title: Dynamic Free-Space Roadmap for Safe Quadrotor Motion Planning

abstract:
  Free-space-oriented roadmaps typically generate a series of convex geometric
primitives, which constitute the safe region for motion planning. However, a
static environment is assumed for this kind of roadmap. This assumption makes
it unable to deal with dynamic obstacles and limits its applications. In this
paper, we present a dynamic free-space roadmap, which provides feasible spaces
and a navigation graph for safe quadrotor motion planning. Our roadmap is
constructed by continuously seeding and extracting free regions in the
environment. In order to adapt our map to environments with dynamic obstacles,
we incrementally decompose the polyhedra intersecting with obstacles into
obstacle-free regions, while the graph is also updated by our well-designed
mechanism. Extensive simulations and real-world experiments demonstrate that
our method is practically applicable and efficient.


title:
Hessian Averaging in Stochastic Newton Methods Achieves Superlinear
  Convergence

abstract:
  We consider minimizing a smooth and strongly convex objective function using
a stochastic Newton method. At each iteration, the algorithm is given an oracle
access to a stochastic estimate of the Hessian matrix. The oracle model
includes popular algorithms such as the Subsampled Newton and Newton Sketch,
which can efficiently construct stochastic Hessian estimates for many tasks.
Despite using second-order information, these existing methods do not exhibit
superlinear convergence, unless the stochastic noise is gradually reduced to
zero during the iteration, which would lead to a computational blow-up in the
per-iteration cost. We address this limitation with Hessian averaging: instead
of using the most recent Hessian estimate, our algorithm maintains an average
of all past estimates. This reduces the stochastic noise while avoiding the
computational blow-up. We show that this scheme enjoys local $Q$-superlinear
convergence with a non-asymptotic rate of $(\Upsilon\sqrt{\log (t)/t}\,)^{t}$,
where $\Upsilon$ is proportional to the level of stochastic noise in the
Hessian oracle. A potential drawback of this (uniform averaging) approach is
that the averaged estimates contain Hessian information from the global phase
of the iteration, i.e., before the iterates converge to a local neighborhood.
This leads to a distortion that may substantially delay the superlinear
convergence until long after the local neighborhood is reached. To address this
drawback, we study a number of weighted averaging schemes that assign larger
weights to recent Hessians, so that the superlinear convergence arises sooner,
albeit with a slightly slower rate. Remarkably, we show that there exists a
universal weighted averaging scheme that transitions to local convergence at an
optimal stage, and still enjoys a superlinear convergence~rate nearly (up to a
logarithmic factor) matching that of uniform Hessian averaging.


title:
The effect of spatial sampling on magnetic field modeling and helicity
  computation

abstract:
  Nonlinear force-free (NLFF) modeling is regularly used in order to indirectly
infer the 3D geometry of the coronal magnetic field, not accessible on a
regular basis by means of direct measurements otherwise. We study the effect of
binning in time series NLFF modeling of individual active regions (ARs) in
order to quantify the effect of a different underlying spatial sampling on the
quality of modeling as well as on the derived physical parameters. We apply an
optimization method to sequences of SDO/HMI vector magnetogram data at three
different plate scales for three solar ARs to obtain nine NLFF model time
series. From the NLFF models, we deduce active-region magnetic fluxes, electric
currents, magnetic energies and relative helicities, and analyze those with
respect to the underlying spatial sampling. We calculate various metrics to
quantify the quality of the derived NLFF models and apply a Helmholtz
decomposition to characterize solenoidal errors. At a given spatial sampling,
the quality of NLFF modeling is different for different ARs, as well as varies
along of the individual model time series. For a given AR, modeling at a given
spatial sampling is not necessarily of superior quality compared to that
performed with a different plate scale. Generally, the NLFF model quality tends
to be higher for larger pixel sizes with the solenoidal quality being the
ultimate cause for systematic variations in model-deduced physical quantities.
Optimization-based modeling based on binned SDO/HMI vector data delivers
magnetic energies and helicity estimates different by $\lesssim$30\%, given
that concise checks ensure the physical plausibility and high solenoidal
quality of the tested model. Thus, spatial-sampling-induced differences are
relatively small compared to that arising from other sources of uncertainty.


title:
Uncertainty-based Cross-Modal Retrieval with Probabilistic
  Representations

abstract:
  Probabilistic embeddings have proven useful for capturing polysemous word
meanings, as well as ambiguity in image matching. In this paper, we study the
advantages of probabilistic embeddings in a cross-modal setting (i.e., text and
images), and propose a simple approach that replaces the standard vector point
embeddings in extant image-text matching models with probabilistic
distributions that are parametrically learned. Our guiding hypothesis is that
the uncertainty encoded in the probabilistic embeddings captures the
cross-modal ambiguity in the input instances, and that it is through capturing
this uncertainty that the probabilistic models can perform better at downstream
tasks, such as image-to-text or text-to-image retrieval. Through extensive
experiments on standard and new benchmarks, we show a consistent advantage for
probabilistic representations in cross-modal retrieval, and validate the
ability of our embeddings to capture uncertainty.


title:
A Survey on Non-Autoregressive Generation for Neural Machine Translation
  and Beyond

abstract:
  Non-autoregressive (NAR) generation, which is first proposed in neural
machine translation (NMT) to speed up inference, has attracted much attention
in both machine learning and natural language processing communities. While NAR
generation can significantly accelerate inference speed for machine
translation, the speedup comes at the cost of sacrificed translation accuracy
compared to its counterpart, auto-regressive (AR) generation. In recent years,
many new models and algorithms have been designed/proposed to bridge the
accuracy gap between NAR generation and AR generation. In this paper, we
conduct a systematic survey with comparisons and discussions of various
non-autoregressive translation (NAT) models from different aspects.
Specifically, we categorize the efforts of NAT into several groups, including
data manipulation, modeling methods, training criterion, decoding algorithms,
and the benefit from pre-trained models. Furthermore, we briefly review other
applications of NAR models beyond machine translation, such as dialogue
generation, text summarization, grammar error correction, semantic parsing,
speech synthesis, and automatic speech recognition. In addition, we also
discuss potential directions for future exploration, including releasing the
dependency of KD, dynamic length prediction, pre-training for NAR, and wider
applications, etc. We hope this survey can help researchers capture the latest
progress in NAR generation, inspire the design of advanced NAR models and
algorithms, and enable industry practitioners to choose appropriate solutions
for their applications. The web page of this survey is at
\url{https://github.com/LitterBrother-Xiao/Overview-of-Non-autoregressive-Applications}.


title:
The high energy spectrum of Proxima Centauri simultaneously observed at
  X-ray and FUV wavelengths

abstract:
  The M dwarf Proxima~Centauri is known to be magnetically active and it hosts
a likely Earth-like planet in its habitable zone. Understanding the
characteristics of stellar radiation by understanding the properties of the
emitting plasma is of paramount importance for a proper assessment of the
conditions on Proxima~Centauri~b and exoplanets around M dwarfs in general. We
determine the temperature structure of the coronal and transition region plasma
of Proxima Centauri from simultaneous X-ray and far-ultraviolet (FUV)
observations. The differential emission measure distribution (DEM) was
constructed for flaring and quiescent periods by analysing optically thin X-ray
and FUV emission lines. Four X-ray observations of Proxima Centauri were
conducted by the LETGS instrument on board of the Chandra X-ray Observatory and
four FUV observations were carried out using the Hubble STIS spectrograph. From
the X-ray light curves, we determined a variation of the quiescent count rate
by a factor of two within 20\% of the stellar rotation period. To obtain the
DEM, 18 optically thin emission lines were analysed (12 X-ray and six FUV). The
flare fluxes differ from the quiescence fluxes by factors of 4-20 (FUV) and
1-30 (X-ray). The temperature structure of the stellar corona and transition
region was determined for both the quiescence and flaring state by fitting the
DEM(T) with Chebyshev polynomials for a temperature range $\log$T = 4.25 - 8.
Compared to quiescence, the emission measure increases during flares for
temperatures below 0.3\,MK (FUV dominated region) and beyond 3.6\,MK (X-ray
dominated region). The reconstructed DEM shape provides acceptable line flux
predictions compared to the measured values. We provide synthetic spectra at
1-1700 \AA, which may be considered as representative for the high-energy
irradiation of Proxima~Cen~b during quiescent and flare periods.


title: A Survey of Video-based Action Quality Assessment

abstract:
  Human action recognition and analysis have great demand and important
application significance in video surveillance, video retrieval, and
human-computer interaction. The task of human action quality evaluation
requires the intelligent system to automatically and objectively evaluate the
action completed by the human. The action quality assessment model can reduce
the human and material resources spent in action evaluation and reduce
subjectivity. In this paper, we provide a comprehensive survey of existing
papers on video-based action quality assessment. Different from human action
recognition, the application scenario of action quality assessment is
relatively narrow. Most of the existing work focuses on sports and medical
care. We first introduce the definition and challenges of human action quality
assessment. Then we present the existing datasets and evaluation metrics. In
addition, we summarized the methods of sports and medical care according to the
model categories and publishing institutions according to the characteristics
of the two fields. At the end, combined with recent work, the promising
development direction in action quality assessment is discussed.


title: Is Non-IID Data a Threat in Federated Online Learning to Rank?

abstract:
  In this perspective paper we study the effect of non independent and
identically distributed (non-IID) data on federated online learning to rank
(FOLTR) and chart directions for future work in this new and largely unexplored
research area of Information Retrieval. In the FOLTR process, clients
participate in a federation to jointly create an effective ranker from the
implicit click signal originating in each client, without the need to share
data (documents, queries, clicks). A well-known factor that affects the
performance of federated learning systems, and that poses serious challenges to
these approaches, is that there may be some type of bias in the way data is
distributed across clients. While FOLTR systems are on their own rights a type
of federated learning system, the presence and effect of non-IID data in FOLTR
has not been studied. To this aim, we first enumerate possible data
distribution settings that may showcase data bias across clients and thus give
rise to the non-IID problem. Then, we study the impact of each setting on the
performance of the current state-of-the-art FOLTR approach, the Federated
Pairwise Differentiable Gradient Descent (FPDGD), and we highlight which data
distributions may pose a problem for FOLTR methods. We also explore how common
approaches proposed in the federated learning literature address non-IID issues
in FOLTR. This allows us to unveil new research gaps that, we argue, future
research in FOLTR should consider. This is an important contribution to the
current state of FOLTR field because, for FOLTR systems to be deployed, the
factors affecting their performance, including the impact of non-IID data, need
to be thoroughly understood.


title: Sound-Guided Semantic Video Generation

abstract:
  The recent success in StyleGAN demonstrates that pre-trained StyleGAN latent
space is useful for realistic video generation. However, the generated motion
in the video is usually not semantically meaningful due to the difficulty of
determining the direction and magnitude in the StyleGAN latent space. In this
paper, we propose a framework to generate realistic videos by leveraging
multimodal (sound-image-text) embedding space. As sound provides the temporal
contexts of the scene, our framework learns to generate a video that is
semantically consistent with sound. First, our sound inversion module maps the
audio directly into the StyleGAN latent space. We then incorporate the
CLIP-based multimodal embedding space to further provide the audio-visual
relationships. Finally, the proposed frame generator learns to find the
trajectory in the latent space which is coherent with the corresponding sound
and generates a video in a hierarchical manner. We provide the new
high-resolution landscape video dataset (audio-visual pair) for the
sound-guided video generation task. The experiments show that our model
outperforms the state-of-the-art methods in terms of video quality. We further
show several applications including image and video editing to verify the
effectiveness of our method.


title: Causality-based Neural Network Repair

abstract:
  Neural networks have had discernible achievements in a wide range of
applications. The wide-spread adoption also raises the concern of their
dependability and reliability. Similar to traditional decision-making programs,
neural networks can have defects that need to be repaired. The defects may
cause unsafe behaviors, raise security concerns or unjust societal impacts. In
this work, we address the problem of repairing a neural network for desirable
properties such as fairness and the absence of backdoor. The goal is to
construct a neural network that satisfies the property by (minimally) adjusting
the given neural network's parameters (i.e., weights). Specifically, we propose
CARE (\textbf{CA}usality-based \textbf{RE}pair), a causality-based neural
network repair technique that 1) performs causality-based fault localization to
identify the `guilty' neurons and 2) optimizes the parameters of the identified
neurons to reduce the misbehavior. We have empirically evaluated CARE on
various tasks such as backdoor removal, neural network repair for fairness and
safety properties. Our experiment results show that CARE is able to repair all
neural networks efficiently and effectively. For fairness repair tasks, CARE
successfully improves fairness by $61.91\%$ on average. For backdoor removal
tasks, CARE reduces the attack success rate from over $98\%$ to less than
$1\%$. For safety property repair tasks, CARE reduces the property violation
rate to less than $1\%$. Results also show that thanks to the causality-based
fault localization, CARE's repair focuses on the misbehavior and preserves the
accuracy of the neural networks.


title:
Equivalence of minimax and viscosity solutions of path-dependent
  Hamilton-Jacobi equations

abstract:
  In the paper, we consider a path-dependent Hamilton-Jacobi equation with
coinvariant derivatives over the space of continuous functions. Such equations
arise from optimal control problems and differential games for time-delay
systems. We study generalized solutions of the considered Hamilton-Jacobi
equation both in the minimax and in the viscosity sense. A minimax solution is
defined as a functional which epigraph and subgraph satisfy certain conditions
of weak invariance, while a viscosity solution is defined in terms of a pair of
inequalities for coinvariant sub- and super-gradients. We prove that these two
notions are equivalent, which is the main result of the paper. As a corollary,
we obtain comparison and uniqueness results for viscosity solutions of a Cauchy
problem for the considered Hamilton-Jacobi equation and a right-end boundary
condition. The proof is based on a certain property of the coinvariant
subdifferential. To establish this property, we develop a technique going back
to the proofs of multidirectional mean-value inequalities. In particular, the
absence of the local compactness property of the underlying continuous function
space is overcome by using Borwein-Preiss variational principle with an
appropriate guage-type functional.


abstract:
  Most automatic matting methods try to separate the salient foreground from
the background. However, the insufficient quantity and subjective bias of the
current existing matting datasets make it difficult to fully explore the
semantic association between object-to-object and object-to-environment in a
given image. In this paper, we propose a Situational Perception Guided Image
Matting (SPG-IM) method that mitigates subjective bias of matting annotations
and captures sufficient situational perception information for better global
saliency distilled from the visual-to-textual task. SPG-IM can better associate
inter-objects and object-to-environment saliency, and compensate the subjective
nature of image matting and its expensive annotation. We also introduce a
textual Semantic Transformation (TST) module that can effectively transform and
integrate the semantic feature stream to guide the visual representations. In
addition, an Adaptive Focal Transformation (AFT) Refinement Network is proposed
to adaptively switch multi-scale receptive fields and focal points to enhance
both global and local details. Extensive experiments demonstrate the
effectiveness of situational perception guidance from the visual-to-textual
tasks on image matting, and our model outperforms the state-of-the-art methods.
We also analyze the significance of different components in our model. The code
will be released soon.


title:
Varied magnetic phases in a van der Waals easy-plane antiferromagnet
  revealed by nitrogen-vacancy center microscopy

abstract:
  Interest in van der Waals materials often stems from a desire to miniaturise
existing technologies by exploiting their intrinsic layered structure to create
near atomically-thin components that do not suffer from surface defects. One
appealing property is easily-switchable yet robust magnetic order, a quality
only sparsely demonstrated in the case of in-plane anisotropy. In this work, we
use widefield nitrogen-vacancy (NV) center magnetic imaging to measure the
properties of individual flakes of CuCrP$_2$S$_6$, a multiferroic van der Waals
magnet known to exhibit weak easy-plane anisotropy in the bulk. We chart the
crossover between in-plane ferromagnetism in thin flakes down to the trilayer,
and the bulk behaviour dominated by a low-field spin-flop transition. Further,
by exploiting the directional dependence of NV center magnetometry, we are able
to observe an instance of a predominantly out-of-plane ferromagetic phase near
zero field, in contradiction with expectation and previous experiments on the
bulk material. We attribute this to the presence of surface anisotropies
arising from the sample preparation process or exposure to the ambient
environment, which is expected to have more general implications for a broader
class of weakly anisotropic van der Waals magnets.


title:
Bone marrow sparing for cervical cancer radiotherapy on multimodality
  medical images

abstract:
  Cervical cancer threatens the health of women seriously. Radiotherapy is one
of the main therapy methods but with high risk of acute hematologic toxicity.
Delineating the bone marrow (BM) for sparing using computer tomography (CT)
images to plan before radiotherapy can effectively avoid this risk. Comparing
with magnetic resonance (MR) images, CT lacks the ability to express the
activity of BM. Thus, in current clinical practice, medical practitioners
manually delineate the BM on CT images by corresponding to MR images. However,
the time?consuming delineating BM by hand cannot guarantee the accuracy due to
the inconsistency of the CT-MR multimodal images. In this study, we propose a
multimodal image oriented automatic registration method for pelvic BM sparing,
which consists of three-dimensional bone point cloud reconstruction, a local
spherical system iteration closest point registration for marking BM on CT
images. Experiments on patient dataset reveal that our proposed method can
enhance the multimodal image registration accuracy and efficiency for medical
practitioners in sparing BM of cervical cancer radiotherapy. The method
proposed in this contribution might also provide references for similar studies
in other clinical application.


title: Genuinely Multipartite Entanglement vias Quantum Communication

abstract:
  Multipartite entanglement is of important resources for quantum communication
and quantum computation. Our goal in this paper is to characterize general
multipartite entanglement according to quantum biseparable channels. We firstly
prove a no-go result for fully genuinely multipartite entanglement on
finite-dimensional spaces with the biseparable channel model. We further
propose a semi-device-independent entanglement model depending on the
connection ability in quantum circuit. This implies a complete hierarchy of
genuinely multipartite entanglements. It also provides a completely different
multipartite nonlocality from quantum network entanglements. These results show
new insights for multipartite entanglement, quantum network, and
measurement-based quantum computation.


title: Reinforced Structured State-Evolution for Vision-Language Navigation

abstract:
  Vision-and-language Navigation (VLN) task requires an embodied agent to
navigate to a remote location following a natural language instruction.
Previous methods usually adopt a sequence model (e.g., Transformer and LSTM) as
the navigator. In such a paradigm, the sequence model predicts action at each
step through a maintained navigation state, which is generally represented as a
one-dimensional vector. However, the crucial navigation clues (i.e.,
object-level environment layout) for embodied navigation task is discarded
since the maintained vector is essentially unstructured. In this paper, we
propose a novel Structured state-Evolution (SEvol) model to effectively
maintain the environment layout clues for VLN. Specifically, we utilise the
graph-based feature to represent the navigation state instead of the
vector-based state. Accordingly, we devise a Reinforced Layout clues Miner
(RLM) to mine and detect the most crucial layout graph for long-term navigation
via a customised reinforcement learning strategy. Moreover, the Structured
Evolving Module (SEM) is proposed to maintain the structured graph-based state
during navigation, where the state is gradually evolved to learn the
object-level spatial-temporal relationship. The experiments on the R2R and R4R
datasets show that the proposed SEvol model improves VLN models' performance by
large margins, e.g., +3% absolute SPL accuracy for NvEM and +8% for EnvDrop on
the R2R test set.


title:
Disorder Induced Anomalous Hall Effect in Type-I Weyl Metals: Connection
  between the Kubo-Streda Formula in the Spin and Chiral basis

abstract:
  We study the anomalous Hall effect (AHE) in tilted Weyl metals with Gaussian
disorder under the Kubo-Streda formalism in this work. To separate the three
different contributions, namely the intrinsic, side jump and skew scattering,
it's usually considered necessary to go to the eigenband (chiral) basis of the
Kubo-Streda formula. However, it's more straight-forward to compute the total
Hall current in the spin basis. For the reason, we develop a systematic and
transparent scheme to separate the three different contributions in the spin
basis by building a one-to-one correspondence between the Feynmann diagrams of
the different contributions in the chiral basis and the products of the
symmetric and anti-symmetric part of the polarization operator in the spin
basis. We obtain the three contributions of the AHE in type-I Weyl metals with
our scheme and compared the difference with the semi-classical Boltzmann
equation approach. Our scheme is applicable for general anomalous Hall systems
with Gaussian disorder, and is especially useful for anisotropic systems for
which both the semi-classical approach and the Kubo-Streda formula in the
chiral basis encounter diffculties in computing the disorder induced anomalous
Hall effect.


title: The Danger of Small Anonymity Sets in Privacy-Preserving Payment Systems

abstract:
  Unlike suggested during their early years of existence, Bitcoin and similar
cryptocurrencies in fact offer significantly less privacy as compared to
traditional banking. A myriad of privacy-enhancing extensions to those
cryptocurrencies as well as several clean-slate privacy-protecting
cryptocurrencies have been proposed in turn. To convey a better understanding
of the protection of popular design decisions, we investigate expected
anonymity set sizes in an initial simulation study. The large variation of
expected transaction values yields soberingly small effective anonymity sets
for protocols that leak transaction values. We hence examine the effect of
preliminary, intuitive strategies for merging groups of payments into larger
anonymity sets, for instance by choosing from pre-specified value classes. The
results hold promise, as they indeed induce larger anonymity sets at
comparatively low cost, depending on the corresponding strategy


title: Inferring entropy production in anharmonic Brownian gyrators

abstract:
  A non-vanishing entropy production rate is one of the defining
characteristics of any non-equilibrium system, and several techniques exist to
determine this quantity directly from experimental data. The short-time
inference scheme, derived from the thermodynamic uncertainty relation, is a
recent addition to the list of these techniques. Here we apply this scheme to
quantify the entropy production rate in a class of microscopic heat engine
models called Brownian gyrators. In particular, we consider models with
anharmonic confining potentials. In these cases, the dynamical equations are
indelibly non-linear, and the exact dependences of the entropy production rate
on the model parameters are unknown. Our results demonstrate that the
short-time inference scheme can efficiently determine these dependencies from a
moderate amount of trajectory data. Furthermore, the results show that the
non-equilibrium properties of the gyrator model with anharmonic confining
potentials are considerably different from its harmonic counterpart -
especially in set-ups leading to a non-equilibrium dynamics and the resulting
gyration patterns.


title:
Spatially resolving amplitude and phase of light with a reconfigurable
  photonic integrated circuit

abstract:
  Photonic integrated circuits (PICs) play a pivotal role in many applications.
Particularly powerful are circuits based on meshes of reconfigurable
Mach-Zehnder interferometers as they enable active processing of light. Various
possibilities exist to get light into such circuits. Sampling an
electromagnetic field distribution with a carefully designed free-space
interface is one of them. Here, a reconfigurable PIC is used to optically
sample and process free-space beams so as to implement a spatially resolving
detector of amplitudes and phases. In order to perform measurements of this
kind we develop and experimentally implement a versatile method for the
calibration and operation of such integrated photonics based detectors. Our
technique works in a wide parameter range, even when running the chip off the
design wavelength. Amplitude, phase and polarization sensitive measurements are
of enormous importance in modern science and technology, providing a vast range
of applications for such detectors.


title: Tight limits and completions from Dedekind-MacNeille to Lambek-Isbell

abstract:
  While any infimum in a poset can also be computed as a supremum, and vice
versa, categorical limits and colimits do not always approximate each other. If
I approach a point from below, and you approach it from above, then we will
surely meet if we live in a poset, but we may miss each other in a category.
Can we characterize the limits and the colimits that approximate each other,
and guarantee that we will meet? Such limits and colimits are called *tight*.
Some critically important network applications depend on them. This paper
characterizes tight limits and colimits, and describes tight completions,
derived by applying the nucleus construction to adjunctions between loose
completions. Just as the Dedekind-MacNeille completion of a poset preserves any
existing infima and suprema, the tight completion of a category preserves any
existing tight limits and colimits and is therefore idempotent.


title: Exact Stringy Microstates from Gauge Theories

abstract:
  We study how the microstates of BPS sectors in string theory are organized in
terms of a dual $U(N)$ gauge theory. The microstates take the form of a
coherent sum of stacks of branes and their open/closed string excitations. The
strings and branes should be understood in the tensionless limit of string
theory, but their indices are exact at finite $N$. We propose a prescription to
construct the indices of string/brane configurations by analyzing the
modifications of determinant operators in gauge theory. In various examples, we
provide evidence that a sum, of the giant graviton-type recently proposed in
the literature, over all such configurations gives the finite $N$ gauge theory
index. Finally, we discuss how these microstates assemble in the BPS Hilbert
space and in what circumstances the branes can form bound states to produce
black hole degeneracies.


title:
Enabling Dynamic and Intelligent Workflows for HPC, Data Analytics, and
  AI Convergence

abstract:
  The evolution of High-Performance Computing (HPC) platforms enables the
design and execution of progressively larger and more complex workflow
applications in these systems. The complexity comes not only from the number of
elements that compose the workflows but also from the type of computations they
perform. While traditional HPC workflows target simulations and modelling of
physical phenomena, current needs require in addition data analytics (DA) and
artificial intelligence (AI) tasks. However, the development of these workflows
is hampered by the lack of proper programming models and environments that
support the integration of HPC, DA, and AI, as well as the lack of tools to
easily deploy and execute the workflows in HPC systems. To progress in this
direction, this paper presents use cases where complex workflows are required
and investigates the main issues to be addressed for the HPC/DA/AI convergence.
Based on this study, the paper identifies the challenges of a new workflow
platform to manage complex workflows. Finally, it proposes a development
approach for such a workflow platform addressing these challenges in two
directions: first, by defining a software stack that provides the
functionalities to manage these complex workflows; and second, by proposing the
HPC Workflow as a Service (HPCWaaS) paradigm, which leverages the software
stack to facilitate the reusability of complex workflows in federated HPC
infrastructures. Proposals presented in this work are subject to study and
development as part of the EuroHPC eFlows4HPC project.


title: Lightweight HI source finding for next generation radio surveys

abstract:
  Future deep HI surveys will be essential for understanding the nature of
galaxies and the content of the Universe. However, the large volume of these
data will require distributed and automated processing techniques. We introduce
LiSA, a set of python modules for the denoising, detection and characterization
of HI sources in 3D spectral data. LiSA was developed and tested on the Square
Kilometer Array Science Data Challenge 2 dataset, and contains modules and
pipelines for easy domain decomposition and parallel execution. LiSA contains
algorithms for 2D-1D wavelet denoising using the starlet transform and flexible
source finding using null-hypothesis testing. These algorithms are lightweight
and portable, needing only a few user-defined parameters reflecting the
resolution of the data. LiSA also includes two convolutional neural networks
developed to analyse data cubes which separate HI sources from artifacts and
predict the HI source properties. All of these components are designed to be as
modular as possible, allowing users to mix and match different components to
create their ideal pipeline. We demonstrate the performance of the different
components of LiSA on the SDC2 dataset, which is able to find 95% of HI sources
with SNR > 3 and accurately predict their properties.


title:
Distributed Coverage Control of Multi-Agent Systems in Uncertain
  Environments using Heat Transfer Equations

abstract:
  This paper addresses the coverage control problem of multi-agent systems in
the uncertain environment. With the aid of Voronoi partition, a distributed
coverage control formulation of multi-agent system is proposed to complete the
workload in uncertain environments. Driven by the gradient of thermal field,
each agent is able to move around for clearing the workload on its own
subregion. Theoretical analysis is conducted to ensure the completion of
workload in finite time. Finally, numerical simulations are carried out to
demonstrate the effectiveness and advantages of the proposed coverage control
approach as compared to other existing approaches.


title: Human-Object Interaction Detection via Disentangled Transformer

abstract:
  Human-Object Interaction Detection tackles the problem of joint localization
and classification of human object interactions. Existing HOI transformers
either adopt a single decoder for triplet prediction, or utilize two parallel
decoders to detect individual objects and interactions separately, and compose
triplets by a matching process. In contrast, we decouple the triplet prediction
into human-object pair detection and interaction classification. Our main
motivation is that detecting the human-object instances and classifying
interactions accurately needs to learn representations that focus on different
regions. To this end, we present Disentangled Transformer, where both encoder
and decoder are disentangled to facilitate learning of two sub-tasks. To
associate the predictions of disentangled decoders, we first generate a unified
representation for HOI triplets with a base decoder, and then utilize it as
input feature of each disentangled decoder. Extensive experiments show that our
method outperforms prior work on two public HOI benchmarks by a sizeable
margin. Code will be available.


title:
Improving generalization of machine learning-identified biomarkers with
  causal modeling: an investigation into immune receptor diagnostics

abstract:
  Machine learning is increasingly used to discover diagnostic and prognostic
biomarkers from high-dimensional molecular data. However, a variety of factors
related to experimental design may affect the ability to learn generalizable
and clinically applicable diagnostics. Here, we argue that a causal perspective
improves the identification of these challenges, and formalizes their relation
to the robustness and generalization of machine learning-based diagnostics. To
make for a concrete discussion, we focus on a specific, recently established
high-dimensional biomarker - adaptive immune receptor repertoires (AIRRs). We
discuss how the main biological and experimental factors of the AIRR domain may
influence the learned biomarkers and provide easily adjustable simulations of
such effects. In conclusion, we find that causal modeling improves machine
learning-based biomarker robustness by identifying stable relations between
variables and by guiding the adjustment of the relations and variables that
vary between populations.


title:
Towards Arabic Sentence Simplification via Classification and Generative
  Approaches

abstract:
  This paper presents an attempt to build a Modern Standard Arabic (MSA)
sentence-level simplification system. We experimented with sentence
simplification using two approaches: (i) a classification approach leading to
lexical simplification pipelines which use Arabic-BERT, a pre-trained
contextualised model, as well as a model of fastText word embeddings; and (ii)
a generative approach, a Seq2Seq technique by applying a multilingual
Text-to-Text Transfer Transformer mT5. We developed our training corpus by
aligning the original and simplified sentences from the internationally
acclaimed Arabic novel "Saaq al-Bambuu". We evaluate effectiveness of these
methods by comparing the generated simple sentences to the target simple
sentences using the BERTScore evaluation metric. The simple sentences produced
by the mT5 model achieve P 0.72, R 0.68 and F-1 0.70 via BERTScore, while,
combining Arabic-BERT and fastText achieves P 0.97, R 0.97 and F-1 0.97. In
addition, we report a manual error analysis for these experiments.
\url{https://github.com/Nouran-Khallaf/Lexical_Simplification}


title:
A variational framework for the inverse Henderson problem of statistical
  mechanics

abstract:
  The inverse Henderson problem refers to the determination of the pair
potential which specifies the interactions in an ensemble of classical
particles in continuous space, given the density and the equilibrium pair
correlation function of these particles as data. For a canonical ensemble in a
bounded domain it has been observed that this pair potential minimizes a
corresponding convex relative entropy functional, and that the Newton iteration
for minimizing this functional coincides with the so-called inverse Monte Carlo
(IMC) iterative scheme. In this paper we show that in the thermodynamic limit
analogous connections exist between the specific relative entropy introduced by
Georgii and Zessin and a proper formulation of the IMC iteration in the full
space. This provides a rigorous variational framework for the inverse Henderson
problem, valid within a large class of pair potentials, including, for example,
Lennard-Jones type potentials.
  It is further shown that the pressure is strictly convex as a function of the
pair potential and the chemical potential, and that the specific relative
entropy at fixed density is a strictly convex function of the pair potential.
At a given reference potential and a corresponding density in the gas phase we
determine the gradient and the Hessian of the specific relative entropy, and we
prove that the Hessian extends to a symmetric positive semidefinite quadratic
functional in the space of square integrable perturbations of this potential.


title: A 3-stage Spectral-spatial Method for Hyperspectral Image Classification

abstract:
  Hyperspectral images often have hundreds of spectral bands of different
wavelengths captured by aircraft or satellites that record land coverage.
Identifying detailed classes of pixels becomes feasible due to the enhancement
in spectral and spatial resolution of hyperspectral images. In this work, we
propose a novel framework that utilizes both spatial and spectral information
for classifying pixels in hyperspectral images. The method consists of three
stages. In the first stage, the pre-processing stage, Nested Sliding Window
algorithm is used to reconstruct the original data by {enhancing the
consistency of neighboring pixels} and then Principal Component Analysis is
used to reduce the dimension of data. In the second stage, Support Vector
Machines are trained to estimate the pixel-wise probability map of each class
using the spectral information from the images. Finally, a smoothed total
variation model is applied to smooth the class probability vectors by {ensuring
spatial connectivity} in the images. We demonstrate the superiority of our
method against three state-of-the-art algorithms on six benchmark hyperspectral
data sets with 10 to 50 training labels for each class. The results show that
our method gives the overall best performance in accuracy. Especially, our gain
in accuracy increases when the number of labeled pixels decreases and therefore
our method is more advantageous to be applied to problems with small training
set. Hence it is of great practical significance since expert annotations are
often expensive and difficult to collect.


title: Nuclear Spin Quantum Memory in Silicon Carbide

abstract:
  Transition metal (TM) defects in silicon carbide (SiC) are a promising
platform for applications in quantum technology. Some TM defects, e.g.
vanadium, emit in one of the telecom bands, but the large ground state
hyperfine manifold poses a problem for applications which require pure quantum
states. We develop a driven, dissipative protocol to polarize the nuclear spin,
based on a rigorous theoretical model of the defect. We further show that
nuclear-spin polarization enables the use of well-known methods for
initialization and long-time coherent storage of quantum states. The proposed
nuclear-spin preparation protocol thus marks the first step towards an
all-optically controlled integrated platform for quantum technology with TM
defects in SiC.


title: Image Restoration in Non-Linear Filtering Domain using MDB approach

abstract:
  This paper proposes a new technique based on a non-linear Minmax Detector
Based (MDB) filter for image restoration. The aim of image enhancement is to
reconstruct the true image from the corrupted image. The process of image
acquisition frequently leads to degradation and the quality of the digitized
image becomes inferior to the original image. Image degradation can be due to
the addition of different types of noise in the original image. Image noise can
be modelled of many types and impulse noise is one of them. Impulse noise
generates pixels with gray value not consistent with their local neighbourhood.
It appears as a sprinkle of both light and dark or only light spots in the
image. Filtering is a technique for enhancing the image. Linear filter is the
filtering in which the value of an output pixel is a linear combination of
neighborhood values, which can produce blur in the image. Thus a variety of
smoothing techniques have been developed that are non linear. Median filter is
the one of the most popular non-linear filter. When considering a small
neighborhood it is highly efficient but for large window and in case of high
noise it gives rise to more blurring to image. The Centre Weighted Mean (CWM)
filter has got a better average performance over the median filter. However the
original pixel corrupted and noise reduction is substantial under high noise
condition. Hence this technique has also blurring affect on the image. To
illustrate the superiority of the proposed approach, the proposed new scheme
has been simulated along with the standard ones and various restored
performance measures have been compared.


title: Effects of Graph Convolutions in Deep Networks

abstract:
  Graph Convolutional Networks (GCNs) are one of the most popular architectures
that are used to solve classification problems accompanied by graphical
information. We present a rigorous theoretical understanding of the effects of
graph convolutions in multi-layer networks. We study these effects through the
node classification problem of a non-linearly separable Gaussian mixture model
coupled with a stochastic block model. First, we show that a single graph
convolution expands the regime of the distance between the means where
multi-layer networks can classify the data by a factor of at least
$1/\sqrt[4]{\mathbb{E}{\rm deg}}$, where $\mathbb{E}{\rm deg}$ denotes the
expected degree of a node. Second, we show that with a slightly stronger graph
density, two graph convolutions improve this factor to at least
$1/\sqrt[4]{n}$, where $n$ is the number of nodes in the graph. Finally, we
provide both theoretical and empirical insights into the performance of graph
convolutions placed in different combinations among the layers of a network,
concluding that the performance is mutually similar for all combinations of the
placement. We present extensive experiments on both synthetic and real-world
data that illustrate our results.


title: Exploring Widevine for Fun and Profit

abstract:
  For years, Digital Right Management (DRM) systems have been used as the go-to
solution for media content protection against piracy. With the growing
consumption of content using Over-the-Top platforms, such as Netflix or Prime
Video, DRMs have been deployed on numerous devices considered as potential
hostile environments. In this paper, we focus on the most widespread solution,
the closed-source Widevine DRM. Installed on billions of devices, Widevine
relies on cryptographic operations to protect content. Our work presents a
study of Widevine internals on Android, mapping its distinct components and
bringing out its different cryptographic keys involved in content decryption.
We provide a structural view of Widevine as a protocol with its complete key
ladder. Based on our insights, we develop WideXtractor, a tool based on Frida
to trace Widevine function calls and intercept messages for inspection. Using
this tool, we analyze Netflix usage of Widevine as a proof-of-concept, and
raised privacy concerns on user-tracking. In addition, we leverage our
knowledge to bypass the obfuscation of Android Widevine software-only version,
namely L3, and recover its Root-of-Trust.


title: Heavy-Quark Diffusion in the Quark-Gluon Plasma

abstract:
  The diffusion of heavy quarks through the quark-gluon plasma (QGP) as
produced in high-energy heavy-ion collisions has long been recognized as an
excellent probe of its transport properties. In addition, the experimentally
observed heavy-flavor hadrons carry valuable information about the
hadronization process of the transported quarks. Here we review recent progress
in the theoretical developments of heavy-quark interactions in the QGP and how
they relate to the nonperturbative hadronization process, and discuss the
recent status of the pertinent phenomenology in heavy-ion collisions at the
RHIC and the LHC. The interactions of heavy quarks in the QGP also constitute a
central building block in the description of the heavy quarkonia which controls
their transport parameters as well. We will thus focus on theoretical
approaches that aim for a unified description of open and hidden heavy-flavor
particles in medium, and discuss how they can be constrained by lattice QCD
"data" and utilized to deduce fundamental properties of the microscopic
interactions and emerging spectral properties of the strongly coupled QGP.


title: Heavy-Ball-Based Hard Thresholding Algorithms for Sparse Signal Recovery

abstract:
  The hard thresholding technique plays a vital role in the development of
algorithms for sparse signal recovery. By merging this technique and heavy-ball
acceleration method which is a multi-step extension of the traditional gradient
descent method, we propose the so-called heavy-ball-based hard thresholding
(HBHT) and heavy-ball-based hard thresholding pursuit (HBHTP) algorithms for
signal recovery. It turns out that the HBHT and HBHTP can successfully recover
a $k$-sparse signal if the restricted isometry constant of the measurement
matrix satisfies $\delta_{3k}<0.618 $ and $\delta_{3k}<0.577,$ respectively.
The guaranteed success of HBHT and HBHTP is also shown under the conditions
$\delta_{2k}<0.356$ and $\delta_{2k}<0.377,$ respectively. Moreover, the finite
convergence and stability of the two algorithms are also established in this
paper. Simulations on random problem instances are performed to compare the
performance of the proposed algorithms and several existing ones. Empirical
results indicate that the HBHTP performs very comparably to a few existing
algorithms and it takes less average time to achieve the signal recovery than
these existing methods.


title: Bistable pulsating fronts in slowly oscillating environments *

abstract:
  We consider reaction-diffusion fronts in spatially periodic bistable media
with large periods. Whereas the homogenization regime associated with small
periods had been well studied for bistable or Fisher-KPP reactions and, in the
latter case, a formula for the limit minimal speeds of fronts in media with
large periods had also been obtained thanks to the linear formulation of these
minimal speeds and their monotonicity with respect to the period, the main
remaining open question is concerned with fronts in bistable environments with
large periods. In bistable media the unique front speeds are not linearly
determined and are not monotone with respect to the spatial period in general,
making the analysis of the limit of large periods more intricate. We show in
this paper the existence of and an explicit formula for the limit of bistable
front speeds as the spatial period goes to infinity. We also prove that the
front profiles converge to a family of front profiles associated with spatially
homogeneous equations. The main results are based on uniform estimates on the
spatial width of the fronts, which themselves use zero number properties and
intersection arguments.


title: Adaptive Non-linear Filtering Technique for Image Restoration

abstract:
  Removing noise from the any processed images is very important. Noise should
be removed in such a way that important information of image should be
preserved. A decisionbased nonlinear algorithm for elimination of band lines,
drop lines, mark, band lost and impulses in images is presented in this paper.
The algorithm performs two simultaneous operations, namely, detection of
corrupted pixels and evaluation of new pixels for replacing the corrupted
pixels. Removal of these artifacts is achieved without damaging edges and
details. However, the restricted window size renders median operation less
effective whenever noise is excessive in that case the proposed algorithm
automatically switches to mean filtering. The performance of the algorithm is
analyzed in terms of Mean Square Error [MSE], Peak-Signal-to-Noise Ratio
[PSNR], Signal-to-Noise Ratio Improved [SNRI], Percentage Of Noise Attenuated
[PONA], and Percentage Of Spoiled Pixels [POSP]. This is compared with standard
algorithms already in use and improved performance of the proposed algorithm is
presented. The advantage of the proposed algorithm is that a single algorithm
can replace several independent algorithms which are required for removal of
different artifacts.


title:
Attention in Attention: Modeling Context Correlation for Efficient Video
  Classification

abstract:
  Attention mechanisms have significantly boosted the performance of video
classification neural networks thanks to the utilization of perspective
contexts. However, the current research on video attention generally focuses on
adopting a specific aspect of contexts (e.g., channel, spatial/temporal, or
global context) to refine the features and neglects their underlying
correlation when computing attentions. This leads to incomplete context
utilization and hence bears the weakness of limited performance improvement. To
tackle the problem, this paper proposes an efficient attention-in-attention
(AIA) method for element-wise feature refinement, which investigates the
feasibility of inserting the channel context into the spatio-temporal attention
learning module, referred to as CinST, and also its reverse variant, referred
to as STinC. Specifically, we instantiate the video feature contexts as
dynamics aggregated along a specific axis with global average and max pooling
operations. The workflow of an AIA module is that the first attention block
uses one kind of context information to guide the gating weights calculation of
the second attention that targets at the other context. Moreover, all the
computational operations in attention units act on the pooled dimension, which
results in quite few computational cost increase ($<$0.02\%). To verify our
method, we densely integrate it into two classical video network backbones and
conduct extensive experiments on several standard video classification
benchmarks. The source code of our AIA is available at
\url{https://github.com/haoyanbin918/Attention-in-Attention}.


title:
Boundary pointwise regularity and applications to the regularity of free
  boundaries

abstract:
  In this paper, we develop a series of boundary pointwise regularity for
Dirichlet problems and oblique derivative problems. As applications, we give
direct and simple proofs of the higher regularity of the free boundaries in
obstacle-type problems and one phase problems.


title: Recent Advances in Asymptotic Analysis

abstract:
  This is a survey article on an old topic in classical analysis. We present
some new developments in asymptotics in the last fifty years. We start with the
classical method of Darboux and its generalizations, including an uniformity
treatment which has a direct application to the Heisenberg polynomials. We then
present the development of an asymptotic theory for difference equations, which
is a major advancement since the work of Birkhoff and Trjitzinsky in 1933. A
new method was introduced into this field in the nineteen nineties, which is
now known as the nonlinear steepest descent method or the Riemann-Hilbert
approach. The advantage of this method is that it can be applied to orthogonal
polynomials which do not satisfy any differential or difference equations
neither do they have any integral representations. As an example, we mention
the case of orthogonal polynomials with respect to the Freud weight. Finally,
we show how the Wiener-Hopf technique can be used to derive asymptotic
expansions for the solutions of an integral equation on a half line.


title: On the $\nu$-zeros of the Bessel functions of purely imaginary order

abstract:
  The $\nu$-zeros of the Bessel functions of purely imaginary order are
examined for fixed argument $x>0$. In the case of the modified Bessel function
of the second kind $K_{i\nu}(x)$, it is known that it possesses a countably
infinite sequence of real $\nu$-zeros described by $\nu_n\sim \pi n/\log\,n$ as
$n\to\infty$. Here we apply a unified approach to determine asymptotic
estimates of the $\nu$-zeros of the modified Bessel functions
$L_{i\nu}(x)\equiv I_{i\nu}(x)+I_{-i\nu}(x)$ and $K_{i\nu}(x)$ and the ordinary
Bessel functions $J_{i\nu}(x)\pm J_{-i\nu}(x)$.


title:
Self-similar shrinking of supports and non-extinction for a nonlinear
  diffusion equation with spatially inhomogeneous strong absorption

abstract:
  We study the dynamics of the following porous medium equation with strong
absorption $$\partial_t u=\Delta u^m-|x|^{\sigma}u^q,$$ posed for $(t, x) \in
(0,\infty) \times \mathbb{R}^N$, with $m > 1$, $q \in (0, 1)$ and $\sigma >
2(1-q)/(m-1)$. Considering the Cauchy problem with non-negative initial
condition $u_0 \in L^\infty(\mathbb{R}^N)$ instantaneous shrinking and
localization of supports for the solution $u(t)$ at any $t > 0$ are
established. With the help of this property, existence and uniqueness of a
nonnegative compactly supported and radially symmetric forward self-similar
solution with algebraic decay in time are proven. Finally, it is shown that
finite time extinction does not occur for a wide class of initial conditions
and this unique self-similar solution is the pattern for large time behavior of
these general solutions.


title: A Deeper Look into Aleatoric and Epistemic Uncertainty Disentanglement

abstract:
  Neural networks are ubiquitous in many tasks, but trusting their predictions
is an open issue. Uncertainty quantification is required for many applications,
and disentangled aleatoric and epistemic uncertainties are best. In this paper,
we generalize methods to produce disentangled uncertainties to work with
different uncertainty quantification methods, and evaluate their capability to
produce disentangled uncertainties. Our results show that: there is an
interaction between learning aleatoric and epistemic uncertainty, which is
unexpected and violates assumptions on aleatoric uncertainty, some methods like
Flipout produce zero epistemic uncertainty, aleatoric uncertainty is unreliable
in the out-of-distribution setting, and Ensembles provide overall the best
disentangling quality. We also explore the error produced by the number of
samples hyper-parameter in the sampling softmax function, recommending N > 100
samples. We expect that our formulation and results help practitioners and
researchers choose uncertainty methods and expand the use of disentangled
uncertainties, as well as motivate additional research into this topic.


title: Subsonic and supersonic gas flows to condensation surface

abstract:
  Intense heat-mass transfer in a gas flow to a condensation surface is studied
with the consistent atomistic and kinetic theory methods. The simple moment
method is utilized for solving the Boltzmann kinetic equation (BKE) for the
nonequilibrium gas flow and its condensation, while molecular dynamics (MD)
simulation of a similar flow is used for verification of BKE results. We
demonstrate that BKE can provide the steady flow profiles close to those
obtained from MD simulations in both subsonic and supersonic regimes of steady
gas flows. Surprisingly, the elementary theory of condensation is shown with
BKE results to have a good accuracy in a wide range of gas flow parameters.
  MD confirms that a steady supersonic gas flow condensates on a surface at the
distinctive temperature after formation of a standing shock front in reference
to this surface, which can be interpreted as a permeable condensating piston.
The last produces the shock compression but completely absorbs incoming gas
flow in contrast to a common impermeable piston. The shock front divides the
vapor flow on the supersonic and subsonic zones, and condensation of compressed
gas happens in the subsonic regime. The complete and partial condensation
regimes are discussed. It is shown that above the certain surface temperatures
determined by the shock Hugoniot the runaway shock front stops an inflow gas
and condensation is ceased.


title: Where is Your App Frustrating Users?

abstract:
  User reviews of mobile apps provide a communication channel for developers to
perceive user satisfaction. Many app features that users have problems with are
usually expressed by key phrases such as "upload pictures", which could be
buried in the review texts. The lack of fine-grained view about problematic
features could obscure the developers' understanding of where the app is
frustrating users, and postpone the improvement of the apps. Existing
pattern-based approaches to extract target phrases suffer from low accuracy due
to insufficient semantic understanding of the reviews, thus can only summarize
the high-level topics/aspects of the reviews. This paper proposes a
semantic-aware, fine-grained app review analysis approach (SIRA) to extract,
cluster, and visualize the problematic features of apps. The main component of
SIRA is a novel BERT+Attr-CRF model for fine-grained problematic feature
extraction, which combines textual descriptions and review attributes to better
model the semantics of reviews and boost the performance of the traditional
BERT-CRF model. SIRA also clusters the extracted phrases based on their
semantic relations and presents a visualization of the summaries. Our
evaluation on 3,426 reviews from six apps confirms the effectiveness of SIRA in
problematic feature extraction and clustering. We further conduct an empirical
study with SIRA on 318,534 reviews of 18 popular apps to explore its potential
application and examine its usefulness in real-world practice.


title: Wireless Crowd Charging with Battery Aging Mitigation

abstract:
  Battery aging is one of the major concerns for the pervasive devices such as
smartphones, wearables and laptops. Current battery aging mitigation approaches
only partially leverage the available options to prolong battery lifetime. In
this regard, we claim that wireless crowd charging via network-wide smart
charging protocols can provide a useful setting for applying battery aging
mitigation. In this paper, for the first time in the state-of-the-art, we
couple the two concepts and we design a fine-grained battery aging model in the
context of wireless crowd charging, and two network-wide protocols to mitigate
battery aging. Our approach directly challenges the related contemporary
research paradigms by (i) taking into account important characteristic
phenomena in the algorithmic modeling process related to fine-grained battery
aging properties, (ii) deploying ubiquitous computing and network-wide
protocols for battery aging mitigation, and (iii) fulfilling the user QoE
expectations with respect to the enjoyment of a longer battery lifetime.
Simulation-based results indicate that the proposed protocols are able to
mitigate battery aging quickly in terms of nearly 46.74-60.87% less reduction
of battery capacity among the crowd, and partially outperform state-of-the-art
protocols in terms of energy balance quality.


title: Classification of log smooth toric del Pezzo pairs

abstract:
  We give a description of all log-Fano pairs (X, D) where X is a smooth toric
surface and D a reduced simple normal crossing divisor such that the pair (X,
D) is equivariant.


title: Minimal models of field theories: SDYM and SDGR

abstract:
  There exists a natural $L_\infty$-algebra or $Q$-manifold that can be
associated to any (gauge) field theory. Perturbatively, it can be obtained by
reducing the $L_\infty$-algebra behind the jet space BV-BRST formulation to its
minimal model. We explicitly construct the minimal models of self-dual
Yang-Mills and self-dual gravity theories, which also represents their
equations of motion as Free Differential Algebras. The minimal model regains
all relevant information about the field theory, e.g. actions, charges,
anomalies, can be understood in terms of the corresponding $Q$-cohomology.


title:
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of
  Compressed Video: Dataset, Methods and Results

abstract:
  This paper reviews the NTIRE 2022 Challenge on Super-Resolution and Quality
Enhancement of Compressed Video. In this challenge, we proposed the LDV 2.0
dataset, which includes the LDV dataset (240 videos) and 95 additional videos.
This challenge includes three tracks. Track 1 aims at enhancing the videos
compressed by HEVC at a fixed QP. Track 2 and Track 3 target both the
super-resolution and quality enhancement of HEVC compressed video. They require
x2 and x4 super-resolution, respectively. The three tracks totally attract more
than 600 registrations. In the test phase, 8 teams, 8 teams and 12 teams
submitted the final results to Tracks 1, 2 and 3, respectively. The proposed
methods and solutions gauge the state-of-the-art of super-resolution and
quality enhancement of compressed video. The proposed LDV 2.0 dataset is
available at https://github.com/RenYang-home/LDV_dataset. The homepage of this
challenge (including open-sourced codes) is at
https://github.com/RenYang-home/NTIRE22_VEnh_SR.


title: Memory-Constrained Policy Optimization

abstract:
  We introduce a new constrained optimization method for policy gradient
reinforcement learning, which uses two trust regions to regulate each policy
update. In addition to using the proximity of one single old policy as the
first trust region as done by prior works, we propose to form a second trust
region through the construction of another virtual policy that represents a
wide range of past policies. We then enforce the new policy to stay closer to
the virtual policy, which is beneficial in case the old policy performs badly.
More importantly, we propose a mechanism to automatically build the virtual
policy from a memory buffer of past policies, providing a new capability for
dynamically selecting appropriate trust regions during the optimization
process. Our proposed method, dubbed as Memory-Constrained Policy Optimization
(MCPO), is examined on a diverse suite of environments including robotic
locomotion control, navigation with sparse rewards and Atari games,
consistently demonstrating competitive performance against recent on-policy
constrained policy gradient methods.


title: Massive Twinning to Enhance Emergent Intelligence

abstract:
  Future Industrial Internet-of-Things in the upcoming 6G era is expected to
deploy artificial intelligence (AI) and digital twins (DTs) ubiquitously. As a
complement to conventional AI solutions, emergent intelligence (EI) exhibits
various outstanding features including robustness, protection to privacy, and
scalability, which makes it competitive for 6G IIoT applications. However,
despite its low computational complexity, it is challenged by its high demand
of data traffic in massive deployment. In this paper, we propose to exploit the
massive twinning paradigm, which 6G is envisaged to support, to reduce the data
traffic in EI and therewith enhance its performance.


title: Regularity in shape optimization under convexity constraint

abstract:
  This paper is concerned with the regularity of shape optimizers of a class of
isoperimetric problems under convexity constraint. We prove that minimizers of
the sum of the perimeter and a perturbative term, among convex shapes, are C
1,1-regular. To that end, we define a notion of quasi-minimizer fitted to the
convexity context and show that any such quasi-minimizer is C 1,1-regular. The
proof relies on a cutting procedure which was introduced to prove similar
regularity results in the calculus of variations context. Using a penalization
method we are able to treat a volume constraint, showing the same regularity in
this case. We go through some examples taken from PDE theory, that is when the
perturbative term is of PDE type, and prove that a large class of such examples
fit into our C 1,1-regularity result. Finally we provide a counterexample
showing that we cannot expect higher regularity in general.


title: Semistable reduction over thick log points

abstract:
  We establish a version of a semistable reduction theorem over a log point
with a non-trivial nilpotent structure. In order to do this we extend the
classical desingularization theories to non-reduced schemes with generically
principal nilradical.


title: Logarithmic Morphological Neural Nets robust to lighting variations

abstract:
  Morphological neural networks allow to learn the weights of a structuring
function knowing the desired output image. However, those networks are not
intrinsically robust to lighting variations in images with an optical cause,
such as a change of light intensity. In this paper, we introduce a
morphological neural network which possesses such a robustness to lighting
variations. It is based on the recent framework of Logarithmic Mathematical
Morphology (LMM), i.e. Mathematical Morphology defined with the Logarithmic
Image Processing (LIP) model. This model has a LIP additive law which simulates
in images a variation of the light intensity. We especially learn the
structuring function of a LMM operator robust to those variations, namely : the
map of LIP-additive Asplund distances. Results in images show that our neural
network verifies the required property.


title:
SpiderNet: Hybrid Differentiable-Evolutionary Architecture Search via
  Train-Free Metrics

abstract:
  Neural Architecture Search (NAS) algorithms are intended to remove the burden
of manual neural network design, and have shown to be capable of designing
excellent models for a variety of well-known problems. However, these
algorithms require a variety of design parameters in the form of user
configuration or hard-coded decisions which limit the variety of networks that
can be discovered. This means that NAS algorithms do not eliminate model design
tuning, they instead merely shift the burden of where that tuning needs to be
applied. In this paper, we present SpiderNet, a hybrid
differentiable-evolutionary and hardware-aware algorithm that rapidly and
efficiently produces state-of-the-art networks. More importantly, SpiderNet is
a proof-of-concept of a minimally-configured NAS algorithm; the majority of
design choices seen in other algorithms are incorporated into SpiderNet's
dynamically-evolving search space, minimizing the number of user choices to
just two: reduction cell count and initial channel count. SpiderNet produces
models highly-competitive with the state-of-the-art, and outperforms random
search in accuracy, runtime, memory size, and parameter count.


title:
Impredicativity and Trees with Gap Condition: A Second Course on Ordinal
  Analysis

abstract:
  These lecture notes introduce central notions of impredicative ordinal
analysis, such as the Bachmann-Howard ordinal and the method of collapsing,
which transforms uncountable proof trees into countable ones. Specifically, we
analyze parameter-free $\Pi^1_1$-comprehension and show that it cannot prove
the extended Kruskal theorem due to Harvey Friedman (not even for two labels).
In terms of prerequisites, we build on a previous lecture on the ordinal
analysis of Peano arithmetic (arXiv:2109.06258). The present material is
intended for 12 lectures and 6 exercise sessions of 90 minutes each.


title: Density of rational points on some quadric bundle threefolds

abstract:
  We prove the Manin-Peyre conjecture for the number of rational points of
bounded height outside of a thin subset on a family of Fano threefolds of
bidegree (1,2). The proof uses a mixture of the circle method and techniques
from the geometry of numbers.


title: Self-supervised Learning for Sonar Image Classification

abstract:
  Self-supervised learning has proved to be a powerful approach to learn image
representations without the need of large labeled datasets. For underwater
robotics, it is of great interest to design computer vision algorithms to
improve perception capabilities such as sonar image classification. Due to the
confidential nature of sonar imaging and the difficulty to interpret sonar
images, it is challenging to create public large labeled sonar datasets to
train supervised learning algorithms. In this work, we investigate the
potential of three self-supervised learning methods (RotNet, Denoising
Autoencoders, and Jigsaw) to learn high-quality sonar image representation
without the need of human labels. We present pre-training and transfer learning
results on real-life sonar image datasets. Our results indicate that
self-supervised pre-training yields classification performance comparable to
supervised pre-training in a few-shot transfer learning setup across all three
methods. Code and self-supervised pre-trained models are be available at
https://github.com/agrija9/ssl-sonar-images


title:
In-situ imaging of the three-dimensional shape of soft responsive
  particles at fluid interfaces by atomic force microscopy

abstract:
  The reconfiguration of soft, deformable particles upon adsorption at the
interface between two fluids underpins many aspects of their dynamics and
interactions, ultimately controlling the macroscopic properties of particle
monolayers of relevance for materials, such as particle-stabilized emulsions
and foams, and processes, e. g. particle-based lithography. In spite of its
importance, experimentally determining the three-dimensional shape of soft
particles at fluid interfaces with high resolution remains an elusive task. In
this work, we take poly(N-isopropylacrylamide) (pNIPAM) microgels as model soft
particles and demonstrate that their conformation at the interface between an
aqueous and an oil phase can be fully reconstructed by means of in-situ atomic
force microscopy (AFM) imaging. We show that imaging the particle topography
from both sides of the interface allows one to characterize the in-plane
deformation of the particle under the action of interfacial tension and to
visualize the occurrence of asymmetric swelling in the two fluids.
Additionally, the technique enables investigating different fluid phases and
particle architectures, as well as studying in situ the effect of temperature
variations on particle conformation. We envisage that these results open up an
exciting range of possibilities to provide microscopic insights between the
single-particle behavior of soft objects at fluid interfaces and macroscopic
material properties of relevance for applications and fundamental studies
alike.


title:
Congestion Mitigation in Unbalanced Residential Networks with OPF-based
  Demand Management

abstract:
  This paper proposes a novel congestion mitigation strategy for low voltage
residential feeders in which the rising power demand due to the electrification
of the transport and heating systems leads to congestion problems. The strategy
is based on requiring residential customers to limit their demand for a certain
amount of time in exchange for economic benefits.
  The main novelty of the method consists of combining a thorough
representation of the network physics with advanced constraints that ensure the
comfort of residential users, in a scalable manner that suits real systems. The
mitigation strategy is presented from a DSO perspective, and takes the form of
contracts between users and system operator. The focus on user comfort aims to
make the contracts appealing, encouraging users to voluntarily enroll in the
proposed mitigation scheme.
  The presented solution is implemented as a mixed-integer multi-period optimal
power flow problem which relies on a linearized three-phase power flow
formulation. Calculations on 100 real-life distribution feeders are performed,
to analyze the congestion-relieving potential of several possible system
operator-user contracts. From a planning perspective, the results can help the
system operator define contractual terms that make a specific congestion
mitigation scheme effective and viable. From an operational perspective, the
same calculations can be used to optimally schedule power reduction on a
day-ahead basis.


title: On generalisations of the Aharoni-Pouzet base exchange theorem

abstract:
  The Greene-Magnanti theorem states that if $ M $ is a finite matroid, $ B_0 $
and $ B_1 $ are bases and $ B_0=\bigcup_{i=1}^{n} X_i $ is a partition, then
there is a partition $ B_1=\bigcup_{i=1}^{n}Y_i $ such that $ (B_0 \setminus
X_i) \cup Y_i $ is a base for every $ i $. The special case where each $ X_i $
is a singleton can be rephrased as the existence of a perfect matching in the
base transition graph. Pouzet conjectured that this remains true in infinite
dimensional vector spaces. Later he and Aharoni answered this conjecture
affirmatively not just for vector spaces but for infinite matroids.
  We prove two generalisations of their result. On the one hand, we show that
`being a singleton' can be relaxed to `being finite' and this is sharp in the
sense the exclusion of infinite sets is really necessary. On the other hand, we
prove that if $ B_0$ and $ B_1 $ are bases, then there is a bijection $ F $
between their finite subsets such that $ (B_0\setminus I) \cup F(I) $ is a base
for every $ I$. In contrast to the approach of Aharoni and Pouzet, our proofs
are completely elementary, they do not rely on infinite matching theory.


title:
Narrowband spikes observed during the 13 June 2012 flare in the 800-2000
  MHz range

abstract:
  Narrowband (~5 MHz) and short-lived (~0.01 s) spikes with three different
distributions on the 800-2000 MHz radio spectrum of the 13 June 2012 flare are
detected and analyzed. We designate them as SB (spikes distributed in broad
band or bands), SZ (spikes distributed in zebra-like bands) and SBN (spikes
distributed in broad and narrow bands). Analyzing AIA/SDO images of the active
region NOAA 11504, a rough correspondence between groups of the spikes observed
on 1000 MHz and peaks in the time profiles of AIA channels taken from the flare
sub-area close to the leading sunspot is found. Among types of spikes the SZ
type is the most interesting because it resembles to zebras. Therefore, using
auto-correlation and cross-correlation methods we compare SZ and SBN spikes
with the typical zebra observed in the same frequency range. While the ratio of
SZ band frequencies with their frequency separation (220 MHz) is about 4, 5 and
6, in the zebra the frequency stripe separation is about 24 MHz and the ratio
is around 50. Moreover, the bandwidth of SZ bands, which consists of clouds of
narrowband spikes, is much broader than that of zebra stripes. This comparison
indicates that SZ spikes are generated different way than the zebra, but
similar way as SBN spikes. We successfully fit the SZ band frequencies by the
Bernstein modes. Based on this fitting we interpret SZ and SBN spikes as those
generated in the model with Bernstein modes. Thus, the magnetic field and
plasma density in the SZ spike source is estimated as about 79 G and 8.4x10^9
cm-3, respectively.


title:
Federated Learning in Multi-Center Critical Care Research: A Systematic
  Case Study using the eICU Database

abstract:
  Federated learning (FL) has been proposed as a method to train a model on
different units without exchanging data. This offers great opportunities in the
healthcare sector, where large datasets are available but cannot be shared to
ensure patient privacy. We systematically investigate the effectiveness of FL
on the publicly available eICU dataset for predicting the survival of each ICU
stay. We employ Federated Averaging as the main practical algorithm for FL and
show how its performance changes by altering three key hyper-parameters, taking
into account that clients can significantly vary in size. We find that in many
settings, a large number of local training epochs improves the performance
while at the same time reducing communication costs. Furthermore, we outline in
which settings it is possible to have only a low number of hospitals
participating in each federated update round. When many hospitals with low
patient counts are involved, the effect of overfitting can be avoided by
decreasing the batchsize. This study thus contributes toward identifying
suitable settings for running distributed algorithms such as FL on clinical
datasets.


title:
Deterministic Distributed algorithms and Descriptive Combinatorics on
  \Delta-regular trees

abstract:
  We study complexity classes of local problems on regular trees from the
perspective of distributed local algorithms and descriptive combinatorics. We
show that, surprisingly, some deterministic local complexity classes from the
hierarchy of distributed computing exactly coincide with well studied classes
of problems in descriptive combinatorics. Namely, we show that a local problem
admits a continuous solution if and only if it admits a local algorithm with
local complexity $O(\log^* n)$, and a Baire measurable solution if and only if
it admits a local algorithm with local complexity $O(\log n)$.


title: Group relations, resilience and the I Ching

abstract:
  We evaluate the robustness and adaptivity of social groups with heterogeneous
agents that are characterized by their binary state, their ability to change
this state, their status and their preferred relations to other agents. To
define group structures, we operationalize the hexagrams of the \emph{I Ching}.
The relations and properties of agents are used to quantify their influence
according to the social impact theory. From these influence values we derive a
weighted stability measure for triads involving three agents, which is based on
the weighted balance theory. It allows to quantify the robustness of groups and
to propose a novel measure for group resilience which combines robustness and
adaptivity. A stochastic approach determines the probabilities to find robust
and adaptive groups. The discussion focuses on the generalization of our
approach.


title: NFormer: Robust Person Re-identification with Neighbor Transformer

abstract:
  Person re-identification aims to retrieve persons in highly varying settings
across different cameras and scenarios, in which robust and discriminative
representation learning is crucial. Most research considers learning
representations from single images, ignoring any potential interactions between
them. However, due to the high intra-identity variations, ignoring such
interactions typically leads to outlier features. To tackle this issue, we
propose a Neighbor Transformer Network, or NFormer, which explicitly models
interactions across all input images, thus suppressing outlier features and
leading to more robust representations overall. As modelling interactions
between enormous amount of images is a massive task with lots of distractors,
NFormer introduces two novel modules, the Landmark Agent Attention, and the
Reciprocal Neighbor Softmax. Specifically, the Landmark Agent Attention
efficiently models the relation map between images by a low-rank factorization
with a few landmarks in feature space. Moreover, the Reciprocal Neighbor
Softmax achieves sparse attention to relevant -- rather than all -- neighbors
only, which alleviates interference of irrelevant representations and further
relieves the computational burden. In experiments on four large-scale datasets,
NFormer achieves a new state-of-the-art. The code is released at
\url{https://github.com/haochenheheda/NFormer}.


title: Detection of stellar-like abundance anomalies in the slow solar wind

abstract:
  The elemental composition of the Sun's hot atmosphere, the corona, shows a
distinctive pattern that is different than the underlying surface, or
photosphere (Pottasch 1963). Elements that are easy to ionize in the
chromosphere are enhanced in abundance in the corona compared to their
photospheric values. A similar pattern of behavior is often observed in the
slow speed (< 500 km/s) solar wind (Meyer 1985), and in solar-like stellar
coronae (Drake et al. 1997), while a reversed effect is seen in M-dwarfs
(Liefke et al. 2008). Studies of the inverse effect have been hampered in the
past because only unresolved (point source) spectroscopic data were available
for these stellar targets. Here we report the discovery of several inverse
events observed in-situ in the slow solar wind using particle counting
techniques. These very rare events all occur during periods of high solar
activity that mimic conditions more widespread on M-dwarfs. The detections
allow a new way of connecting the slow wind to its solar source, and are
broadly consistent with theoretical models of abundance variations due to
chromospheric fast mode waves with amplitudes of 8-10 km/s; sufficient to
accelerate the solar wind. The results imply that M-dwarf winds are dominated
by plasma depleted in easily ionized elements, and lend credence to previous
spectroscopic measurements.


title: Profiling and Evolution of Intellectual Property

abstract:
  In recent years, with the rapid growth of Internet data, the number and types
of scientific and technological resources are also rapidly expanding. However,
the increase in the number and category of information data will also increase
the cost of information acquisition. For technology-based enterprises or users,
in addition to general papers, patents, etc., policies related to technology or
the development of their industries should also belong to a type of scientific
and technological resources. The cost and difficulty of acquiring users.
Extracting valuable science and technology policy resources from a huge amount
of data with mixed contents and providing accurate and fast retrieval will help
to break down information barriers and reduce the cost of information
acquisition, which has profound social significance and social utility. This
article focuses on the difficulties and problems in the field of science and
technology policy, and introduces related technologies and developments.


title:
Unsupervised Domain Adaptation for Cardiac Segmentation: Towards
  Structure Mutual Information Maximization

abstract:
  Unsupervised domain adaptation approaches have recently succeeded in various
medical image segmentation tasks. The reported works often tackle the domain
shift problem by aligning the domain-invariant features and minimizing the
domain-specific discrepancies. That strategy works well when the difference
between a specific domain and between different domains is slight. However, the
generalization ability of these models on diverse imaging modalities remains a
significant challenge. This paper introduces UDA-VAE++, an unsupervised domain
adaptation framework for cardiac segmentation with a compact loss function
lower bound. To estimate this new lower bound, we develop a novel Structure
Mutual Information Estimation (SMIE) block with a global estimator, a local
estimator, and a prior information matching estimator to maximize the mutual
information between the reconstruction and segmentation tasks. Specifically, we
design a novel sequential reparameterization scheme that enables information
flow and variance correction from the low-resolution latent space to the
high-resolution latent space. Comprehensive experiments on benchmark cardiac
segmentation datasets demonstrate that our model outperforms previous
state-of-the-art qualitatively and quantitatively. The code is available at
https://github.com/LOUEY233/Toward-Mutual-Information}{https://github.com/LOUEY233/Toward-Mutual-Information


title: Superradiant instabilities by accretion disks in scalar-tensor theories

abstract:
  We study the superradiant instability in scalar-tensor theories of
gravitation, where matter outside a black hole provides an effective mass to
the scalar degree of freedom of the gravitational sector. We discuss this
effect for arbitrarily spinning black holes and for realistic models of
truncated thin and thick accretion disks (where the perturbation equations are
nonseparable), paying particular attention to the role of hot coronal flows in
the vicinity of the black hole. The system qualitatively resembles the
phenomenology of plasma-driven superradiant instabilities in General
Relativity. Nevertheless, we show that the obstacles hampering the efficiency
of plasma-driven superradiant instabilities in General Relativity can be
circumvented in scalar-tensor theories. We find a wide range of parameter space
where superradiant instabilities can be triggered in realistic scenarios, and
discuss the constraints on scalar-tensor theories imposed by this effect. In
particular, we argue that the existence of highly spinning accreting black
holes is in tension with some scalar-tensor alternatives to the dark energy,
e.g. symmetron models with screening.


title: On chiral bosons in 2D and 6D

abstract:
  In the Hamiltonian formulation of chiral 2k-form electrodynamics, the 2k-form
potential on the (4k+1)-space is defined up to the addition of either (i) a
closed $2k$-form or (ii) an exact 2k-form, depending on the choice of chirality
constraint. Case (i) is realized by the Floreanini-Jackiw 2D chiral boson (for
k=0) and its Henneaux-Teitelboim generalisation to k>0. For all k, but focusing
on the 6D case, we present a simple Lorentz-invariant Hamiltonian model that
realizes case (ii), and we derive it from Siegel's manifestly Lorentz invariant
Lagrangian formulation.


title: Sequential Point Clouds: A Survey

abstract:
  Point cloud has drawn more and more research attention as well as real-world
applications. However, many of these applications (e.g. autonomous driving and
robotic manipulation) are actually based on sequential point clouds (i.e. four
dimensions) because the information of the static point cloud data could
provide is still limited. Recently, researchers put more and more effort into
sequential point clouds. This paper presents an extensive review of the deep
learning-based methods for sequential point cloud research including dynamic
flow estimation, object detection \& tracking, point cloud segmentation, and
point cloud forecasting. This paper further summarizes and compares the
quantitative results of the reviewed methods over the public benchmark
datasets. Finally, this paper is concluded by discussing the challenges in the
current sequential point cloud research and pointing out insightful potential
future research directions.


title:
Control of magnetic susceptibility of probiotic strain Lactobacillus
  Rhamnosus GG

abstract:
  The paper investigates the increase in the natural magnetically controlled
properties of probiotic microorganisms Lactobacillus rhamnosus GG (LGG) and
their ability to form magnetic sensitive inclusions (MsI). The magnetic
susceptibility of LGG was increased by modifying the nutrient medium
composition and by cultivating the probiotic culture in a constant magnetic
field. Therefore, this study can be useful for their further use as
magnetically controlled vectors, since it is being extensively researched for
the use in targeted drug delivery in cancer treatment, for the prevention of
chemotherapy complications, etc. The results of this study indicate that
growing microorganisms with natural magnetically-guided properties in a
modified medium with iron chelate and in an external magnetic field leads to an
increase in the magnetic susceptibility of LGG by 1,8 and 2,6 times,
respectively, compared with the control. The best magnetic susceptibility was
recorded for LGG suspensions, which were grown in a constant magnetic field and
cultured in modified medium with iron chelate. The LGG suspension, grown both
in a constant magnetic field and on a modified medium with iron chelate, had
the highest magnetic susceptibility and was 4.8 times larger than the control.


title:
Energy-dependent flavor ratios, cascade/track spectrum tension and
  high-energy neutrinos from magnetospheres of supermassive black holes

abstract:
  The IceCube neutrino observatory measures the diffuse flux of high-energy
astrophysical neutrinos by means of various techniques, and there exists a mild
tension between spectra obtained in different analyses. The spectrum derived
from reconstruction of muon tracks is harder than that from cascades, dominated
by electron and tau neutrinos. If confirmed, this tension may provide a clue to
the origin of these neutrinos, which remains uncertain. Here we investigate the
possibility that this tension may be caused by the change of the flavor content
of astrophysical neutrinos with energy. We assume that at higher energies, the
flux contains more muon neutrinos than expected in the usually assumed flavor
equipartition. This may happen if the neutrinos are produced in regions of the
magnetic field so strong that muons, born in pi-meson decays, cool by
synchrotron radiation faster than decay. The magnetic field of $\sim 10^4 G$ is
required for this mechanism to be relevant for the IceCube results. We note
that these field values are reachable in the immediate vicinity of supermassive
black holes in active galactic nuclei and present a working toy model of the
population of these potential neutrino sources. While this model predicts the
required flavor ratios and describes the high-energy spectrum, it needs an
additional component to explain the observed neutrino flux at lower energies.


title:
On the construction of equivolume partitions of the $d-$dimensional unit
  cube

abstract:
  The aim of this note is to construct equivolume partitions of the
$d$-dimensional unit cube with hyperplanes that are orthogonal to the main
diagonal of the cube. Each such hyperplane $H_r \subset \mathbb{R}^d$ is
defined via the equation $x_1 + \ldots + x_d + r=0$ for a parameter $r$ with
$-d\leq r \leq 0$. For a given $N$, we characterise those real numbers $r_i$
with $1\leq i \leq N-1$ for which the corresponding hyperplanes $H_{r_i}$
partition the unit cube into $N$ sets of volume $1/N$. As a main result we
derive an algebraic and a probabilistic characterisation of the $r_i$ for
arbitrary $d\geq 2$ and $N \in \mathbb{N}$. Importantly, our results do not
only work for equivolume partitions but also for arbitrary predefined
distributions of volume among the $N$ sets of the partition generated from
hyperplanes $H_r$.


title: OutCast: Outdoor Single-image Relighting with Cast Shadows

abstract:
  We propose a relighting method for outdoor images. Our method mainly focuses
on predicting cast shadows in arbitrary novel lighting directions from a single
image while also accounting for shading and global effects such the sun light
color and clouds. Previous solutions for this problem rely on reconstructing
occluder geometry, e.g. using multi-view stereo, which requires many images of
the scene. Instead, in this work we make use of a noisy off-the-shelf
single-image depth map estimation as a source of geometry. Whilst this can be a
good guide for some lighting effects, the resulting depth map quality is
insufficient for directly ray-tracing the shadows. Addressing this, we propose
a learned image space ray-marching layer that converts the approximate depth
map into a deep 3D representation that is fused into occlusion queries using a
learned traversal. Our proposed method achieves, for the first time,
state-of-the-art relighting results, with only a single image as input. For
supplementary material visit our project page at:
https://dgriffiths.uk/outcast.


title:
Disconnected contributions to the magnetic polarisability of the neutral
  pion

abstract:
  The magnetic polarisability of the neutral pion has been calculated using the
background field method in lattice QCD. These early results do not consider the
effect of the disconnected loop contractions arising from the breaking of
charge symmetry in a background magnetic field. Recent work in chiral
perturbation theory has shown that these quark-self-annihilation contractions
provide the leading loop-order contributions to the magnetic polarisability. A
first investigation of these contractions in a background magnetic field is
presented.


title:
Utilizing unsupervised learning to improve sward content prediction and
  herbage mass estimation

abstract:
  Sward species composition estimation is a tedious one. Herbage must be
collected in the field, manually separated into components, dried and weighed
to estimate species composition. Deep learning approaches using neural networks
have been used in previous work to propose faster and more cost efficient
alternatives to this process by estimating the biomass information from a
picture of an area of pasture alone. Deep learning approaches have, however,
struggled to generalize to distant geographical locations and necessitated
further data collection to retrain and perform optimally in different climates.
In this work, we enhance the deep learning solution by reducing the need for
ground-truthed (GT) images when training the neural network. We demonstrate how
unsupervised contrastive learning can be used in the sward composition
prediction problem and compare with the state-of-the-art on the publicly
available GrassClover dataset collected in Denmark as well as a more recent
dataset from Ireland where we tackle herbage mass and height estimation.


title:
Cyber-Forensic Review of Human Footprint and Gait for Personal
  Identification

abstract:
  The human footprint is having a unique set of ridges unmatched by any other
human being, and therefore it can be used in different identity documents for
example birth certificate, Indian biometric identification system AADHAR card,
driving license, PAN card, and passport. There are many instances of the crime
scene where an accused must walk around and left the footwear impressions as
well as barefoot prints and therefore, it is very crucial to recovering the
footprints from identifying the criminals. Footprint-based biometric is a
considerably newer technique for personal identification. Fingerprints, retina,
iris and face recognition are the methods most useful for attendance record of
the person. This time the world is facing the problem of global terrorism. It
is challenging to identify the terrorist because they are living as regular as
the citizens do. Their soft target includes the industries of special interests
such as defence, silicon and nanotechnology chip manufacturing units, pharmacy
sectors. They pretend themselves as religious persons, so temples and other
holy places, even in markets is in their targets. These are the places where
one can obtain their footprints quickly. The gait itself is sufficient to
predict the behaviour of the suspects. The present research is driven to
identify the usefulness of footprint and gait as an alternative to personal
identification.


title: Online Caching with no Regret: Optimistic Learning via Recommendations

abstract:
  The design of effective online caching policies is an increasingly important
problem for content distribution networks, online social networks and edge
computing services, among other areas. This paper proposes a new algorithmic
toolbox for tackling this problem through the lens of optimistic online
learning. We build upon the Follow-the-Regularized-Leader (FTRL) framework,
which is developed further here to include predictions for the file requests,
and we design online caching algorithms for bipartite networks with fixed-size
caches or elastic leased caches subject to time-average budget constraints. The
predictions are provided by a content recommendation system that influences the
users viewing activity and hence can naturally reduce the caching network's
uncertainty about future requests. We also extend the framework to learn and
utilize the best request predictor in cases where many are available. We prove
that the proposed {optimistic} learning caching policies can achieve sub-zero
performance loss (regret) for perfect predictions, and maintain the sub-linear
regret bound $O(\sqrt T)$, which is the best achievable bound for policies that
do not use predictions, even for arbitrary-bad predictions. The performance of
the proposed algorithms is evaluated with detailed trace-driven numerical
tests.