title: A Primer on the Lagrangian Picture of Transport in Porous Media

abstract:
  The dominant conceptual paradigm of porous media flow, solute mixing and
transport is based on steady two-dimensional (2D) flows that can preclude
important transport dynamics. Novel transport phenomena can arise in unsteady
and/or three-dimensional (3D) flows at the pore- or Darcy- scale which can only
be truly understood in the Lagrangian frame. These Lagrangian kinematics are
governed by Lagrangian coherent structures (LCSs) that include chaotic mixing
regions, hold-up regions and transport barriers that are only beginning to be
visualised by novel experimental methods. In this primer we review the
Lagrangian picture of porous media flow and transport and connect the
associated tools and techniques with the latest research findings from pore to
Darcy scales. This primer provides an introduction to the tools for porous
media researchers to know when to expect complex Lagrangian kinematics, how to
uncover and understand LCSs and their impact on solute transport, and how to
exploit these dynamics to control solute transport in engineered subsurface
flows.


title: Missing Value Knockoffs

abstract:
  One limitation of the most statistical/machine learning-based variable
selection approaches is their inability to control the false selections. A
recently introduced framework, model-x knockoffs, provides that to a wide range
of models but lacks support for datasets with missing values. In this work, we
discuss ways of preserving the theoretical guarantees of the model-x framework
in the missing data setting. First, we prove that posterior sampled imputation
allows reusing existing knockoff samplers in the presence of missing values.
Second, we show that sampling knockoffs only for the observed variables and
applying univariate imputation also preserves the false selection guarantees.
Third, for the special case of latent variable models, we demonstrate how
jointly imputing and sampling knockoffs can reduce the computational
complexity. We have verified the theoretical findings with two different
exploratory variable distributions and investigated how the missing data
pattern, amount of correlation, the number of observations, and missing values
affected the statistical power.


title:
Deep Depth from Focal Stack with Defocus Model for Camera-Setting
  Invariance

abstract:
  We propose a learning-based depth from focus/defocus (DFF), which takes a
focal stack as input for estimating scene depth. Defocus blur is a useful cue
for depth estimation. However, the size of the blur depends on not only scene
depth but also camera settings such as focus distance, focal length, and
f-number. Current learning-based methods without any defocus models cannot
estimate a correct depth map if camera settings are different at training and
test times. Our method takes a plane sweep volume as input for the constraint
between scene depth, defocus images, and camera settings, and this intermediate
representation enables depth estimation with different camera settings at
training and test times. This camera-setting invariance can enhance the
applicability of learning-based DFF methods. The experimental results also
indicate that our method is robust against a synthetic-to-real domain gap, and
exhibits state-of-the-art performance.


title: Automated Identification of Toxic Code Reviews: How Far Can We Go?

abstract:
  Toxic conversations during software development interactions may have serious
repercussions on a Free and Open Source Software (FOSS) development project.
For example, victims of toxic conversations may become afraid to express
themselves, therefore get demotivated, and may eventually leave the project.
Automated filtering of toxic conversations may help a FOSS community to
maintain healthy interactions among its members. However, off-the-shelf
toxicity detectors perform poorly on Software Engineering (SE) dataset, such as
one curated from code review comments. To encounter this challenge, we present
ToxiCR, a supervised learning-based toxicity identification tool for code
review interactions. ToxiCR includes a choice to select one of the ten
supervised learning algorithms, an option to select text vectorization
techniques, five mandatory and three optional SE domain specific processing
steps, and a large scale labeled dataset of 19,571 code review comments. With
our rigorous evaluation of the models with various combinations of
preprocessing steps and vectorization techniques, we have identified the best
combination for our dataset that boosts 95.8% accuracy and 88.9% F1 score.
ToxiCR significantly outperforms existing toxicity detectors on our dataset. We
have released our dataset, pretrained models, evaluation results, and source
code publicly available at: https://github.com/WSU-SEAL/ToxiCR.


title:
Initialization of Latent Space Coordinates via Random Linear Projections
  for Learning Robotic Sensory-Motor Sequences

abstract:
  Robot kinematics data, despite being a high dimensional process, is highly
correlated, especially when considering motions grouped in certain primitives.
These almost linear correlations within primitives allow us to interpret the
motions as points drawn close to a union of low-dimensional linear subspaces in
the space of all motions. Motivated by results of embedding theory, in
particular, generalizations of Whitney embedding theorem, we show that random
linear projection of motor sequences into low dimensional space loses very
little information about structure of kinematics data. Projected points are
very good initial guess for values of latent variables in generative model for
robot sensory-motor behaviour primitives. We conducted series of experiments
where we trained a recurrent neural network to generate sensory-motor sequences
for robotic manipulator with 9 degrees of freedom. Experimental results
demonstrate substantial improvement in generalisation abilities for unobserved
samples in the case of initialization of latent variables with random linear
projection of motor data over initialization with zero or random values.
Moreover, latent space is well-structured wherein samples belonging to
different primitives are well separated from the onset of training process.


title: Random Access with Massive MIMO-OTFS in LEO Satellite Communications

abstract:
  This paper considers the joint channel estimation and device activity
detection in the grant-free random access systems, where a large number of
Internet-of-Things devices intend to communicate with a low-earth orbit
satellite in a sporadic way. In addition, the massive multiple-input
multiple-output (MIMO) with orthogonal time-frequency space (OTFS) modulation
is adopted to combat the dynamics of the terrestrial-satellite link. We first
analyze the input-output relationship of the single-input single-output OTFS
when the large delay and Doppler shift both exist, and then extend it to the
grant-free random access with massive MIMO-OTFS. Next, by exploring the
sparsity of channel in the delay-Doppler-angle domain, a two-dimensional
pattern coupled hierarchical prior with the sparse Bayesian learning and
covariance-free method (TDSBL-FM) is developed for the channel estimation.
Then, the active devices are detected by computing the energy of the estimated
channel. Finally, the generalized approximate message passing algorithm
combined with the sparse Bayesian learning and two-dimensional convolution
(ConvSBL-GAMP) is proposed to decrease the computations of the TDSBL-FM
algorithm. Simulation results demonstrate that the proposed algorithms
outperform conventional methods.


title: Variational Inference with Gaussian Mixture by Entropy Approximation

abstract:
  Variational inference is a technique for approximating intractable posterior
distributions in order to quantify the uncertainty of machine learning.
Although the unimodal Gaussian distribution is usually chosen as a parametric
distribution, it hardly approximates the multimodality. In this paper, we
employ the Gaussian mixture distribution as a parametric distribution. A main
difficulty of variational inference with the Gaussian mixture is how to
approximate the entropy of the Gaussian mixture. We approximate the entropy of
the Gaussian mixture as the sum of the entropy of the unimodal Gaussian, which
can be analytically calculated. In addition, we theoretically analyze the
approximation error between the true entropy and approximated one in order to
reveal when our approximation works well. Specifically, the approximation error
is controlled by the ratios of the distances between the means to the sum of
the variances of the Gaussian mixture, and it converges to zero when the ratios
go to infinity. This situation seems to be more likely to occur in higher
dimensional weight spaces because of the curse of dimensionality. Therefore,
our result guarantees that our approximation works well, for example, in neural
networks that assume a large number of weights.


title: Graph Attention Retrospective

abstract:
  Graph-based learning is a rapidly growing sub-field of machine learning with
applications in social networks, citation networks, and bioinformatics. One of
the most popular type of models is graph attention networks. These models were
introduced to allow a node to aggregate information from the features of
neighbor nodes in a non-uniform way in contrast to simple graph convolution
which does not distinguish the neighbors of a node. In this paper, we study
theoretically this expected behaviour of graph attention networks. We prove
multiple results on the performance of the graph attention mechanism for the
problem of node classification for a contextual stochastic block model. Here
the features of the nodes are obtained from a mixture of Gaussians and the
edges from a stochastic block model where the features and the edges are
coupled in a natural way. First, we show that in an "easy" regime, where the
distance between the means of the Gaussians is large enough, graph attention
maintains the weights of intra-class edges and significantly reduces the
weights of the inter-class edges. As a corollary, we show that this implies
perfect node classification independent of the weights of inter-class edges.
However, a classical argument shows that in the "easy" regime, the graph is not
needed at all to classify the data with high probability. In the "hard" regime,
we show that every attention mechanism fails to distinguish intra-class from
inter-class edges. We evaluate our theoretical results on synthetic and
real-world data.


title:
The expected degree of noninvertibility of compositions of functions and
  a related combinatorial identity

abstract:
  Recently, Defant and Propp [2020] defined the degree of noninvertibility of a
function $f\colon X\to Y$ between two finite nonempty sets by
$\text{deg}(f)=\frac{1}{|X|}\sum_{x\in X}|f^{-1}(f(x))|$. We obtain an exact
formula for the expected degree of noninvertibility of the composition of $t$
functions for every $t\in \mathbb{N}$. An equivalent formulation for the
definition of the degree of noninvertibility is then the starting point for a
generalization yielding a seemingly new combinatorial identity involving the
Stirling transform of the signed Stirling numbers of the first kind.


title:
Collision-free Path Planning on Arbitrary Optimization Criteria in the
  Latent Space through cGANs

abstract:
  We propose a new method for collision-free path planning by Conditional
Generative Adversarial Networks (cGANs) by mapping its latent space to only the
collision-free areas of the robot joint space when an obstacle map is given as
a condition. When manipulating a robot arm, it is necessary to generate a
trajectory that avoids contact with the robot itself or the surrounding
environment for safety reasons, and it is convenient to generate multiple
arbitrary trajectories appropriate for respective purposes. In the proposed
method, various trajectories to avoid obstacles can be generated by connecting
the start and goal with arbitrary line segments in this latent space. Our
method simply provides this collision-free latent space after which any
planner, using any optimization conditions, can be used to generate the most
suitable paths on the fly. We successfully verified this method with a
simulated and actual UR5e 6-DoF robotic arm. We confirmed that different
trajectories can be generated according to different optimization conditions.


title: Reheating and Dark Matter Freeze-in in the Higgs-$R^2$ Inflation Model

abstract:
  We study the post-inflationary dynamics for reheating and freeze-in dark
matter in the Higgs-$R^2$ inflation model. Taking the perturbative approach for
reheating, we determine the evolution of the temperature for radiation bath
produced during reheating and determine the maximum and reheating temperatures
of the Universe. Adopting a singlet scalar dark matter with a conformal
non-minimal coupling and a vanishing Higgs-portal coupling, we discuss the
freeze-in production of dark matter both from the non-thermal scattering during
reheating and the thermal scattering after reheating. We find that thermal
scattering is dominant for dark matter production in our model due to the high
reheating temperature. The reheating temperature in our model is determined
dominantly by the Higgs condensate to be up to about $10^{14}\,{\rm GeV}$ and
dark matter with masses up to about $10^9\,{\rm GeV}$ can be produced with a
correct relic density.


title: Watch Me Calibrate My Force-Sensing Shoes!

abstract:
  This letter presents a novel method for smaller-sized humanoid robots to
self-calibrate their foot force sensors. This method consists of two steps: 1.
The robot is commanded to move along several planned whole-body trajectories.
2. A nonlinear least-squares technique is implemented to determine sensor
parameters by minimizing the error between the measured and modeled center of
pressure (CoP) and ground reaction force (GRF) in the robot's movement. This is
the first proposed autonomous calibration method for foot force sensors in
humanoid robots. Additionally, a manual calibration method is developed to
improve the CoP measurement accuracy, which establishes the ground truth for
evaluating the self-calibration approach. The manual calibration and the
self-calibration are implemented on our previously presented force-sensing
shoes. The results show that the self-calibration method, without any manual
intervention, can accurately estimate CoP and GRF.


title: An End-to-End Transformer Model for Crowd Localization

abstract:
  Crowd localization, predicting head positions, is a more practical and
high-level task than simply counting. Existing methods employ pseudo-bounding
boxes or pre-designed localization maps, relying on complex post-processing to
obtain the head positions. In this paper, we propose an elegant, end-to-end
Crowd Localization TRansformer named CLTR that solves the task in the
regression-based paradigm. The proposed method views the crowd localization as
a direct set prediction problem, taking extracted features and trainable
embeddings as input of the transformer-decoder. To achieve good matching
results, we introduce a KMO-based Hungarian, which innovatively revisits the
label assignment from a context view instead of an independent instance view.
Extensive experiments conducted on five datasets in various data settings show
the effectiveness of our method. In particular, the proposed method achieves
the best localization performance on the NWPU-Crowd, UCF-QNRF, and ShanghaiTech
Part A datasets.


title: Revisiting Over-Smoothness in Text to Speech

abstract:
  Non-autoregressive text to speech (NAR-TTS) models have attracted much
attention from both academia and industry due to their fast generation speed.
One limitation of NAR-TTS models is that they ignore the correlation in time
and frequency domains while generating speech mel-spectrograms, and thus cause
blurry and over-smoothed results. In this work, we revisit this over-smoothing
problem from a novel perspective: the degree of over-smoothness is determined
by the gap between the complexity of data distributions and the capability of
modeling methods. Both simplifying data distributions and improving modeling
methods can alleviate the problem. Accordingly, we first study methods reducing
the complexity of data distributions. Then we conduct a comprehensive study on
NAR-TTS models that use some advanced modeling methods. Based on these studies,
we find that 1) methods that provide additional condition inputs reduce the
complexity of data distributions to model, thus alleviating the over-smoothing
problem and achieving better voice quality. 2) Among advanced modeling methods,
Laplacian mixture loss performs well at modeling multimodal distributions and
enjoys its simplicity, while GAN and Glow achieve the best voice quality while
suffering from increased training or model complexity. 3) The two categories of
methods can be combined to further alleviate the over-smoothness and improve
the voice quality. 4) Our experiments on the multi-speaker dataset lead to
similar conclusions as above and providing more variance information can reduce
the difficulty of modeling the target data distribution and alleviate the
requirements for model capacity.


title: A Robust Document Image Watermarking Scheme using Deep Neural Network

abstract:
  Watermarking is an important copyright protection technology which generally
embeds the identity information into the carrier imperceptibly. Then the
identity can be extracted to prove the copyright from the watermarked carrier
even after suffering various attacks. Most of the existing watermarking
technologies take the nature images as carriers. Different from the natural
images, document images are not so rich in color and texture, and thus have
less redundant information to carry watermarks. This paper proposes an
end-to-end document image watermarking scheme using the deep neural network.
Specifically, an encoder and a decoder are designed to embed and extract the
watermark. A noise layer is added to simulate the various attacks that could be
encountered in reality, such as the Cropout, Dropout, Gaussian blur, Gaussian
noise, Resize, and JPEG Compression. A text-sensitive loss function is designed
to limit the embedding modification on characters. An embedding strength
adjustment strategy is proposed to improve the quality of watermarked image
with little loss of extraction accuracy. Experimental results show that the
proposed document image watermarking technology outperforms three
state-of-the-arts in terms of the robustness and image quality.


title:
Long-time dynamics of a nonlocal epidemic model with free boundaries:
  spreading-vanishing dichotomy

abstract:
  In this paper, we examine the long-time dynamics of an epidemic model whose
diffusion and reaction terms involve nonlocal effects described by suitable
convolution operators.The spreading front of the disease is represented by the
free boundaries in the model. We show that the model is well-posed, its
long-time dynamical behaviour is characterised by a spreading-vanishing
dichotomy, and we also obtain sharp criteria to determine the dichotomy. Some
of the nonlocal effects in the model pose extra difficulties in the
mathematical treatment, which are dealt with by introducing new approaches. The
model can capture accelerated spreading, and its spreading rate will be
discussed in a subsequent work.


title:
2D magnetoelectric multiferroics in MnSTe/In2Se3 heterobilayer with
  ferroelectrically controllable skyrmions

abstract:
  The magnetoelectric effect and skyrmions are two fundamental phenomena in the
field of condensed-matter physics. Here, using first-principles calculations
and Monte-Carlo simulations, we propose that strong magnetoelectric coupling
can be demonstrated in a multiferroic heterobilayer consisting of
two-dimensional (2D) MnSTe and {\alpha}-In2Se3. As the electric polarization in
ferroelectric {\alpha}-In2Se3 is switched, the creation and annihilation of
topological magnetic phase can be achieved in this multiferroic heterobilayer,
giving rise to the intriguing ferroelectrically controllable skyrmions. This
feature is further revealed to be closely related to the physical quantity of
D2/|KJ|, which is generally applicable for describing the required conditions
of such physics. Moreover, the evaluations of their topological magnetic phases
with temperature are systematically discussed. These insights not only greatly
enrich the research on 2D magnetoelectric multiferroics, but also pave a
promising avenue to realize new skyrmionic device concepts.


title: Phenomenology of Trimaximal mixing with one texture equality

abstract:
  We study neutrino mass matrices with one texture equality and the neutrino
mixing matrix having either its first (TM$_1$) or second (TM$_2$) column
identical to that of the tribimaximal mixing matrix. We found that out of total
fifteen possible neutrino mass matrices with one texture equality, only seven
textures are compatible with $TM_1$ mixing and six textures are compatible with
$TM_2$ mixing in the light of the current neutrino oscillation data. These
textures have interesting implications for the presently unknown parameters
such as the neutrino mass scale, effective Majorana neutrino mass, the
atmospheric mixing and the Dirac- and Majorana-type CP violating phases. We,
also, present the $S_{3}$ group motivation for some of these textures.


title: Uncertainty-Aware Deep Multi-View Photometric Stereo

abstract:
  This paper presents a simple and effective solution to the longstanding
classical multi-view photometric stereo (MVPS) problem. It is well-known that
photometric stereo (PS) is excellent at recovering high-frequency surface
details, whereas multi-view stereo (MVS) can help remove the low-frequency
distortion due to PS and retain the global geometry of the shape. This paper
proposes an approach that can effectively utilize such complementary strengths
of PS and MVS. Our key idea is to combine them suitably while considering the
per-pixel uncertainty of their estimates. To this end, we estimate per-pixel
surface normals and depth using an uncertainty-aware deep-PS network and
deep-MVS network, respectively. Uncertainty modeling helps select reliable
surface normal and depth estimates at each pixel which then act as a true
representative of the dense surface geometry. At each pixel, our approach
either selects or discards deep-PS and deep-MVS network prediction depending on
the prediction uncertainty measure. For dense, detailed, and precise inference
of the object's surface profile, we propose to learn the implicit neural shape
representation via a multilayer perceptron (MLP). Our approach encourages the
MLP to converge to a natural zero-level set surface using the confident
prediction from deep-PS and deep-MVS networks, providing superior dense surface
reconstruction. Extensive experiments on the DiLiGenT-MV benchmark dataset show
that our method provides high-quality shape recovery with a much lower memory
footprint while outperforming almost all of the existing approaches.


title: Adversarial Contrastive Self-Supervised Learning

abstract:
  Recently, learning from vast unlabeled data, especially self-supervised
learning, has been emerging and attracted widespread attention. Self-supervised
learning followed by the supervised fine-tuning on a few labeled examples can
significantly improve label efficiency and outperform standard supervised
training using fully annotated data. In this work, we present a novel
self-supervised deep learning paradigm based on online hard negative pair
mining. Specifically, we design a student-teacher network to generate
multi-view of the data for self-supervised learning and integrate hard negative
pair mining into the training. Then we derive a new triplet-like loss
considering both positive sample pairs and mined hard negative sample pairs.
Extensive experiments demonstrate the effectiveness of the proposed method and
its components on ILSVRC-2012.


title: Global Instance Tracking: Locating Target More Like Humans

abstract:
  Target tracking, the essential ability of the human visual system, has been
simulated by computer vision tasks. However, existing trackers perform well in
austere experimental environments but fail in challenges like occlusion and
fast motion. The massive gap indicates that researches only measure tracking
performance rather than intelligence. How to scientifically judge the
intelligence level of trackers? Distinct from decision-making problems, lacking
three requirements (a challenging task, a fair environment, and a scientific
evaluation procedure) makes it strenuous to answer the question. In this
article, we first propose the global instance tracking (GIT) task, which is
supposed to search an arbitrary user-specified instance in a video without any
assumptions about camera or motion consistency, to model the human visual
tracking ability. Whereafter, we construct a high-quality and large-scale
benchmark VideoCube to create a challenging environment. Finally, we design a
scientific evaluation procedure using human capabilities as the baseline to
judge tracking intelligence. Additionally, we provide an online platform with
toolkit and an updated leaderboard. Although the experimental results indicate
a definite gap between trackers and humans, we expect to take a step forward to
generate authentic human-like trackers. The database, toolkit, evaluation
server, and baseline results are available at http://videocube.aitestunion.com.


title: Neuro-Inspired Deep Neural Networks with Sparse, Strong Activations

abstract:
  While end-to-end training of Deep Neural Networks (DNNs) yields state of the
art performance in an increasing array of applications, it does not provide
insight into, or control over, the features being extracted. We report here on
a promising neuro-inspired approach to DNNs with sparser and stronger
activations. We use standard stochastic gradient training, supplementing the
end-to-end discriminative cost function with layer-wise costs promoting Hebbian
("fire together," "wire together") updates for highly active neurons, and
anti-Hebbian updates for the remaining neurons. Instead of batch norm, we use
divisive normalization of activations (suppressing weak outputs using strong
outputs), along with implicit $\ell_2$ normalization of neuronal weights.
Experiments with standard image classification tasks on CIFAR-10 demonstrate
that, relative to baseline end-to-end trained architectures, our proposed
architecture (a) leads to sparser activations (with only a slight compromise on
accuracy), (b) exhibits more robustness to noise (without being trained on
noisy data), (c) exhibits more robustness to adversarial perturbations (without
adversarial training).


title: Non-isothermal non-Newtonian fluids: the stationary case

abstract:
  The stationary Navier-Stokes equations for a non-Newtonian incompressible
fluid are coupled with the stationary heat equation and subject to Dirichlet
type boundary conditions. The viscosity is supposed to depend on the
temperature and the stress depends on the strain through a suit-able power law
depending on $p \in (1,2)$ (shear thinning case). For this problem we establish
the existence of a weak solution as well as we prove some regularity results
both for the Navier-Stokes and the Stokes cases.Then, the latter case with the
Carreau power law is approximated through a FEM scheme and some error estimates
are obtained. Such estimates are then validated through some two-dimensional
numerical experiments.


title: Utility and Feasibility of a Center Surround Event Camera

abstract:
  Standard dynamic vision sensor (DVS) event cameras output a stream of
spatially-independent log-intensity brightness change events so they cannot
suppress spatial redundancy. Nearly all biological retinas use an antagonistic
center-surround organization. This paper proposes a practical method of
implementing a compact, energy-efficient Center Surround DVS (CSDVS) with a
surround smoothing network that uses compact polysilicon resistors for lateral
resistance. The paper includes behavioral simulation results for the CSDVS (see
sites.google.com/view/csdvs/home). The CSDVS would significantly reduce events
caused by low spatial frequencies, but amplify the informative high frequency
spatiotemporal events.


title:
An insight into the $q$-difference two-dimensional Toda lattice
  equation, $q$-difference sine-Gordon equation and their integrability

abstract:
  In our previous work \cite{LNS}, we constructed quasi-Casoratian solutions to
the noncommutative $q$-difference two-dimensional Toda lattice ($q$-2DTL)
equation by Darboux transformation, which we can prove produces the existing
Casoratian solutions to the bilinear $q$-2DTL equation obtained by Hirota's
bilinear method in commutative setting. It is actually true that one can not
only construct solutions to soliton equations but also solutions to their
corresponding B$\ddot{a}$cklund transformations by their Darboux
transformations and binary Darboux transformations. To be more specific,
eigenfunctions produced by iterating Darboux transformations and binary Darboux
transformations for soliton equations give nothing but determinant solutions to
their B$\ddot{a}$cklund transformations, individually. This reveals the
profound connections between Darboux transformations and Hirota's bilinear
method. In this paper, we shall expound this viewpoint in the case of the
$q$-2DTL equation. First, we derive a generalized bilinear B$\ddot{a}$cklund
transformation and thus a generalized Lax pair for the bilinear $q$-2DTL
equation. And then we successfully construct the binary Darboux transformation
for the $q$-2DTL equation, based on which, Grammian solutions expressed in
terms of quantum integrals are established for both the bilinear $q$-2DTL
equation and its bilinear B$\ddot{a}$cklund transformation. In the end, by
imposing the 2-periodic reductions on the corresponding results of the $q$-2DTL
equation, we derive a $q$-difference sine-Gordon equation, a modified
$q$-difference sine-Gordon equation and obtain their corresponding solutions.


title:
SWIS: Self-Supervised Representation Learning For Writer Independent
  Offline Signature Verification

abstract:
  Writer independent offline signature verification is one of the most
challenging tasks in pattern recognition as there is often a scarcity of
training data. To handle such data scarcity problem, in this paper, we propose
a novel self-supervised learning (SSL) framework for writer independent offline
signature verification. To our knowledge, this is the first attempt to utilize
self-supervised setting for the signature verification task. The objective of
self-supervised representation learning from the signature images is achieved
by minimizing the cross-covariance between two random variables belonging to
different feature directions and ensuring a positive cross-covariance between
the random variables denoting the same feature direction. This ensures that the
features are decorrelated linearly and the redundant information is discarded.
Through experimental results on different data sets, we obtained encouraging
results.


title:
Bi-directional Joint Neural Networks for Intent Classification and Slot
  Filling

abstract:
  Intent classification and slot filling are two critical tasks for natural
language understanding. Traditionally the two tasks proceeded independently.
However, more recently joint models for intent classification and slot filling
have achieved state-of-the-art performance, and have proved that there exists a
strong relationship between the two tasks. In this paper, we propose a
bi-directional joint model for intent classification and slot filling, which
includes a multi-stage hierarchical process via BERT and bi-directional joint
natural language understanding mechanisms, including intent2slot and
slot2intent, to obtain mutual performance enhancement between intent
classification and slot filling. The evaluations show that our model achieves
state-of-the-art results on intent classification accuracy, slot filling F1,
and significantly improves sentence-level semantic frame accuracy when applied
to publicly available benchmark datasets, ATIS (88.6%) and SNIPS (92.8%).


title: Improved Hard Example Mining Approach for Single Shot Object Detectors

abstract:
  Hard example mining methods generally improve the performance of the object
detectors, which suffer from imbalanced training sets. In this work, two
existing hard example mining approaches (LRM and focal loss, FL) are adapted
and combined in a state-of-the-art real-time object detector, YOLOv5. The
effectiveness of the proposed approach for improving the performance on hard
examples is extensively evaluated. The proposed method increases mAP by 3%
compared to using the original loss function and around 1-2% compared to using
the hard-mining methods (LRM or FL) individually on 2021 Anti-UAV Challenge
Dataset.


title:
An Improved Deep Learning Approach For Product Recognition on Racks in
  Retail Stores

abstract:
  Automated product recognition in retail stores is an important real-world
application in the domain of Computer Vision and Pattern Recognition. In this
paper, we consider the problem of automatically identifying the classes of the
products placed on racks in retail stores from an image of the rack and
information about the query/product images. We improve upon the existing
approaches in terms of effectiveness and memory requirement by developing a
two-stage object detection and recognition pipeline comprising of a
Faster-RCNN-based object localizer that detects the object regions in the rack
image and a ResNet-18-based image encoder that classifies the detected regions
into the appropriate classes. Each of the models is fine-tuned using
appropriate data sets for better prediction and data augmentation is performed
on each query image to prepare an extensive gallery set for fine-tuning the
ResNet-18-based product recognition model. This encoder is trained using a
triplet loss function following the strategy of online-hard-negative-mining for
improved prediction. The proposed models are lightweight and can be connected
in an end-to-end manner during deployment for automatically identifying each
product object placed in a rack image. Extensive experiments using Grozi-32k
and GP-180 data sets verify the effectiveness of the proposed model.


title: A dispersive analysis of low energy pion photo- and electroproduction

abstract:
  A dispersive representation based on unitarity and analyticity is used to
study the low energy $\gamma N \to \pi N\ \text{and}\ \gamma^* N \to \pi N$
partial wave amplitudes. Final state interactions of the $\pi N$ system are
critical to this analysis. The left-hand cut contribution is estimated by
invoking baryon chiral perturbation theory results, while the right-hand cut
contribution responsible for final state interaction effects is taken into
account via an Omn\`{e}s formalism with elastic phase shifts as input. It is
found that a good numerical fit can be achieved with only one subtraction
parameter, and the experimental data of the multipole amplitudes $E_{0+},
S_{0+}$ in the energy region below the $\Delta (1232)$ are well described when
the photon virtuality $Q^2 \leq 0.1 \mathrm{GeV}^2$. Furthermore, we extend the
partial wave amplitudes to the second Riemann sheet to extract the couplings of
the subthreshold resonance $N^*(890)$. Its couplings extracted from the
multipole amplitudes $E_{0+}, S_{0+}$ are comparable to those of the $N^*
(1535)$ resonance.


title: Multi-Level Contrastive Learning for Cross-Lingual Alignment

abstract:
  Cross-language pre-trained models such as multilingual BERT (mBERT) have
achieved significant performance in various cross-lingual downstream NLP tasks.
This paper proposes a multi-level contrastive learning (ML-CTL) framework to
further improve the cross-lingual ability of pre-trained models. The proposed
method uses translated parallel data to encourage the model to generate similar
semantic embeddings for different languages. However, unlike the sentence-level
alignment used in most previous studies, in this paper, we explicitly integrate
the word-level information of each pair of parallel sentences into contrastive
learning. Moreover, cross-zero noise contrastive estimation (CZ-NCE) loss is
proposed to alleviate the impact of the floating-point error in the training
process with a small batch size. The proposed method significantly improves the
cross-lingual transfer ability of our basic model (mBERT) and outperforms on
multiple zero-shot cross-lingual downstream tasks compared to the same-size
models in the Xtreme benchmark.


title: Visual Speech Recognition for Multiple Languages in the Wild

abstract:
  Visual speech recognition (VSR) aims to recognise the content of speech based
on the lip movements without relying on the audio stream. Advances in deep
learning and the availability of large audio-visual datasets have led to the
development of much more accurate and robust VSR models than ever before.
However, these advances are usually due to larger training sets rather than the
model design. In this work, we demonstrate that designing better models is
equally important to using larger training sets. We propose the addition of
prediction-based auxiliary tasks to a VSR model and highlight the importance of
hyper-parameter optimisation and appropriate data augmentations. We show that
such model works for different languages and outperforms all previous methods
trained on publicly available datasets by a large margin. It even outperforms
models that were trained on non-publicly available datasets containing up to to
21 times more data. We show furthermore that using additional training data,
even in other languages or with automatically generated transcriptions, results
in further improvement.


title: Design-based composite estimation of small proportions in small domains

abstract:
  Traditional direct estimation methods are not efficient for domains of a
survey population with small sample sizes. To estimate the domain proportions,
we combine the direct estimators and the regression-synthetic estimators based
on domain-level auxiliary information. For the case of small true proportions,
we introduce the design-based linear combination that is a robust alternative
to the empirical best linear unbiased predictor (EBLUP) based on the
Fay--Herriot model. We also consider an adaptive procedure optimizing a
sample-size-dependent composite estimator, which depends on a single parameter
for all domains. We imitate the Lithuanian Labor Force Survey, where we
estimate the proportions of the unemployed and employed in municipalities. We
show where the considered design-based compositions and estimators of their
mean square errors are competitive for EBLUP and its accuracy estimation.


title: Scattering of fermionic isodoublets on the sine-Gordon kink

abstract:
  The scattering of Dirac fermions on the sine-Gordon kink is studied both
analytically and numerically. To achieve invariance with respect to a discrete
symmetry, the sine-Gordon model is treated as a nonlinear $\sigma$-model with a
circular target space that interacts with fermionic isodublets through the
Yukawa interaction. It is shown that the diagonal and antidiagonal parts of the
fermionic wave function interact independently with the external field of the
sine-Gordon kink. The wave functions of the fermionic scattering states are
expressed in terms of the Heun functions. General expressions for the
transmission and reflection coefficients are derived, and their dependences on
the fermion momentum and mass are studied numerically. The existence condition
is found for two fermionic zero modes, and their analytical expressions are
obtained. It is shown that the zero modes do not lead to fragmentation of the
fermionic charge, but can lead to polarization of the fermionic vacuum. The
scattering of the diagonal and antidiagonal fermionic states is found to be
significantly different; this difference is shown to be due to the different
dependences of the energy levels of these bound states on the fermion mass, and
is in accordance with Levinson's theorem.


title:
Predicting the formation and stability of oxide perovskites by
  extracting underlying mechanisms using machine learning

abstract:
  The optimization of properties of perovskite oxides has drawn interest on
account of their diverse areas of application. In this work, the hierarchical
clustering technique is used to reduce the multi-collinearity among selected
features from literature that are reported to have an effect on perovskite
formation and stability. Operating on the vast composition space of double
oxide perovskite compositions available in literature and online repositories,
in this manuscript, an attempt has been made to extract the relationship
between the composition and structure to predict their formability and
stability. Machine learning (ML) classifiers are trained on these datasets to
predict novel stable perovskite compositions. The study uses a vast feature
space to narrow down the most important factors affecting the formability and
stability in perovskite compounds. It also identifies stable compositions that
have band gaps suitable for photovoltaic and photocatalytic applications. The
developed random forest (RF)-based models may be extended to include the
implications beyond photosensitive applications by focusing on the
physico-chemical mechanisms driving the phenomena behind each application.


title:
Almost Tight Approximation Hardness for Single-Source Directed
  k-Edge-Connectivity

abstract:
  In the $k$-connected directed Steiner tree problem ($k$-DST), we are given an
$n$-vertex directed graph $G=(V,E)$ with edge costs, a connectivity requirement
$k$, a root $r\in V$ and a set of terminals $T\subseteq V$. The goal is to find
a minimum-cost subgraph $H\subseteq G$ that has $k$ internally disjoint paths
from the root vertex $r$ to every terminal $t\in T$.
  In this paper, we show the approximation hardness of $k$-DST for various
parameters, which thus close some long-standing open problems.
  - $\Omega\left(|T|/\log |T|\right)$-approximation hardness, which holds under
the standard assumption $\mathrm{NP}\neq \mathrm{ZPP}$. The inapproximability
ratio is tightened to $\Omega\left(|T|\right)$ under the Strongish Planted
Clique Hypothesis [Manurangsi, Rubinstein and Schramm, ITCS 2021].
  The latter hardness result matches the approximation ratio of $|T|$ obtained
by a trivial approximation algorithm, thus closing the long-standing open
problem.
  - $\Omega\left(\sqrt{2}^k / k\right)$-approximation hardness for the general
case of $k$-DST under the assumption $\mathrm{NP}\neq\mathrm{ZPP}$. This is the
first hardness result known for survivable network design problems with an
inapproximability ratio exponential in $k$.
  - $\Omega\left((k/L)^{L/4}\right)$-approximation hardness for $k$-DST on
$L$-layered graphs for $L\le O\left(\log n\right)$. This almost matches the
approximation ratio of $O(k^{L-1}\cdot L \cdot \log |T|)$ achieving in
$O\left(n^L\right)$-time due to Laekhanukit [ICALP`16].


title: Stable and metastable contract networks

abstract:
  We consider a hypergraph (I,C), with possible multiple (hyper)edges and
loops, in which the vertices $i\in I$ are interpreted as agents, and the edges
$c\in C$ as contracts that can be concluded between agents. The preferences of
each agent i concerning the contracts where i takes part are given by use of a
choice function $f_i$ possessing the so-called path independent property. In
this general setup we introduce the notion of stable network of contracts.
  The paper contains two main results. The first one is that a general problem
on stable systems of contracts for (I,C,f) is reduced to a set of special ones
in which preferences of agents are described by use of so-called weak orders,
or utility functions. However, for a special case of this sort, the stability
may not exist. Trying to overcome this trouble when dealing with such special
cases, we introduce a weaker notion of metastability for systems of contracts.
Our second result is that a metastable system always exists.


title: Disentangling Long and Short-Term Interests for Recommendation

abstract:
  Modeling user's long-term and short-term interests is crucial for accurate
recommendation. However, since there is no manually annotated label for user
interests, existing approaches always follow the paradigm of entangling these
two aspects, which may lead to inferior recommendation accuracy and
interpretability. In this paper, to address it, we propose a Contrastive
learning framework to disentangle Long and Short-term interests for
Recommendation (CLSR) with self-supervision. Specifically, we first propose two
separate encoders to independently capture user interests of different time
scales. We then extract long-term and short-term interests proxies from the
interaction sequences, which serve as pseudo labels for user interests. Then
pairwise contrastive tasks are designed to supervise the similarity between
interest representations and their corresponding interest proxies. Finally,
since the importance of long-term and short-term interests is dynamically
changing, we propose to adaptively aggregate them through an attention-based
network for prediction. We conduct experiments on two large-scale real-world
datasets for e-commerce and short-video recommendation. Empirical results show
that our CLSR consistently outperforms all state-of-the-art models with
significant improvements: GAUC is improved by over 0.01, and NDCG is improved
by over 4%. Further counterfactual evaluations demonstrate that stronger
disentanglement of long and short-term interests is successfully achieved by
CLSR. The code and data are available at
https://github.com/tsinghua-fib-lab/CLSR.


title:
The dimension of Kronheimer-Mrowka instanton homology group for plane
  trivalent graphs

abstract:
  We proved that the dimension of the F-vector space J#(G) for a plane
trivalent graph G, defined by Kronheimer and Mrowka using their SO(3) instanton
Floer homology, is equal to the number of Tait colorings of G.


title:
Investigation of Optimization Techniques on the Elevator Dispatching
  Problem

abstract:
  In the elevator industry, reducing passenger journey time in an elevator
system is a major aim. The key obstacle to optimising elevator dispatching is
the unpredictable traffic flow of passengers. To address this difficulty, two
main features must be optimised: waiting time and journey time. To address the
problem in real time, several strategies are employed, including Simulated
Annealing (SA), Genetic Algorithm (GA), Particle Swarm Optimization Algorithm
(PSO), and Whale Optimization Algorithm (WOA). This research article compares
the algorithms discussed above. To investigate the functioning of the
algorithms for visualisation and insight, a case study was created. In order to
discover the optimum algorithm for the elevator dispatching problem,
performance indices such as average and ideal fitness value are generated in 5
runs to compare the outcomes of the methods. The goal of this study is to
compute a dispatching scheme, which is the result of the algorithms, in order
to lower the average trip time for all passengers. This study builds on
previous studies that recommended ways to reduce waiting time. The proposed
technique reduces average wait time, improves lift efficiency, and improves
customer experience.


title:
Exploring the Impact of Negative Samples of Contrastive Learning: A Case
  Study of Sentence Embedding

abstract:
  Contrastive learning is emerging as a powerful technique for extracting
knowledge from unlabeled data. This technique requires a balanced mixture of
two ingredients: positive (similar) and negative (dissimilar) samples. This is
typically achieved by maintaining a queue of negative samples during training.
Prior works in the area typically uses a fixed-length negative sample queue,
but how the negative sample size affects the model performance remains unclear.
The opaque impact of the number of negative samples on performance when
employing contrastive learning aroused our in-depth exploration. This paper
presents a momentum contrastive learning model with negative sample queue for
sentence embedding, namely MoCoSE. We add the prediction layer to the online
branch to make the model asymmetric and together with EMA update mechanism of
the target branch to prevent the model from collapsing. We define a maximum
traceable distance metric, through which we learn to what extent the text
contrastive learning benefits from the historical information of negative
samples. Our experiments find that the best results are obtained when the
maximum traceable distance is at a certain range, demonstrating that there is
an optimal range of historical information for a negative sample queue. We
evaluate the proposed unsupervised MoCoSE on the semantic text similarity (STS)
task and obtain an average Spearman's correlation of $77.27\%$. Source code is
available at https://github.com/xbdxwyh/mocose.


title:
RIConv++: Effective Rotation Invariant Convolutions for 3D Point Clouds
  Deep Learning

abstract:
  3D point clouds deep learning is a promising field of research that allows a
neural network to learn features of point clouds directly, making it a robust
tool for solving 3D scene understanding tasks. While recent works show that
point cloud convolutions can be invariant to translation and point permutation,
investigations of the rotation invariance property for point cloud convolution
has been so far scarce. Some existing methods perform point cloud convolutions
with rotation-invariant features, existing methods generally do not perform as
well as translation-invariant only counterpart. In this work, we argue that a
key reason is that compared to point coordinates, rotation-invariant features
consumed by point cloud convolution are not as distinctive. To address this
problem, we propose a simple yet effective convolution operator that enhances
feature distinction by designing powerful rotation invariant features from the
local regions. We consider the relationship between the point of interest and
its neighbors as well as the internal relationship of the neighbors to largely
improve the feature descriptiveness. Our network architecture can capture both
local and global context by simply tuning the neighborhood size in each
convolution layer. We conduct several experiments on synthetic and real-world
point cloud classifications, part segmentation, and shape retrieval to evaluate
our method, which achieves the state-of-the-art accuracy under challenging
rotations.


title: Stability of involutions on Banach Algebra by fixed point method

abstract:
  Using the fixed point method of the stability of the Jensen's functional
equation, we proved the Hyers-Ulam-Rassias stability and super stability of
involution on Banach algebra and find some conditions that with them a Banach
algebra with approximate involution is $C^*$-algebra.


title:
Continuous Human Action Recognition for Human-Machine Interaction: A
  Review

abstract:
  With advances in data-driven machine learning research, a wide variety of
prediction models have been proposed to capture spatio-temporal features for
the analysis of video streams. Recognising actions and detecting action
transitions within an input video are challenging but necessary tasks for
applications that require real-time human-machine interaction. By reviewing a
large body of recent related work in the literature, we thoroughly analyse,
explain and compare action segmentation methods and provide details on the
feature extraction and learning strategies that are used on most
state-of-the-art methods. We cover the impact of the performance of object
detection and tracking techniques on human action segmentation methodologies.
We investigate the application of such models to real-world scenarios and
discuss several limitations and key research directions towards improving
interpretability, generalisation, optimisation and deployment.


title:
Language-Independent Speaker Anonymization Approach using
  Self-Supervised Pre-Trained Models

abstract:
  Speaker anonymization aims to protect the privacy of speakers while
preserving spoken linguistic information from speech. Current mainstream neural
network speaker anonymization systems are complicated, containing an F0
extractor, speaker encoder, automatic speech recognition acoustic model (ASR
AM), speech synthesis acoustic model and speech waveform generation model.
Moreover, as an ASR AM is language-dependent, trained on English data, it is
hard to adapt it into another language. In this paper, we propose a simpler
self-supervised learning (SSL)-based method for language-independent speaker
anonymization without any explicit language-dependent model, which can be
easily used for other languages. Extensive experiments were conducted on the
VoicePrivacy Challenge 2020 datasets in English and AISHELL-3 datasets in
Mandarin to demonstrate the effectiveness of our proposed SSL-based
language-independent speaker anonymization method.


title:
Fast and Accurate Data-Driven Simulation Framework for Contact-Intensive
  Tight-Tolerance Robotic Assembly Tasks

abstract:
  We propose a novel fast and accurate simulation framework for
contact-intensive tight-tolerance robotic assembly tasks. The key components of
our framework are as follows: 1) data-driven contact point clustering with a
certain variable-input network, which is explicitly trained for simulation
accuracy (with real experimental data) and able to accommodate
complex/non-convex object shapes; 2) contact force solving, which
precisely/robustly enforces physics of contact (i.e., no penetration, Coulomb
friction, maximum energy dissipation) with contact mechanics of contact nodes
augmented with that of their object; 3) contact detection with a neural
network, which is parallelized for each contact point, thus, can be computed
very quickly even for complex shape objects with no exhaust pair-wise test; and
4) time integration with PMI (passive mid-point integration), whose
discrete-time passivity improves overall simulation accuracy, stability, and
speed. We then implement our proposed framework for two
widely-encountered/benchmarked contact-intensive tight-tolerance tasks, namely,
peg-in-hole assembly and bolt-nut assembly, and validate its speed and accuracy
against real experimental data. It is worthwhile to mention that our proposed
simulation framework is applicable to other general contact-intensive
tight-tolerance robotic assembly tasks as well. We also compare its performance
with other physics engines and manifest its robustness via haptic rendering of
virtual bolting task.


title:
Symmetric Convolutional Filters: A Novel Way to Constrain Parameters in
  CNN

abstract:
  We propose a novel technique to constrain parameters in CNN based on
symmetric filters. We investigate the impact on SOTA networks when varying the
combinations of symmetricity. We demonstrate that our models offer effective
generalisation and a structured elimination of redundancy in parameters. We
conclude by comparing our method with other pruning techniques.


title: Semantic Supervision: Enabling Generalization over Output Spaces

abstract:
  In this paper, we propose Semantic Supervision (SemSup) - a unified paradigm
for training classifiers that generalize over output spaces. In contrast to
standard classification, which treats classes as discrete symbols, SemSup
represents them as dense vector features obtained from descriptions of classes
(e.g., "The cat is a small carnivorous mammal"). This allows the output space
to be unbounded (in the space of descriptions) and enables models to generalize
both over unseen inputs and unseen outputs (e.g. "The aardvark is a nocturnal
burrowing mammal with long ears"). Specifically, SemSup enables four types of
generalization, to -- (1) unseen class descriptions, (2) unseen classes, (3)
unseen super-classes, and (4) unseen tasks. Through experiments on four
classification datasets across two variants (multi-class and multi-label), two
input modalities (text and images), and two output description modalities (text
and JSON), we show that our SemSup models significantly outperform standard
supervised models and existing models that leverage word embeddings over class
names. For instance, our model outperforms baselines by 40% and 15% precision
points on unseen descriptions and classes, respectively, on a news
categorization dataset (RCV1). SemSup can serve as a pathway for scaling neural
models to large unbounded output spaces and enabling better generalization and
model reuse for unseen tasks and domains.


title:
Sustainability using Renewable Electricity (SuRE) towards NetZero
  Emissions

abstract:
  Demand for energy has increased significantly across the globe due to
increase in population and economic growth. Growth in energy demand poses
serious threat to the environment since majority of the energy sources are
non-renewable and based on fossil fuels, which leads to emission of harmful
greenhouse gases. Organizations across the world are facing challenges in
transitioning from fossil fuels-based sources to greener sources to reduce
their carbon footprint. As a step towards achieving Net-Zero emission target,
we present a scalable AI based solution that can be used by organizations to
increase their overall renewable electricity share in total energy consumption.
Our solution provides facilities with accurate energy demand forecast,
recommendation for procurement of renewable electricity to optimize cost and
carbon offset recommendations to compensate for Greenhouse Gas (GHG) emissions.
This solution has been used in production for more than a year for four
facilities and has increased their renewable electricity share significantly.


title:
A local boundedness result for a class of obstacle problems with
  non-standard growth conditions

abstract:
  We prove the local boundedness for solutions to a class of obstacle problems
with non-standard growth conditions. The novelty here is that we are able to
establish the local boundedness under a sharp bound on the gap between the
growth exponents.


title: Transparency Beyond VNP in the Monotone Setting

abstract:
  Recently Hrubes and Yehudayoff (2021) showed a connection between the
monotone algebraic circuit complexity of transparent polynomials and a
geometric complexity measure of their Newton polytope. They then used this
connection to prove lower bounds against monotone VP (mVP). We extend their
work by showing that their technique can be used to prove lower bounds against
classes that are seemingly more powerful than monotone VNP (mVNP).
  In the process, we define a natural monotone analogue of VPSPACE -- a
well-studied class in the non-monotone setting (Poizat 2008, Koiran-Perifel
2009, Malod 2011, Mahajan-Rao 2013) -- and prove an exponential separation
between the computational powers of this class and mVNP.
  To show this separation, we define a new polynomial family with an
interesting combinatorial structure which we use heavily in our lower bound.
Both the polynomial and the combinatorial nature of our proof might be of
independent interest.


title:
Effects of interconnections among corruption, institutional punishment,
  and economic factors for the evolution of cooperation

abstract:
  The view that altruistic punishment plays an important role in supporting
public cooperation among human beings and other species has been widely
accepted by the public. However, the positive role of altruistic punishment in
enhancing cooperation will be undermined if corruption is considered. Recently,
behavioral experiments have confirmed this finding and further investigated the
effects of the leader's punitive power and the economic potential.
Nevertheless, there are relatively few studies focusing on how these factors
affect the evolution of cooperation from a theoretical perspective. Here, we
combine institutional punishment public goods games with bribery games to
investigate the effects of the above factors on the evolution of cooperation.
Theoretical and numerical results reveal that the existence of corruption will
reduce the level of cooperation when cooperators are more inclined to provide
bribes. In addition, we demonstrate that stronger leader and richer economic
potential are both important to enhance cooperation. In particular, when
defectors are more inclined to provide bribes, stronger leaders can sustain the
contributions of public goods from cooperators if the economic potential is
weak.


title:
Python for Smarter Cities: Comparison of Python libraries for static and
  interactive visualisations of large vector data

abstract:
  Local governments, as part of 'smart city' initiatives and to promote
interoperability, are increasingly incorporating open-source software into
their data management, analysis, and visualisation workflows. Python, with its
concise and natural syntax, presents a low barrier to entry for municipal staff
without computer science backgrounds. However, with regard to geospatial
visualisations in particular, the range of available Python libraries has
diversified to such an extent that identifying candidate libraries for specific
use cases is a challenging undertaking. This study therefore assesses
prominent, actively-developed visualisation libraries in the Python ecosystem
with respect to their suitability for producing visualisations of large vector
datasets. A simple visualisation task common in urban development is used to
produce near-identical thematic maps across static and an interactive 'tracks'
of comparison. All short-listed libraries were able to generate the sample map
products for both a small and larger dataset. Code complexity differed more
strongly for interactive visualisations. Formal and informal documentation
channels are highlighted to outline available resources for flattening learning
curves. CPU runtimes for the Python-based portion of the process chain differed
starkly for both tracks, pointing to avenues for further research. These
results demonstrate that the Python ecosystem offers local governments powerful
tools, free of vendor lock-in and licensing fees, to produce performant and
consistently formatted visualisations for both internal and public
distribution.


title:
Cosmic Gamma-Ray Bursts Detected in the RELEC Experiment onboard the
  Vernov Satellite

abstract:
  The RELEC scientific instrumentation onboard the Vernov spacecraft launched
on July 8, 2014, included the DRGE gamma-ray and electron spectrometer. This
instrument incorporates a set of scintillation phoswich detectors, including
four identical X-ray and gamma-ray detectors in the energy range from 10 keV to
3 MeV with a total area of $\sim$500 $cm^{2}$ directed toward the nadir, and an
electron spectrometer containing three mutually orthogonal detector units with
a geometry factor of $\sim$2 $cm^{2} sr$, which is also sensitive to X-rays and
gamma-rays. The goal of the space experiment with the DRGE instrument was to
investigate phenomena with fast temporal variability, in particular,
terrestrial gamma-ray flashes (TGFs) and magnetospheric electron
precipitations. However, the detectors of the DRGE instrument could record
cosmic gamma-ray bursts (GRBs) and allowed one not only to perform a detailed
analysis of the gamma-ray variability but also to compare the time profiles
with the measurements made by other instruments of the RELEC scientific
instrumentation (the detectors of optical and ultraviolet flashes, the
radio-frequency and low-frequency analyzers of electromagnetic field
parameters). We present the results of our observations of cosmic GRB 141011A
and GRB 141104A, compare the parameters obtained in the GBM/Fermi and KONUSWind
experiments, and estimate the redshifts and Eiso for the sources of these GRBs.
The detectability of GRBs and good agreement between the independent estimates
of their parameters obtained in various experiments are an important factor of
the successful operation of similar detectors onboard the Lomonosov spacecraft.


title: Piecewise rotations: limit set for the non -bijective maps

abstract:
  We consider non-bijective piecewise rotations of the plane. These maps belong
to a family introduced in previous papers by Boshernitzan and Goetz. We derive
in this paper some upper bounds to the size of the limit set. This improves
results of \cite{Bosh.Goet.03}.


title:
Improving position resolution of neutron detectors with ultra-thin B4C
  foils

abstract:
  A new technique for detection of slow neutrons with gaseous detectors using
ultra-thin layers with 10B atoms is presented. The reaction between a thermal
neutron and a 10B atom releases 2 secondary particles, namely a 7Li ion and an
alpha particle, which due to momentum conservation are emitted in opposite
directions, along the same line (back to back). Current boron coated neutron
detectors are equipped with 10B films with thicknesses of several micrometers,
deposited on very thick substrate plates. However, since the ranges of the 7Li
ion and the alpha particle are of few micrometeres in most materials, one of
these particles is always lost in the 10B layer or substrate. As such, these
detectors lose the ability to reconstruct the reaction line of action and to
precisely determine the neutron position, as only one of the two secondary
particles track can be measured. With the technique now presented, the sum of
the 10B layer and the substrate thicknesses is small enough to allow for both
secondary particles to escape and ionize the gas in opposite sides of the 10B
converter foil. Independent readout structures, one on each side of the 10B
converter foil, detect each secondary particle and determine its track centroid
and the deposited energy. Since the two secondary particles are emitted back to
back, the neutron position can be obtained by combining the information
recorded by the two readout structures. Through GEANT4 simulations, we verified
that the spatial resolution can be significantly improved: our results show
that, by using a B4C layer with a thickness of 1 micron on a 0.9 micron Mylar
substrate, the spatial resolution can by improved by a factor of 8, compared to
conventional detectors with thick 10B detection layers.


title: Yamabe problem in the presence of singular Riemannian Foliations

abstract:
  Using variational methods together with symmetries given by singular
Riemannian foliations with positive dimensional leaves, we prove the existence
of an infinite number of sign-changing solutions to Yamabe type problems, which
are constant along the leaves of the foliation, and one positive solution of
minimal energy among any other solution with these symmetries. In particular,
we find sign-changing solutions to the Yamabe problem on the round sphere with
new qualitative behavior when compared to previous results, that is, these
solutions are constant along the leaves of a singular Riemannian foliation
which is not induced neither by a group action nor by an isoparametric
function. To prove the existence of these solutions, we prove a Sobolev
embedding theorem for general singular Riemannian foliations, and a Principle
of Symmetric Criticality for the associated energy functional to a Yamabe type
problem.


title: Optimal-er Auctions through Attention

abstract:
  RegretNet is a recent breakthrough in the automated design of
revenue-maximizing auctions. It combines the expressivity of deep learning with
the regret-based approach to relax and quantify the Incentive Compatibility
constraint (that participants benefit from bidding truthfully). As a follow-up
to its success, we propose two independent modifications of RegretNet, namely a
new neural architecture based on the attention mechanism, denoted as
TransRegret, and an alternative loss function that is interpretable and
significantly less sensitive to hyperparameters. We investigate both proposed
modifications in an extensive experimental study in settings with fixed and
varied input sizes and additionally test out-of-setting generalization of our
network. In all experiments, we find that TransRegret consistently outperforms
existing architectures in revenue. Regarding our loss modification, we confirm
its effectiveness at controlling the revenue-regret trade-off by varying a
single interpretable hyperparameter.


title: Hitting Times for Continuous-Time Imprecise-Markov Chains

abstract:
  We study the problem of characterizing the expected hitting times for a
robust generalization of continuous-time Markov chains. This generalization is
based on the theory of imprecise probabilities, and the models with which we
work essentially constitute sets of stochastic processes. Their inferences are
tight lower- and upper bounds with respect to variation within these sets.
  We consider three distinct types of these models, corresponding to different
levels of generality and structural independence assumptions on the constituent
processes.
  Our main results are twofold; first, we demonstrate that the hitting times
for all three types are equivalent. Moreover, we show that these inferences are
described by a straightforward generalization of a well-known linear system of
equations that characterizes expected hitting times for traditional
time-homogeneous continuous-time Markov chains.


title: Faisceaux equivariants sur $\mathbb{P}^1$ et faisceaux automorphes

abstract:
  Inspired by [Pan21], we define and study a functor which associates to
equivariant sheaves on $\mathbb{P}^1$ automorphic sheaves on modular curves.


title:
Notes on extraction of energy from an extremal Kerr-Newman black hole
  via charged particle collisions

abstract:
  The so-called BSW effect is an idealised scenario for high-energy test
particle collisions in the vicinity of black holes; if the black hole is
extremal and one of the particles fine-tuned, the centre-of-mass collision
energy can be arbitrarily high. It has been recently shown that the energy of
escaping particles produced in this process can also be arbitrarily high in the
given approximation, as long as both the black hole and the escaping particles
are charged, regardless of how small the black-hole charge might be. We revisit
these results and show that they are also compatible with properties of
microscopic particles for the case of motion in the equatorial plane of an
extremal Kerr-Newman black hole.


title: BeDivFuzz: Integrating Behavioral Diversity into Generator-based Fuzzing

abstract:
  A popular metric to evaluate the performance of fuzzers is branch coverage.
However, we argue that focusing solely on covering many different branches
(i.e., the richness) is not sufficient since the majority of the covered
branches may have been exercised only once, which does not inspire a high
confidence in the reliability of the covered code. Instead, the distribution of
the executed branches (i.e., the evenness) should also be considered. That is,
behavioral diversity is only given if the generated inputs not only trigger
many different branches, but also trigger them evenly often with diverse
inputs. We introduce BeDivFuzz, a feedback-driven fuzzing technique for
generator-based fuzzers. BeDivFuzz distinguishes between structure-preserving
and structure-changing mutations in the space of syntactically valid inputs,
and biases its mutation strategy towards validity and behavioral diversity
based on the received program feedback. We have evaluated BeDivFuzz on Ant,
Maven, Rhino, Closure, Nashorn, and Tomcat. The results show that BeDivFuzz
achieves better behavioral diversity than the state of the art, measured by
established biodiversity metrics, namely the Hill numbers, from the field of
ecology.


title: Analysis of Visual Reasoning on One-Stage Object Detection

abstract:
  Current state-of-the-art one-stage object detectors are limited by treating
each image region separately without considering possible relations of the
objects. This causes dependency solely on high-quality convolutional feature
representations for detecting objects successfully. However, this may not be
possible sometimes due to some challenging conditions. In this paper, the usage
of reasoning features on one-stage object detection is analyzed. We attempted
different architectures that reason the relations of the image regions by using
self-attention. YOLOv3-Reasoner2 model spatially and semantically enhances
features in the reasoning layer and fuses them with the original convolutional
features to improve performance. The YOLOv3-Reasoner2 model achieves around
2.5% absolute improvement with respect to baseline YOLOv3 on COCO in terms of
mAP while still running in real-time.


title:
Mobile Device Association and Resource Allocation in Small-Cell IoT
  Networks with Mobile Edge Computing and Caching

abstract:
  To meet the need of computation-sensitive (CS) and high-rate (HR)
communications, the framework of mobile edge computing and caching has been
widely regarded as a promising solution. When such a framework is implemented
in small-cell IoT (Internet of Tings) networks, it is a key and open topic how
to assign mobile edge computing and caching servers to mobile devices (MDs)
with CS and HR communications. Since these servers are integrated into small
base stations (BSs), the assignment of them refers to not only the BS selection
(i.e., MD association), but also the selection of computing and caching modes.
To mitigate the network interference and thus enhance the system performance,
some highly-effective resource partitioning mechanisms are introduced for
access and backhaul links firstly. After that a problem with minimizing the sum
of MDs' weighted delays is formulated to attain a goal of joint MD association
and resource allocation under limited resources. Considering that the MD
association and resource allocation parameters are coupling in such a
formulated problem, we develop an alternating optimization algorithm according
to the coalitional game and convex optimization theorems. To ensure that the
designed algorithm begins from a feasible initial solution, we develop an
initiation algorithm according to the conventional best channel association,
which is used for comparison and the input of coalition game in the simulation.
Simulation results show that the algorithm designed for minimizing the sum of
MDs' weighted delays may achieve a better performance than the initiation (best
channel association) algorithm in general.


title:
An Unsupervised Cross-Modal Hashing Method Robust to Noisy Training
  Image-Text Correspondences in Remote Sensing

abstract:
  The development of accurate and scalable cross-modal image-text retrieval
methods, where queries from one modality (e.g., text) can be matched to archive
entries from another (e.g., remote sensing image) has attracted great attention
in remote sensing (RS). Most of the existing methods assume that a reliable
multi-modal training set with accurately matched text-image pairs is existing.
However, this assumption may not always hold since the multi-modal training
sets may include noisy pairs (i.e., textual descriptions/captions associated to
training images can be noisy), distorting the learning process of the retrieval
methods. To address this problem, we propose a novel unsupervised cross-modal
hashing method robust to the noisy image-text correspondences (CHNR). CHNR
consists of three modules: 1) feature extraction module, which extracts feature
representations of image-text pairs; 2) noise detection module, which detects
potential noisy correspondences; and 3) hashing module that generates
cross-modal binary hash codes. The proposed CHNR includes two training phases:
i) meta-learning phase that uses a small portion of clean (i.e., reliable) data
to train the noise detection module in an adversarial fashion; and ii) the main
training phase for which the trained noise detection module is used to identify
noisy correspondences while the hashing module is trained on the noisy
multi-modal training set. Experimental results show that the proposed CHNR
outperforms state-of-the-art methods. Our code is publicly available at
https://git.tu-berlin.de/rsim/chnr


title: Accurate Human Body Reconstruction for Volumetric Video

abstract:
  In this work, we enhance a professional end-to-end volumetric video
production pipeline to achieve high-fidelity human body reconstruction using
only passive cameras. While current volumetric video approaches estimate depth
maps using traditional stereo matching techniques, we introduce and optimize
deep learning-based multi-view stereo networks for depth map estimation in the
context of professional volumetric video reconstruction. Furthermore, we
propose a novel depth map post-processing approach including filtering and
fusion, by taking into account photometric confidence, cross-view geometric
consistency, foreground masks as well as camera viewing frustums. We show that
our method can generate high levels of geometric detail for reconstructed human
bodies.


title: Unprecedented change in the position of four radio sources

abstract:
  Astrometric positions of radio-emitting active galactic nuclei (AGNs) can be
determined with sub-milliarcsec accuracy using very long baseline
interferometry (VLBI). The usually small apparent proper motion of distant
extragalactic targets allow us to realize the fundamental celestial reference
frame with VLBI observations. However, long-term astrometric monitoring may
reveal extreme changes in some AGN positions. Using new VLBI observations in
2018-2021, we show here that four extragalactic radio sources (3C48, CTA21,
1144+352, 1328+254) have a dramatic shift in their positions by 20-130
milliarcsec over two decades. For all four sources, the apparent positional
shift is caused by their radio structure change.


title:
A Log-Gaussian Cox Process with Sequential Monte Carlo for Line
  Narrowing in Spectroscopy

abstract:
  We propose a statistical model for narrowing line shapes in spectroscopy that
are well approximated as linear combinations of Lorentzian or Voigt functions.
We introduce a log-Gaussian Cox process to represent the peak locations thereby
providing uncertainty quantification for the line narrowing. Bayesian
formulation of the method allows for robust and explicit inclusion of prior
information as probability distributions for parameters of the model.
Estimation of the signal and its parameters is performed using a sequential
Monte Carlo algorithm allowing for parallelization of model likelihood
computation. The method is validated using simulated spectra and applied to an
experimental Raman spectrum obtained from a protein droplet measurement.


title:
Person Re-identification: A Retrospective on Domain Specific Open
  Challenges and Future Trends

abstract:
  Person re-identification (Re-ID) is one of the primary components of an
automated visual surveillance system. It aims to automatically identify/search
persons in a multi-camera network having non-overlapping field-of-views. Owing
to its potential in various applications and research significance, a plethora
of deep learning based re-Id approaches have been proposed in the recent years.
However, there exist several vision related challenges, e.g., occlusion, pose
scale \& viewpoint variance, background clutter, person misalignment and
cross-domain generalization across camera modalities, which makes the problem
of re-Id still far from being solved. Majority of the proposed approaches
directly or indirectly aim to solve one or multiple of these existing
challenges. In this context, a comprehensive review of current re-ID approaches
in solving theses challenges is needed to analyze and focus on particular
aspects for further advancements. At present, such a focused review does not
exist and henceforth in this paper, we have presented a systematic
challenge-specific literature survey of 230+ papers between the years of
2015-21. For the first time a survey of this type have been presented where the
person re-Id approaches are reviewed in such solution-oriented perspective.
Moreover, we have presented several diversified prominent developing trends in
the respective research domain which will provide a visionary perspective
regarding ongoing person re-Id research and eventually help to develop
practical real world solutions.


title:
What ODE-Approximation Schemes of Time-Delay Systems Reveal about
  Lyapunov-Krasovskii Functionals

abstract:
  Lyapunov-Krasovskii functionals are found to be related to Lyapunov functions
that prove partial stability in finite dimensional system approximations. These
approximations are ordinary differential equations, which, in the present
paper, originate from the Chebyshev (pseudospectral) collocation or the
Legendre tau method. Lyapunov functions that prove partial stability are simply
obtained by solving a Lyapunov equation. They approximate the
Lyapunov-Krasovskii functional. A formula for the partial positive definiteness
bound on the Lyapunov function is derived. The formula is also applied to a
numerical integration of the known Lyapunov-Krasovskii functional. An example
shows that both approaches converge to identical results, representing the
largest quadratic lower bound on complete-type or related functionals.


title:
Content-Variant Reference Image Quality Assessment via Knowledge
  Distillation

abstract:
  Generally, humans are more skilled at perceiving differences between
high-quality (HQ) and low-quality (LQ) images than directly judging the quality
of a single LQ image. This situation also applies to image quality assessment
(IQA). Although recent no-reference (NR-IQA) methods have made great progress
to predict image quality free from the reference image, they still have the
potential to achieve better performance since HQ image information is not fully
exploited. In contrast, full-reference (FR-IQA) methods tend to provide more
reliable quality evaluation, but its practicability is affected by the
requirement for pixel-level aligned reference images. To address this, we
firstly propose the content-variant reference method via knowledge distillation
(CVRKD-IQA). Specifically, we use non-aligned reference (NAR) images to
introduce various prior distributions of high-quality images. The comparisons
of distribution differences between HQ and LQ images can help our model better
assess the image quality. Further, the knowledge distillation transfers more
HQ-LQ distribution difference information from the FR-teacher to the
NAR-student and stabilizing CVRKD-IQA performance. Moreover, to fully mine the
local-global combined information, while achieving faster inference speed, our
model directly processes multiple image patches from the input with the
MLP-mixer. Cross-dataset experiments verify that our model can outperform all
NAR/NR-IQA SOTAs, even reach comparable performance with FR-IQA methods on some
occasions. Since the content-variant and non-aligned reference HQ images are
easy to obtain, our model can support more IQA applications with its relative
robustness to content variations. Our code and more detailed elaborations of
supplements are available: https://github.com/guanghaoyin/CVRKD-IQA.


title:
Multi-image Super-resolution via Quality Map Associated Temporal
  Attention Network

abstract:
  With the rising interest in deep learning-based methods in remote sensing,
neural networks have made remarkable advancements in multi-image fusion and
super-resolution. To fully exploit the advantages of multi-image
super-resolution, temporal attention is crucial as it allows a model to focus
on reliable features rather than noises. Despite the presence of quality maps
(QMs) that indicate noises in images, most of the methods tested in the PROBA-V
dataset have not been used QMs for temporal attention. We present a quality map
associated temporal attention network (QA-Net), a novel method that
incorporates QMs into both feature representation and fusion processes for the
first time. Low-resolution features are temporally attended by QM features in
repeated multi-head attention modules. The proposed method achieved
state-of-the-art results in the PROBA-V dataset.


title: Quantum Algorithms for solving Hard Constrained Optimisation Problems

abstract:
  The thesis deals with Quantum Algorithms for solving Hard Constrained
Optimization Problems. It shows how quantum computers can solve difficult
everyday problems such as finding the best schedule for social workers or the
path of a robot picking and batching in a warehouse. The path to the solution
has led to the definition of a new artificial intelligence paradigm with
quantum computing, quantum Case-Based Reasoning (qCBR) and to a proof of
concept to integrate the capacity of quantum computing within mobile robotics
using a Raspberry Pi 4 as a processor (qRobot), capable of operating with
leading technology players such as IBMQ, Amazon Braket (D-Wave) and Pennylane.
To improve the execution time of variational algorithms in this NISQ era and
the next, we have proposed EVA: a quantum Exponential Value Approximation
algorithm that speeds up the VQE, and that is, to date, the flagship of the
quantum computation. To improve the execution time of variational algorithms in
this NISQ era and the next, we have proposed EVA: a quantum Exponential Value
Approximation algorithm that speeds up the VQE, and that is, to date, the
flagship of the quantum computation.


title: A Two-Component Dark Matter Model and its Associated Gravitational Waves

abstract:
  We consider an extension of the Standard Model that accounts for the muon
$g-2$ tension and neutrino masses and study in detail dark matter
phenomenology. The model under consideration includes a WIMP and a FIMP scalar
dark matter candidates and thus gives rise to two-component dark matter
scenarios. We discuss different regimes and mechanisms of production, including
the novel freeze-in semi-production, and show that the WIMP and FIMP together
compose the observed relic density today. The presence of the extra scalar
fields allows phase transitions of the first order. We examine the evolution of
the vacuum state and discuss stochastic gravitational wave signals associated
with the first-order phase transition. We show that the gravitational wave
signals may be probed by future gravitational wave experiments which may serve
as a complementary detection signal.


title: Plug-and-play quantum devices with efficient fiber-quantum dot interface

abstract:
  Incorporating solid-state quantum emitters into optical fiber networks
enables the long-distance transmission of quantum information and the remote
connection of distributed quantum nodes. However, interfacing quantum emitters
with fiber optics encounters several challenges, including low coupling
efficiency and stability. Here, we demonstrate a highly efficient
fiber-interfacing photonic device that directly launches single photons from
quantum dots into a standard FC/PC-connectorized single-mode fiber (SMF28).
Optimally designed photonic structures based on hole gratings produce an
ultra-narrow directional beam that matches the small numerical aperture of a
single-mode fiber. A pick-and-place technique selectively integrates a single
miniaturized device into the core of the fiber. Our approach realizes a
plug-and-play single-photon device that does not require any optical alignment
and thus guarantees long-term stability. The results thus represent a major
step toward practical and reliable quantum lights across a fiber network.


title:
Prevalent behavior and almost sure Poincare-Bendixson Theorem for smooth
  flows with invariant k-cones

abstract:
  We investigate the global dynamics from a measure-theoretic perspective for
smooth flows with invariant cones of rank k. For such systems, it is shown that
prevalent (or equivalently, almost all) orbits will be pseudo-ordered or
convergent to equilibria. This reduces to Hirsch's prevalent convergence
Theorem if the rank k=1; and implies an almost-sure Poincare-Bendixson Theorem
for the case k=2. These results are then applied to obtain an almost sure
Poincare-Bendixson theorem for high-dimensional differential equations.


title:
Direct data-driven forecast of local turbulent heat flux in
  Rayleigh-B\'{e}nard convection

abstract:
  A combined convolutional autoencoder-recurrent neural network machine
learning model is presented to analyse and forecast the dynamics and low-order
statistics of the local convective heat flux field in a two-dimensional
turbulent Rayleigh-B\'{e}nard convection flow at Prandtl number ${\rm Pr}=7$
and Rayleigh number ${\rm Ra}=10^7$. Two recurrent neural networks are applied
for the temporal advancement of flow data in the reduced latent data space, a
reservoir computing model in the form of an echo state network and a recurrent
gated unit. Thereby, the present work exploits the modular combination of three
different machine learning algorithms to build a fully data-driven and reduced
model for the dynamics of the turbulent heat transfer in a complex thermally
driven flow. The convolutional autoencoder with 12 hidden layers is able to
reduce the dimensionality of the turbulence data to about 0.2 \% of their
original size. Our results indicate a fairly good accuracy in the first- and
second-order statistics of the convective heat flux. The algorithm is also able
to reproduce the intermittent plume-mixing dynamics at the upper edges of the
thermal boundary layers with some deviations. The same holds for the
probability density function of the local convective heat flux with differences
in the far tails. Furthermore, we demonstrate the noise resilience of the
framework which suggests the present model might be applicable as a reduced
dynamical model that delivers transport fluxes and their variations to the
coarse grid cells of larger-scale computational models, such as global
circulation models for the atmosphere and ocean.


title: Central factorial numbers associated with sequences of polynomials

abstract:
  Many important special numbers appear in the expansions of some polynomials
in terms of central factorials and vice versa, for example central factorial
numbers, degenerate central factorial numbers, and central Lah numbers which
are recently introduced. Here we generalize this to any sequence of
polynomials. Let P be the space of a sequence of polynomials such that the
degree of the polynomials less than n . The aim of this paper is to study the
central factorial numbers of the second associated with P and of the first kind
associated with P, in a unified and systematic way with the help of umbral
calculus technique. The central factorial numbers associated with P enjoy
orthogonality and inverse relations. We illustrate our results with many
examples and obtain interesting orthogonality and inverse relations by applying
such relations for the central factorial numbers associated with P to each of
our examples.


title: Entanglement detection with imprecise measurements

abstract:
  We investigate entanglement detection when the local measurements only nearly
correspond to those intended. This corresponds to a scenario in which
measurement devices are not perfectly controlled, but nevertheless operate with
bounded inaccuracy. We formalise this through an operational notion of
inaccuracy that can be estimated directly in the lab. To demonstrate the
relevance of this approach, we show that small magnitudes of inaccuracy can
significantly compromise several renowned entanglement witnesses. For two
arbitrary-dimensional systems, we show how to compute tight corrections to a
family of standard entanglement witnesses due to any given level of measurement
inaccuracy. We also develop semidefinite programming methods to bound
correlations in these scenarios.


title:
A Synthetic Perspective on $(\infty,1)$-Category Theory: Fibrational and
  Semantic Aspects

abstract:
  Reasoning about weak higher categorical structures constitutes a challenging
task, even to the experts. One principal reason is that the language of set
theory is not invariant under the weaker notions of equivalence at play, such
as homotopy equivalence. From this point of view, it is natural to ask for a
different foundational setting which more natively supports these notions.
  Our work takes up on suggestions in the original article arXiv:1705.07442 by
Riehl--Shulman to further develop synthetic $(\infty,1)$-category theory in
simplicial homotopy type theory, including in particular the study of
cocartesian fibrations. Together with a collection of analytic results, notably
due to Riehl--Verity and Rasekh, it follows that our type-theoretic account
constitutes a synthetic theory of fibrations of internal
$(\infty,1)$-categories, w.r.t. to an arbitrary
Grothendieck--Rezk--Lurie-$(\infty,1)$-topos via Shulman's major result
arXiv:1904.07004 about strictification of univalent universes.


title: Nonlinear Discrete Optimisation of Reversible Steganographic Coding

abstract:
  Authentication mechanisms are at the forefront of defending the world from
various types of cybercrime. Steganography can serve as an authentication
solution by embedding a digital signature into a carrier object to ensure the
integrity of the object and simultaneously lighten the burden of metadata
management. However, steganographic distortion, albeit generally imperceptible
to human sensory systems, might be inadmissible in fidelity-sensitive
situations. This has led to the concept of reversible steganography. A
fundamental element of reversible steganography is predictive analytics, for
which powerful neural network models have been effectively deployed. As another
core aspect, contemporary reversible steganographic coding is based primarily
on heuristics and therefore worth further study. While attempts have been made
to realise automatic coding with neural networks, perfect reversibility is
still unreachable via such an unexplainable intelligent machinery. Instead of
relying on deep learning, we aim to derive an optimal coding by means of
mathematical optimisation. In this study, we formulate reversible
steganographic coding as a nonlinear discrete optimisation problem with a
logarithmic capacity constraint and a quadratic distortion objective.
Linearisation techniques are developed to enable mixed-integer linear
programming. Experimental results validate the near-optimality of the proposed
optimisation algorithm benchmarked against a brute-force method.


title:
Preventing Timing Side-Channels via Security-Aware Just-In-Time
  Compilation

abstract:
  Recent work has shown that Just-In-Time (JIT) compilation can introduce
timing side-channels to constant-time programs, which would otherwise be a
principled and effective means to counter timing attacks. In this paper, we
propose a novel approach to eliminate JIT-induced leaks from these programs.
Specifically, we present an operational semantics and a formal definition of
constant-time programs under JIT compilation, laying the foundation for
reasoning about programs with JIT compilation. We then propose to eliminate
JIT-induced leaks via a fine-grained JIT compilation for which we provide an
automated approach to generate policies and a novel type system to show its
soundness. We develop a tool DeJITLeak for Java based on our approach and
implement the fine-grained JIT compilation in HotSpot. Experimental results
show that DeJITLeak can effectively and efficiently eliminate JIT-induced leaks
on three datasets used in side-channel detection


title:
Magnetostatic interaction in oriented assembly of elongated
  nanoparticles

abstract:
  The elements of the magneto-dipole (MD) interaction matrix are calculated for
a pair of oriented spheroidal magnetic nanoparticles with a semiaxes ratio a/b
= 1.25, 1.5, 2.0 and 3.0 as a function of the distance between the particle
centers. It is shown that the spherical approximation for MD interaction matrix
is incorrect already at aspect ratios a/b > 1.25, if the distance R between the
particle centers is of the order of particle sizes. However, for moderate
particle aspect ratios, a/b < 1.5, the first order correction to spherical
approximation with respect to a corresponding small parameter is shown to be in
a good agreement with numerical data. Using exact MD interaction matrix, the
quasi-static hysteresis loops of dilute assemblies of oriented clusters of
spheroidal nanoparticles with different filling densities are calculated,
depending on the direction of the external magnetic field with respect to the
particle orientation axis.


title: On the pulsar spin frequency derivatives and the glitch activity

abstract:
  The number of sudden spin-ups in radio pulsars known as pulsar glitches has
increased over the years. Though a consensus has not been reached with regards
to the actual cause of the phenomenon, the electromagnetic braking torque on
the crust quantified via the magnitude of pulsar spin frequency first
derivative, $ \dot{\nu} $ is a key factor in mechanisms put across toward the
understanding of the underlying principles involved. The glitch size has been
used to establish a quantity used to constrain the mean possible change in
pulsar spin frequency $ (\nu) $ per year due to a glitch known as the `glitch
activity'. Traditionally, the glitch activity parameter $ A_{g} $ is calculated
from the cumulative glitch sizes in a pulsar at a certain observational time
span. In this analysis, we test the possibility of of quantifying the $ A_{g} $
with the pulsars main spin frequency derivatives (i.e. $ \dot{\nu} $ and
$\ddot{\nu} $). In this approach, the ratio of the frequency derivatives, i.e.
$ |\ddot{\nu}|/\dot{\nu}^{2} $ is seen to constrains the glitch activity in
radio pulsars. The glitch size is found to be independent of the magnitude of
the ratio, however, based on the recorded glitch events, the lower end of $
|\ddot{\nu}|/\dot{\nu}^{2} $ distribution appear to have more glitches. The
minimum inter-glitch time interval in the ensemble of pulsars scale with the
ratio as $t_{g} \sim 3.35(|\ddot{\nu}|/\dot{\nu}^{2})^{0.23} $. The $ A_{g} $
quantified in this analysis supports the idea of neutron star inner-crust
superfluid being the reservoir of momentum transferred during glitches. It
suggests that the moment of inertia of the inner-crust to be at most 10 % of
the entire neutron star moment of inertia.