, we show that the torsion
obstruction $t_q(K)=\hat{t}(K)$ from an earlier paper of Alishahi and the
author is a lower bound for the proper rational unknotting number. Moreover,
$t_q(K\#K')=\max\{t_q(K),t_q(K')\}$ and $t'_q(K\#K')=\max\{t'_q(K),t'_q(K')\}$.
For the torus knot $K=T_{p,pk+1}$ we compute $t'_q(K)=\lfloor p/2\rfloor$ and
$t_q(K)=p-1$.


title:
Self-aligned patterning technique for fabricating high-performance
  diamond sensor arrays with nanoscale precision

abstract:
  To efficiently align the creation of defect center with photonics structure
in nanoscale precision is one of the outstanding challenges for realizing
high-performance photonic devices and the application in quantum technology
such as quantum sensing, scalable quantum systems, and quantum computing
network. Here, we propose a facile self-aligned patterning technique wholly
based on conventional engineering technology, with the doping precision can
reach ~15nm. Specifically, we demonstrate this technique by fabricating diamond
nanopillar sensor arrays, which show high consistency and near-optimal photon
counts, high yield approaching the theoretical limit, and high filtering
efficiency for different NV centers. Combined with appropriate crystal
orientation, a saturated fluorescence rate of 4.65 Mcps and the best reported
fluorescence-dependent detection sensitivity of 1900 cps^(-1/2) are achieved.
This technique applicable to all similar solid-state systems should facilitate
the development of parallel quantum sensing and scalable information
processing.


title:
Non-commutative propositional logic with short-circuited biconditional
  and NAND

abstract:
  Short-circuit evaluation denotes the semantics of propositional connectives
in which the second argument is evaluated only if the first argument does not
suffice to determine the value of the expression. In programming, short-circuit
evaluation is widely used, with left-sequential conjunction and disjunction as
primitive connectives. We consider left-sequential, non-commutative
propositional logic, also known as MSCL (memorising short-circuit logic), and
start from a previously published, equational axiomatisation. First, we extend
this logic with a left-sequential version of the biconditional connective,
which allows for an elegant axiomatisation of MSCL. Next, we consider a
left-sequential version of the NAND operator (the Sheffer stroke) and again
give a complete, equational axiomatisation of the corresponding variant of
MSCL. Finally, we consider these logical systems in a three-valued setting with
a constant for `undefined', and again provide completeness results.


title: Towards a global map of the artificial all-sky brightness

abstract:
  Modeling the hemispherical night sky brightness of anthropogenic origin is a
demanding computational challenge, due to the intensive calculations required
to produce all-sky maps with fine angular resolution including high-order
scattering effects. We present in this Letter a physically consistent,
semi-analytic two-parameter model of the all-sky radiance produced by an
artificial light source that encodes efficiently the spectral radiance in all
directions of the sky above the observer. The two parameters of this function
are derived from the state of the atmosphere, the distance to the observer, and
the source's angular and spectral emission pattern. The anthropogenic all-sky
radiance at any place on Earth can be easily calculated by adding up the
contributions of the surrounding artificial sources, using the information
available from nighttime satellite imagery and ground-truth lighting
inventories. This opens the way for the elaboration of a global world map of
the artificial all-sky brightness.


title: Covering rectangles by few monotonous polyominoes

abstract:
  A monotonous polyomino is formed by all lattice unit squares met by the graph
of some fixed monotonous continuous function $f:[a,b] \to \mathbb{R}$ with
$f(k) \notin \mathbb{Z}$ whenever $k \in \mathbb{Z}$. Our main result says that
the least cardinality of a covering of a lattice $(m \times n)$-rectangle by
monotonous polyominoes is $\left\lceil
\frac{2}{3}\left(m+n-\sqrt{m^2+n^2-mn}\right)\right\rceil$. The paper is
motivated by a problem on arrangements of straight lines on chessboards.


title: Localizing Visual Sounds the Easy Way

abstract:
  Unsupervised audio-visual source localization aims at localizing visible
sound sources in a video without relying on ground-truth localization for
training. Previous works often seek high audio-visual similarities for likely
positive (sounding) regions and low similarities for likely negative regions.
However, accurately distinguishing between sounding and non-sounding regions is
challenging without manual annotations. In this work, we propose a simple yet
effective approach for Easy Visual Sound Localization, namely EZ-VSL, without
relying on the construction of positive and/or negative regions during
training. Instead, we align audio and visual spaces by seeking audio-visual
representations that are aligned in, at least, one location of the associated
image, while not matching other images, at any location. We also introduce a
novel object guided localization scheme at inference time for improved
precision. Our simple and effective framework achieves state-of-the-art
performance on two popular benchmarks, Flickr SoundNet and VGG-Sound Source. In
particular, we improve the CIoU of the Flickr SoundNet test set from 76.80% to
83.94%, and on the VGG-Sound Source dataset from 34.60% to 38.85%. The code is
available at https://github.com/stoneMo/EZ-VSL.


title:
Application of Data Collected by Endpoint Detection and Response Systems
  for Implementation of a Network Security System based on Zero Trust
  Principles and the EigenTrust Algorithm

abstract:
  Traditionally, security systems for enterprises have implicit access based on
strong cryptography, authentication and key sharing, wherein access control is
based on Role Based Access Control (RBAC), in which roles such as manager,
accountant and so on provide a way of deciding a subject's authority. However,
years of post-attack analysis on enterprise networks has shown that a majority
of times, security breaches occur intentionally or accidently due to implicitly
trusted people of an enterprise itself. Zero Trust Architecture works on the
principle of never granting trust implicitly, but rather continuously
evaluating the trust parameters for each resource access request and has a
strict, but not rigid, set of protocols for access control of a subject to
resources. Endpoint Detection and Response (EDR) systems are tools that collect
a large number of attributes in and around machines within an enterprise
network to have close visibility into sophisticated intrusion. In our work, we
seek to deploy EDR systems and build trust algorithms using tactical provenance
analysis, threshold cryptography and reputation management to continuously
record data, evaluate trust of a subject, and simultaneously analyze them
against a database of known threat vectors to provide conditional access
control. However, EDR tools generate a high volume of data that leads to false
alarms, misdetections and correspondingly a high backlog of tasks that makes it
infeasible, which is addressed using tactical provenance analysis and
information theory.


title: Combining Static and Contextualised Multilingual Embeddings

abstract:
  Static and contextual multilingual embeddings have complementary strengths.
Static embeddings, while less expressive than contextual language models, can
be more straightforwardly aligned across multiple languages. We combine the
strengths of static and contextual models to improve multilingual
representations. We extract static embeddings for 40 languages from XLM-R,
validate those embeddings with cross-lingual word retrieval, and then align
them using VecMap. This results in high-quality, highly multilingual static
embeddings. Then we apply a novel continued pre-training approach to XLM-R,
leveraging the high quality alignment of our static embeddings to better align
the representation space of XLM-R. We show positive results for multiple
complex semantic tasks. We release the static embeddings and the continued
pre-training code. Unlike most previous work, our continued pre-training
approach does not require parallel text.


title:
A Pricing Mechanism for Balancing the Charging of Ride-Hailing Electric
  Vehicle Fleets

abstract:
  Both ride-hailing services and electric vehicles are becoming increasingly
popular and it is likely that charging management of the ride-hailing vehicles
will be a significant part of the ride-hailing company's operation in the near
future. Motivated by this, we propose a game theoretic model for charging
management, where we assume that it is the fleet-operator that wants to
minimize its operational cost, which among others include the price of
charging. To avoid overcrowded charging stations, a central authority will
design pricing policies to incentivize the vehicles to spread out among the
charging stations, in a setting where several ride-hailing companies compete
about the resources. We show that it is possible to construct pricing policies
that make the Nash-equilibrium between the companies follow the central
authority's target value when the desired load is feasible. Moreover, we
provide a decentralized algorithm for computation of the equilibrium and
conclude the paper with a numerical example illustrating the results.


title:
Piezo-optomechanical signal transduction using Lamb wave supermodes in a
  suspended Gallium Arsenide photonic integrated circuits platform

abstract:
  Piezoelectric optomechanical platforms present one of the most promising
routes towards efficient transduction of signals from the microwave to the
optical frequency domains. New device architectures need to be developed in
order to achieve the stringent requirements for building efficient quantum
transducers. In this work, we utilize the mechanical supermode principle to
improve the overall microwave to optical transduction efficiency, by
fabricating Lamb wave resonators that are hybridized with the mechanical
breathing modes of a rib waveguide in a suspended gallium arsenide (GaAs)
photonic integrated circuits (PIC) platform. Combining the strong elasto-optic
interactions available in GaAs with the increased phonon injection efficiency
enabled by this architecture, we demonstrate signal transduction up to 7 GHz,
and an increase in transduction efficiency by $\approx$ 25$\times$ for the
hybridized mode ($f_m\approx$ 2 GHz), using this approach. We also outline
routes for improving device performance to enable quantum transduction within
this platform.


title:
Crucial role of out-of-plane Sb-$p$ orbitals in Van Hove singularity
  formation and electronic correlation for superconducting Kagome metal
  CsV$_3$Sb$_5$

abstract:
  First-principles density functional theory calculations are performed to
understand the electronic structure and interaction parameters for recently
discovered superconducting Kagome metal CsV$_3$Sb$_5$. A systematic analysis of
the tight-binding parameters based on maximally localized Wannier function
method demonstrates that the out-of-plane Sb$^{\rm out}$-$p$ orbital is a key
element in complete description of the three Van Hove singularity structures
known in this material at $M$ point near the Fermi level. Further, the
correlation strengths are also largely determined by Sb$^{\rm out}$-$p$ states.
Based on constrained random phase approximation, we find that on-site and
inter-site interaction parameter are both significantly affected by the
screening effect of Sb$^{\rm out}$-$p$ orbitals. As the role of this previously
unnoticed orbital state can be tuned or controlled by out-of-plane lattice
parameters, we examine the electronic structure and particularly the evolution
of Van Hove singularity points as a function of strain and pressure, which can
serve as useful knobs to control the material properties.


title:
Fighting Noise with Noise: Causal Inference with Many Candidate
  Instruments

abstract:
  Instrumental variable methods provide useful tools for inferring causal
effects in the presence of unmeasured confounding. To apply these methods with
large-scale data sets, a major challenge is to find valid instruments from a
possibly large candidate set. In practice, most of the candidate instruments
are often not relevant for studying a particular exposure of interest.
Moreover, not all relevant candidate instruments are valid as they may directly
influence the outcome of interest. In this article, we propose a data-driven
method for causal inference with many candidate instruments that addresses
these two challenges simultaneously. A key component of our proposal is a novel
resampling method that constructs pseudo variables to remove irrelevant
candidate instruments having spurious correlations with the exposure.
Theoretical and synthetic data analyses show that the proposed method performs
favourably compared to existing methods. We apply our method to a Mendelian
randomization study estimating the effect of obesity on health-related quality
of life.


title:
Effects of annealing atmosphere on defect-controlled ferromagnetism in
  Mn-doped belta-Gallium oxide

abstract:
  Mn-doped belta-gallium oxide (GMO) films with room-temperature ferromagnetism
are synthesized on c-plane sapphire substrates by polymer-assisted deposition
and the effects of annealing atmospheres (air and pure O2 gas) on their
structures and physical properties are investigated. The characterizations
reveal that the concentrations of oxygen vacancies (VOs) and various Mn (Mn2+,
Mn3+ and Mn4+) ions in the GMO films are modulated by the annealing
atmospheres. The inter-planar spacing of the samples annealed in air (GMO-air)
with more Mn2+ ions is larger than that of the samples annealed in pure O2 gas
(GMO-O2) with more Mn4+ ions. Notably, GMO-air exhibits a saturation
magnetization value as strong as 1.7 times that of GMO-O2, owing to the
enhanced coupling between VOs and Mn ions as well as the spin polarization
contribution from more 3d electrons in GMO-air. Besides, the optical properties
and electrical resistivity of the GMO films are also tuned by the annealing
atmospheres. Our results reveal the important effect of annealing atmospheres
on the structural and magnetic properties of the GMO films and provide an
alternative way for spintronic engineering of related materials.


title:
Machine Learning for Encrypted Malicious Traffic Detection: Approaches,
  Datasets and Comparative Study

abstract:
  As people's demand for personal privacy and data security becomes a priority,
encrypted traffic has become mainstream in the cyber world. However, traffic
encryption is also shielding malicious and illegal traffic introduced by
adversaries, from being detected. This is especially so in the post-COVID-19
environment where malicious traffic encryption is growing rapidly. Common
security solutions that rely on plain payload content analysis such as deep
packet inspection are rendered useless. Thus, machine learning based approaches
have become an important direction for encrypted malicious traffic detection.
In this paper, we formulate a universal framework of machine learning based
encrypted malicious traffic detection techniques and provided a systematic
review. Furthermore, current research adopts different datasets to train their
models due to the lack of well-recognized datasets and feature sets. As a
result, their model performance cannot be compared and analyzed reliably.
Therefore, in this paper, we analyse, process and combine datasets from 5
different sources to generate a comprehensive and fair dataset to aid future
research in this field. On this basis, we also implement and compare 10
encrypted malicious traffic detection algorithms. We then discuss challenges
and propose future directions of research.


title: Modulated Contrast for Versatile Image Synthesis

abstract:
  Perceiving the similarity between images has been a long-standing and
fundamental problem underlying various visual generation tasks. Predominant
approaches measure the inter-image distance by computing pointwise absolute
deviations, which tends to estimate the median of instance distributions and
leads to blurs and artifacts in the generated images. This paper presents
MoNCE, a versatile metric that introduces image contrast to learn a calibrated
metric for the perception of multifaceted inter-image distances. Unlike vanilla
contrast which indiscriminately pushes negative samples from the anchor
regardless of their similarity, we propose to re-weight the pushing force of
negative samples adaptively according to their similarity to the anchor, which
facilitates the contrastive learning from informative negative samples. Since
multiple patch-level contrastive objectives are involved in image distance
measurement, we introduce optimal transport in MoNCE to modulate the pushing
force of negative samples collaboratively across multiple contrastive
objectives. Extensive experiments over multiple image translation tasks show
that the proposed MoNCE outperforms various prevailing metrics substantially.
The code is available at https://github.com/fnzhan/MoNCE.


title: Stronger 3SUM-Indexing Lower Bounds

abstract:
  The $3$SUM-Indexing problem was introduced as a data structure version of the
$3$SUM problem, with the goal of proving strong conditional lower bounds for
static data structures via reductions. Ideally, the conjectured hardness of
$3$SUM-Indexing should be replaced by an unconditional lower bound.
Unfortunately, we are far from proving this, with the strongest current lower
bound being a logarithmic query time lower bound by Golovnev et al. from
STOC'20. Moreover, their lower bound holds only for non-adaptive data
structures and they explicitly asked for a lower bound for adaptive data
structures. Our main contribution is precisely such a lower bound against
adaptive data structures. As a secondary result, we also strengthen the
non-adaptive lower bound of Golovnev et al. and prove strong lower bounds for
$2$-bit-probe non-adaptive $3$SUM-Indexing data structures via a completely new
approach that we find interesting in its own right.


title:
Spatial resolution determination of a position sensitive ultra-cold
  neutron detector

abstract:
  The study of the properties of the quantum states of bouncing neutrons
requires position sensitive detection with micro-metric spatial resolution. The
UCNBoX detector relies on Charge Coupled Devices (CCD) coated with a thin
boron-10 conversion layer to detect neutron hits. In this paper, we present an
original experimental method to determine the spatial resolution of this device
using micrometric masks. The observed resolution is $2.0\pm0.3~\mu$m.


title:
The Necessity of International Particle Physics Opportunities for
  American Education

abstract:
  This Snowmass2021 Contributed Paper addresses the role of the Particle
Physics community in creating and fostering international connections in
American education. It describes the pressing need to introduce students and
faculty to the challenges and rewards of international collaboration, not only
to develop the next generation of scientists and engineers for particle
physics, but to maintain and build U.S. leadership on an increasingly
competitive world stage.
  We present and assess current efforts in education and public engagement with
an eye toward identifying those activities in need of change or increased
resources to improve audience reach and program efficacy. We also consider
possible new activities that might improve upon or complement existing
programs, with the common goal of providing all U.S. students with the
opportunity to benefit from a quality international scientific experience.


title: cRoK: A Composable Robotics Benchmark

abstract:
  Selecting an optimal robot and configuring it for a given task is currently
mostly done by human expertise or trial and error. To evaluate automatic
selection and adaptation of robots to specific tasks, we introduce a benchmark
suite encompassing a common format for robots, environments, and task
descriptions. Our benchmark suite is especially useful for modular robots,
where the creation of the robots themselves creates a host of additional
parameters to optimize. The benchmark defines this optimization and facilitates
the comparison of solution algorithms. All benchmarks are accessible through a
website to conveniently share, reference, and compare solutions.


title: Object Localization under Single Coarse Point Supervision

abstract:
  Point-based object localization (POL), which pursues high-performance object
sensing under low-cost data annotation, has attracted increased attention.
However, the point annotation mode inevitably introduces semantic variance for
the inconsistency of annotated points. Existing POL methods heavily reply on
accurate key-point annotations which are difficult to define. In this study, we
propose a POL method using coarse point annotations, relaxing the supervision
signals from accurate key points to freely spotted points. To this end, we
propose a coarse point refinement (CPR) approach, which to our best knowledge
is the first attempt to alleviate semantic variance from the perspective of
algorithm. CPR constructs point bags, selects semantic-correlated points, and
produces semantic center points through multiple instance learning (MIL). In
this way, CPR defines a weakly supervised evolution procedure, which ensures
training high-performance object localizer under coarse point supervision.
Experimental results on COCO, DOTA and our proposed SeaPerson dataset validate
the effectiveness of the CPR approach. The dataset and code will be available
at https://github.com/ucas-vg/PointTinyBenchmark/.


title:
The free boundary of steady axisymmetric inviscid flow with vorticity I:
  near the degenerate point

abstract:
  In this paper, we investigate the singularity near the degenerate points of
the steady axisymmetric flow with general vorticity of an inviscid
incompressible fluid acted on by gravity and with a free surface. We called the
points on the free boundary at which the gradient of the stream function
vanishes as the degenerate points. The main results in this paper give the
different classifications of the singularity near the degenerate points on the
free surface. More precisely, we obtained that at the stagnation points, the
possible profiles must be a Stokes corner, or a horizontal cusp, or a
horizontal flatness. At the degenerate points on the symmetric axis except the
origin, the wave profile must be a cusp. At the origin, the possible wave
profiles must be a Garabedian pointed bubble, or a horizontal cusp, or a
horizontal flatness.


title:
Connecting Slow Solutions to Nested Recurrences with Linear Recurrent
  Sequences

abstract:
  Labeled infinite trees provide combinatorial interpretations for many integer
sequences generated by nested recurrence relations. Typically, such sequences
are monotone increasing. Several of these sequences also have straightforward
descriptions in terms of how often each value in the sequence occurs. In this
paper, we generalize the most classical examples to a larger family of
sequences parametrized by linear recurrence relations. Each of our sequences
can be constructed in three different ways: via a nested recurrence relation,
from labeled infinite trees, or by using Zeckendorf-like strings of digits to
describe its frequency sequence. We conclude the paper by discussing the
asymptotic behaviors of our sequences.


title:
Boundary conditions for stationary black holes ; Application to Kerr,
  MTZ and hairy black holes

abstract:
  This work proposes a set of equations that can be used to numerically compute
spacetimes containing a stationary black hole. The formalism is based on the
3+1 decomposition of General Relativity with maximal slicing and spatial
harmonic gauge. The presence of the black hole is enforced using the notion of
apparent horizon in equilibrium. This setting leads to the main result of this
paper: a set of boundary conditions describing the horizon and that must be
used when solving the 3+1 equations. Those conditions lead to a choice of
coordinates that is regular even on the horizon itself. The whole procedure is
validated with three different examples chosen to illustrate the great
versatility of the method. First, the single rotating black holes are recovered
up to very high values of the Kerr parameter. Second, non-rotating black holes
coupled to a real scalar field, in the presence of a negative cosmological
constant (the so-called MTZ black holes), are obtained. Last, black holes with
complex scalar hairs are computed. Eventually, prospects for future work, in
particular in contexts where stationarity is only approximate, are discussed.


title:
A necessary and sufficient condition for the existence of non-trivial
  $S_n$-invariants in the splitting algebra

abstract:
  For a monic polynomial $f$ over a commutative, unitary ring $A$ the splitting
algebra $A_f$ is the universal $A$-algebra such that $f$ splits in $A_f$. The
symmetric group acts on the splitting algebra by permuting the roots of $f$. It
is known that if the intersection of the annihilators of the elements $2$ and
$D_f$ (where $D_f$ depends on $f$) in $A$ is zero, then the invariants under
the group action are exactly equal to $A$. We show that the converse holds.