title:
Cyber-resilience for marine navigation by information fusion and change
  detection

abstract:
  Cyber-resilience is an increasing concern in developing autonomous navigation
solutions for marine vessels. This paper scrutinizes cyber-resilience
properties of marine navigation through a prism with three edges: multiple
sensor information fusion, diagnosis of not-normal behaviours, and change
detection. It proposes a two-stage estimator for diagnosis and mitigation of
sensor signals used for coastal navigation. Developing a Likelihood Field
approach, a first stage extracts shoreline features from radar and matches them
to the electronic navigation chart. A second stage associates buoy and beacon
features from the radar with chart information. Using real data logged at sea
tests combined with simulated spoofing, the paper verifies the ability to
timely diagnose and isolate an attempt to compromise position measurements. A
new approach is suggested for high level processing of received data to
evaluate their consistency, that is agnostic to the underlying technology of
the individual sensory input. A combined parametric Gaussian modelling and
Kernel Density Estimation is suggested and compared with a generalized
likelihood ratio change detector that uses sliding windows. The paper shows how
deviations from nominal behaviour and isolation of the components is possible
when under attack or when defects in sensors occur.


title: Radio Map Estimation: A Data-Driven Approach to Spectrum Cartography

abstract:
  Radio maps can be utilized to characterize a parameter of interest in a
communication channel, such as the received signal strength, at every point of
a certain geographical region. This article presents an introductory tutorial
to radio map estimation, where radio maps are constructed using spatially
distributed measurements. After describing the applications of this kind of
maps, this article delves into estimation approaches. Starting by simple
regression techniques, gradually more sophisticated algorithms are introduced
until reaching state-of-the-art estimators. The presentation of this versatile
toolkit is accompanied with toy examples to build up intuition and gain insight
into the foundations of radio map estimation. As a secondary objective, this
article attempts to reconcile the sometimes conflicting terminology in the
literature and to connect multiple bodies of literature and sub-communities
that have been working separately in this context.


title: Do Developers Refactor Data Access Code? An Empirical Study

abstract:
  Developers often refactor code to improve the maintainability and
comprehension of the software. There are many studies on refactoring activities
in traditional software systems. However, refactoring in data-intensive systems
is not well explored. Understanding the refactoring practices of developers is
important to develop efficient tool support.We conducted a longitudinal study
of refactoring activities in data access classes using 12 data-intensive
subject systems. We investigated the prevalence and evolution of refactorings
and the association of refactorings with data access smells. We also conducted
a manual analysis of over 378 samples of data access refactoring instances to
identify the functionalities of the code that are targeted by such
refactorings. Our results show that (1) data access refactorings are prevalent
and different in type. \textit{Rename variable} is the most prevalent data
access refactoring. (2) The prevalence and type of refactorings vary as systems
evolve in time. (3) Most data access refactorings target codes that implement
data fetching and insertion. (4) Data access refactorings do not generally
touch SQL queries. Overall, the results show that data access refactorings
focus on improving the code quality but not the underlying data access
operations. Hence, more work is needed from the research community on providing
awareness and support to practitioners on the benefits of addressing data
access smells with refactorings.


title: Spectro Temporal EEG Biomarkers For Binary Emotion Classification

abstract:
  Electroencephalogram (EEG) is one of the most reliable physiological signal
for emotion detection. Being non-stationary in nature, EEGs are better analysed
by spectro temporal representations. Standard features like Discrete Wavelet
Transformation (DWT) can represent temporal changes in spectral dynamics of an
EEG, but is insufficient to extract information other way around, i.e. spectral
changes in temporal dynamics. On the other hand, Empirical mode decomposition
(EMD) based features can be useful to bridge the above mentioned gap. Towards
this direction, we extract two novel features on top of EMD, namely, (a)
marginal hilbert spectrum (MHS) and (b) Holo-Hilbert spectral analysis (HHSA)
based on EMD, to better represent emotions in 2D arousal-valence (A-V) space.
The usefulness of these features for EEG emotion classification is investigated
through extensive experiments using state-of-the-art classifiers. In addition,
experiments conducted on DEAP dataset for binary emotion classification in both
A-V space, reveal the efficacy of the proposed features over the standard set
of temporal and spectral features.


title: Classical shadows with Pauli-invariant unitary ensembles

abstract:
  The classical shadow estimation protocol is a noise-resilient and
sample-efficient quantum algorithm for learning the properties of quantum
systems. Its performance depends on the choice of a unitary ensemble, which
must be chosen by a user in advance. What is the weakest assumption that can be
made on the chosen unitary ensemble that would still yield meaningful and
interesting results? To address this question, we consider the class of
Pauli-invariant unitary ensembles, i.e. unitary ensembles that are invariant
under multiplication by a Pauli operator. This class includes many previously
studied ensembles like the local and global Clifford ensembles as well as
locally scrambled unitary ensembles. For this class of ensembles, we provide an
explicit formula for the reconstruction map corresponding to the shadow channel
and give explicit sample complexity bounds. In addition, we provide two
applications of our results. Our first application is to locally scrambled
unitary ensembles, where we give explicit formulas for the reconstruction map
and sample complexity bounds that circumvent the need to solve an
exponential-sized linear system. Our second application is to the classical
shadow tomography of quantum channels with Pauli-invariant unitary ensembles.
Our results pave the way for more efficient or robust protocols for predicting
important properties of quantum states, such as their fidelity, entanglement
entropy, and quantum Fisher information.


title: Wave-Controlled Metasurface-Based Reconfigurable Intelligent Surfaces

abstract:
  Reconfigurable Intelligent Surfaces (RISs) are programmable metasurfaces that
can adaptively steer received electromagnetic energy in desired directions by
employing controllable phase shifting cells. Among other uses, an RIS can
modify the propagation environment in order to provide wireless access to user
locations that are not otherwise reachable by a base station. Alternatively, an
RIS can steer the waves away from particular locations in space, to eliminate
interference and allow for co-existence of the wireless network with other
types of fixed wireless services (e.g., radars, unlicensed radio bands, etc.).
The novel approach in this work is a wave-controlled architecture that properly
accounts for the maximum possible change in the local reflection phase that can
be achieved by adjacent RIS elements. It obviates the need for dense wiring and
signal paths that would be required for individual control of every RIS
element, and thus offers a substantial reduction in the required hardware. We
specify this wave-controlled RIS architecture in detail and discuss signal
processing and machine learning methods that exploit it in both point-to-point
and multicell MIMO systems. Such implementations can lead to a dramatic
improvement in next-generation wireless, radar, and navigation systems where
RIS finds wide applications. They have the potential to improve the efficiency
of spectrum utilization and coexistence by orders of magnitude.


title:
Human Activity Recognition Using Tools of Convolutional Neural Networks:
  A State of the Art Review, Data Sets, Challenges and Future Prospects

abstract:
  Human Activity Recognition (HAR) plays a significant role in the everyday
life of people because of its ability to learn extensive high-level information
about human activity from wearable or stationary devices. A substantial amount
of research has been conducted on HAR and numerous approaches based on deep
learning and machine learning have been exploited by the research community to
classify human activities. The main goal of this review is to summarize recent
works based on a wide range of deep neural networks architecture, namely
convolutional neural networks (CNNs) for human activity recognition. The
reviewed systems are clustered into four categories depending on the use of
input devices like multimodal sensing devices, smartphones, radar, and vision
devices. This review describes the performances, strengths, weaknesses, and the
used hyperparameters of CNN architectures for each reviewed system with an
overview of available public data sources. In addition, a discussion with the
current challenges to CNN-based HAR systems is presented. Finally, this review
is concluded with some potential future directions that would be of great
assistance for the researchers who would like to contribute to this field.


title: Detecting Soil Moisture Levels Using Battery-Free Wi-Fi Tag

abstract:
  Soil sensing plays an important role in increasing agricultural output and
protecting soil sites. Existing soil sensing methods failed to achieve both
high accuracy and low cost. In this paper, we design and implement a
high-accuracy and low cost chipless soil moisture sensing system called
SoilTAG. We propose a general chipless sensor design methodology which can
allow us to customize the signal feature for sensing soil moisture, instead of
blindly capturing the disturbance law of the soil. Based on this principle, we
design a battery-free passive tag which can respond to different soil-moisture.
Further, we optimize hardware and algorithm design of SoilTAG to locate the
passive tag and extract its reflection signal feature to identify soil-moisture
using WiFi signals. Extensive experimental results reveal that it can identify
2% absolute soil water content with a sensing distance up to 3m in open field.
When the sensing distance is up to 13 m, it can also achieve 5% absolute
soil-moisture sensing resolution.


title:
Ransomware: Analysing the Impact on Windows Active Directory Domain
  Services

abstract:
  Ransomware has become an increasingly popular type of malware across the past
decade and continues to rise in popularity due to its high profitability.
Organisations and enterprises have become prime targets for ransomware as they
are more likely to succumb to ransom demands as part of operating expenses to
counter the cost incurred from downtime. Despite the prevalence of ransomware
as a threat towards organisations, there is very little information outlining
how ransomware affects Windows Server environments, and particularly its
proprietary domain services such as Active Directory. Hence, we aim to increase
the cyber situational awareness of organisations and corporations that utilise
these environments. Dynamic analysis was performed using three ransomware
variants to uncover how crypto-ransomware affects Windows Server-specific
services and processes. Our work outlines the practical investigation
undertaken as WannaCry, TeslaCrypt, and Jigsaw were acquired and tested against
several domain services. The findings showed that none of the three variants
stopped the processes and decidedly left all domain services untouched.
However, although the services remained operational, they became uniquely
dysfunctional as ransomware encrypted the files pertaining to those services


title:
On The Empirical Effectiveness of Unrealistic Adversarial Hardening
  Against Realistic Adversarial Attacks

abstract:
  While the literature on security attacks and defense of Machine Learning (ML)
systems mostly focuses on unrealistic adversarial examples, recent research has
raised concern about the under-explored field of realistic adversarial attacks
and their implications on the robustness of real-world systems. Our paper paves
the way for a better understanding of adversarial robustness against realistic
attacks and makes two major contributions. First, we conduct a study on three
real-world use cases (text classification, botnet detection, malware
detection)) and five datasets in order to evaluate whether unrealistic
adversarial examples can be used to protect models against realistic examples.
Our results reveal discrepancies across the use cases, where unrealistic
examples can either be as effective as the realistic ones or may offer only
limited improvement. Second, to explain these results, we analyze the latent
representation of the adversarial examples generated with realistic and
unrealistic attacks. We shed light on the patterns that discriminate which
unrealistic examples can be used for effective hardening. We release our code,
datasets and models to support future research in exploring how to reduce the
gap between unrealistic and realistic adversarial attacks.


title: Crafting Better Contrastive Views for Siamese Representation Learning

abstract:
  Recent self-supervised contrastive learning methods greatly benefit from the
Siamese structure that aims at minimizing distances between positive pairs. For
high performance Siamese representation learning, one of the keys is to design
good contrastive pairs. Most previous works simply apply random sampling to
make different crops of the same image, which overlooks the semantic
information that may degrade the quality of views. In this work, we propose
ContrastiveCrop, which could effectively generate better crops for Siamese
representation learning. Firstly, a semantic-aware object localization strategy
is proposed within the training process in a fully unsupervised manner. This
guides us to generate contrastive views which could avoid most false positives
(i.e., object vs. background). Moreover, we empirically find that views with
similar appearances are trivial for the Siamese model training. Thus, a
center-suppressed sampling is further designed to enlarge the variance of
crops. Remarkably, our method takes a careful consideration of positive pairs
for contrastive learning with negligible extra training overhead. As a
plug-and-play and framework-agnostic module, ContrastiveCrop consistently
improves SimCLR, MoCo, BYOL, SimSiam by 0.4% ~ 2.0% classification accuracy on
CIFAR-10, CIFAR-100, Tiny ImageNet and STL-10. Superior results are also
achieved on downstream detection and segmentation tasks when pre-trained on
ImageNet-1K.


title:
On the sensitivity of implementations of a least-squares collocation
  method for linear higher-index differential-algebraic equations

abstract:
  The present paper continues our investigation of an implementation of a
least-squares collocation method for higher-index differential-algebraic
equations. In earlier papers, we were able to substantiate the choice of basis
functions and collocation points for a robust implementation as well as
algorithms for the solution of the discrete system. The present paper is
devoted to an analytic estimation of condition numbers for different components
of an implementation. We present error estimations, which show the sources for
the different errors.


title:
Elaborating the word problem for free idempotent-generated semigroups
  over the full transformation monoid

abstract:
  With each semigroup one can associate a partial algebra, called the biordered
set, which captures important algebraic and geometric features of the structure
of idempotents of that semigroup. For a biordered set $\mathcal{E}$, one can
construct the free idempotent-generated semigroup over $\mathcal{E}$,
$\mathsf{IG}(\mathcal{E})$, which is the free-est semigroup (in a definite
categorical sense) whose biorder of idempotents is isomorphic to $\mathcal{E}$.
Studies of these intriguing objects have been recently focusing on their
particular aspects, such as maximal subgroups, the word problem, etc. In 2012,
Gray and Ru\v{s}kuc pointed out that a more detailed investigation into the
structure of the free idempotent-generated semigroup over the biorder of
$\mathcal{T}_n$, the full transformation monoid over an $n$-element set, might
be worth pursuing. In 2019, together with Gould and Yang, the present author
showed that the word problem for $\mathsf{IG}(\mathcal{E}_{\mathcal{T}_n})$ is
algorithmically soluble. In a recent work by the author, it was showed that,
for a wide class of biorders $\mathcal{E}$, the algorithmic solution of the
word problem revolves around the so-called vertex groups, which arise as
certain subgroups of direct products of pairs of maximal subgroups of
$\mathsf{IG}(\mathcal{E})$. In this paper we determine these vertex groups for
the case when $\mathcal{E}$ is the biorder of idempotents of $\mathcal{T}_n$.


title:
Personalized Public Policy Analysis in Social Sciences using
  Causal-Graphical Normalizing Flows

abstract:
  Structural Equation/Causal Models (SEMs/SCMs) are widely used in epidemiology
and social sciences to identify and analyze the average causal effect (ACE) and
conditional ACE (CACE). Traditional causal effect estimation methods such as
Inverse Probability Weighting (IPW) and more recently Regression-With-Residuals
(RWR) are widely used - as they avoid the challenging task of identifying the
SCM parameters - to estimate ACE and CACE. However, much work remains before
traditional estimation methods can be used for counterfactual inference, and
for the benefit of Personalized Public Policy Analysis (P$^3$A) in the social
sciences. While doctors rely on personalized medicine to tailor treatments to
patients in laboratory settings (relatively closed systems), P$^3$A draws
inspiration from such tailoring but adapts it for open social systems. In this
article, we develop a method for counterfactual inference that we name
causal-Graphical Normalizing Flow (c-GNF), facilitating P$^3$A. First, we show
how c-GNF captures the underlying SCM without making any assumption about
functional forms. Second, we propose a novel dequantization trick to deal with
discrete variables, which is a limitation of normalizing flows in general.
Third, we demonstrate in experiments that c-GNF performs on-par with IPW and
RWR in terms of bias and variance for estimating the ATE, when the true
functional forms are known, and better when they are unknown. Fourth and most
importantly, we conduct counterfactual inference with c-GNFs, demonstrating
promising empirical performance. Because IPW and RWR, like other traditional
methods, lack the capability of counterfactual inference, c-GNFs will likely
play a major role in tailoring personalized treatment, facilitating P$^3$A,
optimizing social interventions - in contrast to the current
`one-size-fits-all' approach of existing methods.


title:
Efficient coverage planning for full-area C-ITS communications based on
  radio propagation simulation and measurement tools

abstract:
  Intelligent infrastructure, currently often consisting of C-ITS stations and
prospectively supplemented by 5G, is a key-enabler for application-oriented and
area-wide realization of highly automated and connected driving. For this,
radio coverage along the routes must be ensured, leading to high demands on
location- and radio-specific planning and parameterization of roadside units
(RSU). Hence, this paper presents efficient planning, measurement and
evaluation methods for RSU coverage outlining, allowing economically efficient
and technically secured planning of intelligent infrastructure. Necessary
scientific technical steps are showcased along a 3.5 km testbed for automated
and connected driving in rural environments. First, a radio propagation
simulation based on a 3D environment model and its electro-magnetic properties
is performed, allowing the examination and optimization of RSU quantity as well
as site and antenna selection. Additionally, the necessary calibration of
simulation results based on continuous wave (CW) and C-ITS service measurements
in both lab-based and real-world scenarios is presented.


title:
CZU-MHAD: A multimodal dataset for human action recognition utilizing a
  depth camera and 10 wearable inertial sensors

abstract:
  Human action recognition has been widely used in many fields of life, and
many human action datasets have been published at the same time. However, most
of the multi-modal databases have some shortcomings in the layout and number of
sensors, which cannot fully represent the action features. Regarding the
problems, this paper proposes a freely available dataset, named CZU-MHAD
(Changzhou University: a comprehensive multi-modal human action dataset). It
consists of 22 actions and three modals temporal synchronized data. These
modals include depth videos and skeleton positions from a kinect v2 camera, and
inertial signals from 10 wearable sensors. Compared with single modal sensors,
multi-modal sensors can collect different modal data, so the use of multi-modal
sensors can describe actions more accurately. Moreover, CZU-MHAD obtains the
3-axis acceleration and 3-axis angular velocity of 10 main motion joints by
binding inertial sensors to them, and these data were captured at the same
time. Experimental results are provided to show that this dataset can be used
to study structural relationships between different parts of the human body
when performing actions and fusion approaches that involve multi-modal sensor
data.


title: Towards Quantum Computing with Molecular Electronics

abstract:
  In this study, we explore the use of molecules and molecular electronics for
quantum computing. We construct one-qubit gates using one-electron scattering
in molecules, and two-qubit controlled-phase gates using electron-electron
scattering along metallic leads. Furthermore, we propose a class of circuit
implementations, and show initial applications of the framework by illustrating
one-qubit gates using the molecular electronic structure of molecular hydrogen
as a baseline model.


title: Jet Flavour Tagging for Future Colliders with Fast Simulation

abstract:
  Jet flavour identification algorithms are of paramount importance to maximise
the physics potential of future collider experiments. This work describes a
novel set of tools allowing for a realistic simulation and reconstruction of
particle level observables that are necessary ingredients to jet flavour
identification. An algorithm for reconstructing the track parameters and
covariance matrix of charged particles for an arbitrary tracking sub-detector
geometries has been developed. Additional modules allowing for particle
identification using time-of-flight and ionizing energy loss information have
been implemented. A jet flavour identification algorithm based on a graph
neural network architecture and exploiting all available particle level
information has been developed. The impact of different detector design
assumptions on the flavour tagging performance is assessed using the FCC-ee
IDEA detector prototype.


title: Red Teaming Language Models with Language Models

abstract:
  Language Models (LMs) often cannot be deployed because of their potential to
harm users in hard-to-predict ways. Prior work identifies harmful behaviors
before deployment by using human annotators to hand-write test cases. However,
human annotation is expensive, limiting the number and diversity of test cases.
In this work, we automatically find cases where a target LM behaves in a
harmful way, by generating test cases ("red teaming") using another LM. We
evaluate the target LM's replies to generated test questions using a classifier
trained to detect offensive content, uncovering tens of thousands of offensive
replies in a 280B parameter LM chatbot. We explore several methods, from
zero-shot generation to reinforcement learning, for generating test cases with
varying levels of diversity and difficulty. Furthermore, we use prompt
engineering to control LM-generated test cases to uncover a variety of other
harms, automatically finding groups of people that the chatbot discusses in
offensive ways, personal and hospital phone numbers generated as the chatbot's
own contact info, leakage of private training data in generated text, and harms
that occur over the course of a conversation. Overall, LM-based red teaming is
one promising tool (among many needed) for finding and fixing diverse,
undesirable LM behaviors before impacting users.


title:
Gaussian Graphical Models as an Ensemble Method for Distributed Gaussian
  Processes

abstract:
  Distributed Gaussian process (DGP) is a popular approach to scale GP to big
data which divides the training data into some subsets, performs local
inference for each partition, and aggregates the results to acquire global
prediction. To combine the local predictions, the conditional independence
assumption is used which basically means there is a perfect diversity between
the subsets. Although it keeps the aggregation tractable, it is often violated
in practice and generally yields poor results. In this paper, we propose a
novel approach for aggregating the Gaussian experts' predictions by Gaussian
graphical model (GGM) where the target aggregation is defined as an unobserved
latent variable and the local predictions are the observed variables. We first
estimate the joint distribution of latent and observed variables using the
Expectation-Maximization (EM) algorithm. The interaction between experts can be
encoded by the precision matrix of the joint distribution and the aggregated
predictions are obtained based on the property of conditional Gaussian
distribution. Using both synthetic and real datasets, our experimental
evaluations illustrate that our new method outperforms other state-of-the-art
DGP approaches.


title: Exclusive photoproduction of heavy quarkonia pairs

abstract:
  In this paper we study the high energy exclusive photoproduction of heavy
quarkonia pairs in the leading order of the strong coupling constant
$\alpha_{s}$. In the suggested mechanism the quarkonia pairs are produced with
opposite charge parities, and have predominantly oppositely directed transverse
momenta. Using the Color Glass Condensate approach, we estimated numerically
the production cross-sections in the kinematics of the forthcoming
electron-proton colliders, as well as proton-ion colliders in ultraperipheral
collisions. We found that the cross-sections are within the reach of planned
experiments and can be measured with reasonable precision. The suggested
mechanism has significantly larger cross-section than that of the same
$C$-parity quarkonia pair production.


title:
Approximation error of single hidden layer neural networks with fixed
  weights

abstract:
  This paper provides an explicit formula for the approximation error of single
hidden layer neural networks with two fixed weights.


title: A Novel Max Pressure Algorithm Based on Traffic Delay

abstract:
  This paper considers a novel travel-delay-based Max Pressure algorithm for
control of arbitrary transportation networks with signalized intersections. The
traditional queue-based Max Pressure algorithm has received tremendous
attention recently due to its ease of implementation and scalability to large
network scenarios. The queue-based Max Pressure algorithm also has a desirable
property called maximum stability, which means a demand scenario can be
accommodated by this method as long as it can be accommodated by any existing
control policy. However, implementation of the queue-based Max Pressure
algorithm may be difficult in practice as estimation of queue lengths at
intersections requires significant measurement infrastructure. The queue-based
framework also uses a point queue model to represent the vehicle transition
between links, which does not consider the position of the vehicles, even
though this may significantly impact the control performance. In addition,
intersection approaches with low travel demand can incur arbitrarily large
delays due to having short queues. The proposed travel-delay based Max Pressure
model overcomes these drawbacks while inheriting the maximum stability feature
of the queue-based alternative. It also is shown to outperform several
benchmark Max Pressure variants in a battery of simulation tests. Lastly, the
proposed algorithm can be implemented in a Connected Vehicle (CV) environment,
in which a subset of vehicles serve as mobile probes. The results show the
proposed travel-delay-based model generates lower delay with non-full
penetration rate than the benchmark models with full penetration rate under
certain traffic conditions.


title:
Mental Disorders on Online Social Media Through the Lens of Language and
  Behaviour: Analysis and Visualisation

abstract:
  Due to the worldwide accessibility to the Internet along with the continuous
advances in mobile technologies, physical and digital worlds have become
completely blended, and the proliferation of social media platforms has taken a
leading role over this evolution. In this paper, we undertake a thorough
analysis towards better visualising and understanding the factors that
characterise and differentiate social media users affected by mental disorders.
We perform different experiments studying multiple dimensions of language,
including vocabulary uniqueness, word usage, linguistic style, psychometric
attributes, emotions' co-occurrence patterns, and online behavioural traits,
including social engagement and posting trends. Our findings reveal significant
differences on the use of function words, such as adverbs and verb tense, and
topic-specific vocabulary, such as biological processes. As for emotional
expression, we observe that affected users tend to share emotions more
regularly than control individuals on average. Overall, the monthly posting
variance of the affected groups is higher than the control groups. Moreover, we
found evidence suggesting that language use on micro-blogging platforms is less
distinguishable for users who have a mental disorder than other less
restrictive platforms. In particular, we observe on Twitter less quantifiable
differences between affected and control groups compared to Reddit.


title:
Pressure-induced concomitant topological and metal-insulator quantum
  phase transitions in Ce$_3$Pd$_3$Bi$_4$

abstract:
  The electronic property and magnetic susceptibility of Ce$_3$Pd$_3$Bi$_4$
were systemically investigated from 18 K to 290 K for varying values of
cell-volume using dynamic mean-field theory coupled with density functional
theory. By extrapolating to zero temperature, the ground state of
Ce$_3$Pd$_3$Bi$_4$ at ambient pressure is found to be a correlated semimetal
due to insufficient hybridization. Upon applying pressure, the hybridization
strength increases and a crossover to Kondo insulator is observed at finite
temperatures. The characteristic temperature signaling the formation of Kondo
singlet, as well as the characteristic temperature associated with $f$-electron
delocalization-localization change, simultaneously vanishes around a critical
volume of 0.992$V_0$, suggesting that such metal-insulator transition is
possibly associated with a quantum critical point. Finally, the Wilson's loop
calculations indicate that the Kondo insulating side is topologically trivial,
thus a topological transition also occurs across the quantum critical point.


title:
Composable and Modular Code Generation in MLIR: A Structured and
  Retargetable Approach to Tensor Compiler Construction

abstract:
  Despite significant investment in software infrastructure, machine learning
systems, runtimes and compilers do not compose properly. We propose a new
design aiming at providing unprecedented degrees of modularity, composability
and genericity. This paper discusses a structured approach to the construction
of domain-specific code generators for tensor compilers, with the stated goal
of improving the productivity of both compiler engineers and end-users. The
approach leverages the natural structure of tensor algebra. It has been the
main driver for the design of progressive lowering paths in \MLIR. The proposed
abstractions and transformations span data structures and control flow with
both functional (SSA form) and imperative (side-effecting) semantics. We
discuss the implications of this infrastructure on compiler construction and
present preliminary experimental results.


title: The exact renormalization group and dimensional regularization

abstract:
  The exact renormalization group (ERG) is formulated implementing the
decimation of degrees of freedom by means of a particular momentum integration
measure. The definition of this measure involves a distribution that links this
decimation process with the dimensional regularization technique employed in
field theory calculations. Taking the dimension d=4-\epsilon, the one loop
solutions to the ERG equations for the scalar field theory in this scheme are
shown to coincide with the dimensionally regularized perturbative field theory
calculation in the limit \mu\to0, if a particular relation between the scale
parameter \mu and \epsilon is employed. In general, it is shown that in this
scheme the solutions to the ERG equations for the proper functions coincide
when \mu\to0 with the complete diagrammatic contributions appearing in field
theory for these functions and this theory, provided that exact relations
between \mu and \epsilon hold. In addition a non-perturbative approximation is
considered. This approximation consists in a truncation of the ERG equations,
which by means of a low momentum expansion leads to reasonable results.


title:
Theoretical characterization of uncertainty in high-dimensional linear
  classification

abstract:
  Being able to reliably assess not only the accuracy but also the uncertainty
of models' predictions is an important endeavour in modern machine learning.
Even if the model generating the data and labels is known, computing the
intrinsic uncertainty after learning the model from a limited number of samples
amounts to sampling the corresponding posterior probability measure. Such
sampling is computationally challenging in high-dimensional problems and
theoretical results on heuristic uncertainty estimators in high-dimensions are
thus scarce. In this manuscript, we characterise uncertainty for learning from
limited number of samples of high-dimensional Gaussian input data and labels
generated by the probit model. We prove that the Bayesian uncertainty (i.e. the
posterior marginals) can be asymptotically obtained by the approximate message
passing algorithm, bypassing the canonical but costly Monte Carlo sampling of
the posterior. We then provide a closed-form formula for the joint statistics
between the logistic classifier, the uncertainty of the statistically optimal
Bayesian classifier and the ground-truth probit uncertainty. The formula allows
us to investigate calibration of the logistic classifier learning from limited
amount of samples. We discuss how over-confidence can be mitigated by
appropriately regularising, and show that cross-validating with respect to the
loss leads to better calibration than with the 0/1 error.


title:
Reconfigurable Intelligent Surface With Energy Harvesting Assisted
  Cooperative Ambient Backscatter Communications

abstract:
  The performance of cooperative ambient backscatter communications (CABC) can
be enhanced by employing reconfigurable intelligent surface (RIS) to assist
backscatter transmitters. Since the RIS power consumption is a non-negligible
issue, we consider a RIS assisted CABC system where the RIS with energy
harvesting circuit can not only reflect signal but also harvest wireless
energy. We study a transmission design problem to minimize the RIS power
consumption with the quality of service constraints for both active and
backscatter transmissions. The optimization problem is a mixed-integer
non-convex programming problem which is NP-hard. To tackle it, an algorithm is
proposed by employing the block coordinate descent, semidefinite relaxation and
alternating direction method of multipliers techniques. Simulation results
demonstrate the effectiveness of the proposed algorithm.


title: Grassmann Stein Variational Gradient Descent

abstract:
  Stein variational gradient descent (SVGD) is a deterministic particle
inference algorithm that provides an efficient alternative to Markov chain
Monte Carlo. However, SVGD has been found to suffer from variance
underestimation when the dimensionality of the target distribution is high.
Recent developments have advocated projecting both the score function and the
data onto real lines to sidestep this issue, although this can severely
overestimate the epistemic (model) uncertainty. In this work, we propose
Grassmann Stein variational gradient descent (GSVGD) as an alternative
approach, which permits projections onto arbitrary dimensional subspaces.
Compared with other variants of SVGD that rely on dimensionality reduction,
GSVGD updates the projectors simultaneously for the score function and the
data, and the optimal projectors are determined through a coupled
Grassmann-valued diffusion process which explores favourable subspaces. Both
our theoretical and experimental results suggest that GSVGD enjoys efficient
state-space exploration in high-dimensional problems that have an intrinsic
low-dimensional structure.


title: On the growth of multi-recurrences

abstract:
  In this paper we provide a complete proof for a bound on the growth of
multi-recurrences which are defined over a number field. The proven bound was
already stated by van der Poorten and Schlickewei forty years ago.


title: Training OOD Detectors in their Natural Habitats

abstract:
  Out-of-distribution (OOD) detection is important for machine learning models
deployed in the wild. Recent methods use auxiliary outlier data to regularize
the model for improved OOD detection. However, these approaches make a strong
distributional assumption that the auxiliary outlier data is completely
separable from the in-distribution (ID) data. In this paper, we propose a novel
framework that leverages wild mixture data -- that naturally consists of both
ID and OOD samples. Such wild data is abundant and arises freely upon deploying
a machine learning classifier in their \emph{natural habitats}. Our key idea is
to formulate a constrained optimization problem and to show how to tractably
solve it. Our learning objective maximizes the OOD detection rate, subject to
constraints on the classification error of ID data and on the OOD error rate of
ID examples. We extensively evaluate our approach on common OOD detection tasks
and demonstrate superior performance.


title: Thermodynamic parametrization of dark energy

abstract:
  We propose a model-independent parametrization of dark energy motivated by
thermodynamics. To this aim, we consider Pad\'e polynomials to reconstruct the
form of deceleration parameter adequate to describe different epochs of cosmic
history and divergence-free in the far future. The proposed scenario also
fulfills the demand of structure formation and contains the $\Lambda$CDM model
as a limiting case. Thus, a numerical analysis at both background and
perturbation levels is performed through the Markov Chain Monte Carlo method in
view of the most recent cosmic data. We then use the observational constraints
to explore the features of dark energy evolution and compare our findings with
the predictions of the standard cosmological model.


title:
Some Results on the Improved Bound and Construction of Optimal
  $(r,\delta)$ LRCs

abstract:
  Locally repairable codes (LRCs) with $(r,\delta)$ locality were introduced by
Prakash \emph{et al.} into distributed storage systems (DSSs) due to their
benefit of locally repairing at least $\delta-1$ erasures via other $r$
survival nodes among the same local group. An LRC achieving the $(r,\delta)$
Singleton-type bound is called an optimal $(r,\delta)$ LRC. Constructions of
optimal $(r,\delta)$ LRCs with longer code length and determining the maximal
code length have been an important research direction in coding theory in
recent years. In this paper, we conduct further research on the improvement of
maximum code length of optimal $(r,\delta)$ LRCs. For $2\delta+1\leq d\leq
2\delta+2$, our upper bounds largely improve the ones by Cai \emph{et al.},
which are tight in some special cases. Moreover, we generalize the results of
Chen \emph{et al.} and obtain a complete characterization of optimal $(r=2,
\delta)$-LRCs in the sense of geometrical existence in the finite projective
plane $PG(2,q)$. Within this geometrical characterization, we construct a class
of optimal $(r,\delta)$ LRCs based on the sunflower structure. Both the
construction and upper bounds are better than previous ones.


title:
Numerical analysis for the interaction of mean curvature flow and
  diffusion on closed surfaces

abstract:
  An evolving surface finite element discretisation is analysed for the
evolution of a closed two-dimensional surface governed by a system coupling a
generalised forced mean curvature flow and a reaction--diffusion process on the
surface, inspired by a gradient flow of a coupled energy. Two algorithms are
proposed, both based on a system coupling the diffusion equation to evolution
equations for geometric quantities in the velocity law for the surface.
  One of the numerical methods is proved to be convergent in the $H^1$ norm
with optimal-order for finite elements of degree at least two.
  We present numerical experiments illustrating the convergence behaviour and
demonstrating the qualitative properties of the flow: preservation of mean
convexity, loss of convexity, weak maximum principles, and the occurrence of
self-intersections.


title:
Serre weights for three-dimensional wildly ramified Galois
  representations

abstract:
  We formulate and prove the weight part of Serre's conjecture for
three-dimensional mod $p$ Galois representations under a genericity condition
when the field is unramified at $p$. This removes the assumption in
\cite{arXiv:1512.06380}, \cite{arXiv:1608.06570} that the representation be
tamely ramified at $p$. We also prove a version of Breuil's lattice conjecture
and a mod $p$ multiplicity one result for the cohomology of $U(3)$-arithmetic
manifolds. The key input is a study of the geometry of the Emerton--Gee stacks
\cite{arXiv:2012.12719} using the local models introduced in
\cite{arXiv:2007.05398}.


title: AdS/BCFT and Island for curvature-squared gravity

abstract:
  In this paper, we investigate AdS/BCFT for curvature-squared gravity. To warm
up, we start with Gauss-Bonnet gravity. We derive the one point function of
stress tensor and show that the central charge related to the norm of
displacement operator is positive for the couplings obeying causality
constraints. Furthermore, by imposing the null energy condition on the
end-of-the-world brane, we prove the holographic g-theorem for Gauss-Bonnet
gravity. This corrects a wrong point of view in the literature, which claims
that the holographic g-theorem is violated for Gauss-Bonnet gravity. We also
study AdS/BCFT for general curvature-squared gravity. We find that it is too
restrictive for the shape of the brane and the dual BCFT is trivial if one
imposes Neumann boundary conditions for all of the gravitational modes.
Instead, we propose to impose Dirichlet boundary condition for the massive
graviton, while imposing Neumann boundary condition for the massless graviton.
In this way, we obtain non-trivial shape dependence of stress tensor and
well-defined central charges. In particular, the holographic g-theorem is
satisfied by general curvature-squared gravity. Finally, we discuss the island
and show that the Page curve can be recovered for Gauss-Bonnet gravity.
Interestingly, there are first-order phase transitions for the Page curve
within one range of couplings obeying causality constraints. Generalizing the
discussions to holographic complexity, we get a new constraint for the
Gauss-Bonnet coupling, which is stronger than the causality constraint.


title:
The Near-Infrared Spectrograph (NIRSpec) on the James Webb Space
  Telescope I. Overview of the instrument and its capabilities

abstract:
  We provide an overview of the design and capabilities of the near-infrared
spectrograph (NIRSpec) onboard the James Webb Space Telescope. NIRSpec is
designed to be capable of carrying out low-resolution ($R\!=30\!-330$) prism
spectroscopy over the wavelength range $0.6-5.3\!~\mu$m and higher resolution
($R\!=500\!-1340$ or $R\!=1320\!-3600$) grating spectroscopy over
$0.7-5.2\!~\mu$m, both in single-object mode employing any one of five fixed
slits, or a 3.1$\times$3.2 arcsec$^2$ integral field unit, or in multiobject
mode employing a novel programmable micro-shutter device covering a
3.6$\times$3.4~arcmin$^2$ field of view. The all-reflective optical chain of
NIRSpec and the performance of its different components are described, and some
of the trade-offs made in designing the instrument are touched upon. The
faint-end spectrophotometric sensitivity expected of NIRSpec, as well as its
dependency on the energetic particle environment that its two detector arrays
are likely to be subjected to in orbit are also discussed.


title:
The Near-Infrared Spectrograph (NIRSpec) on the James Webb Space
  Telescope II. Multi-object spectroscopy (MOS)

abstract:
  We provide an overview of the capabilities and performance of the
Near-Infrared Spectrograph (NIRSpec) on the James Webb Space Telescope (JWST)
when used in its multi-object spectroscopy (MOS) mode employing a novel Micro
Shutter Array (MSA) slit device. The MSA consists of four separate 98 arcsec
$\times$ 91 arcsec quadrants each containing $365\times171$ individually
addressable shutters whose open areas on the sky measure 0.20 arcsec $\times$
0.46 arcsec on a 0.27 arcsec $\times$ 0.53 arcsec pitch. This is the first time
that a configurable multi-object spectrograph has been available on a space
mission. The levels of multiplexing achievable with NIRSpec MOS mode are
quantified and we show that NIRSpec will be able to observe typically fifty to
two hundred objects simultaneously with the pattern of close to a quarter of a
million shutters provided by the MSA. This pattern is fixed and regular, and we
identify the specific constraints that it yields for NIRSpec observation
planning. We also present the data processing and calibration steps planned for
the NIRSpec MOS data. The significant variation in size of the mostly
diffraction-limited instrument point spread function over the large wavelength
range of 0.6-5.3 $\mu$m covered by the instrument, combined with the fact that
most targets observed with the MSA cannot be expected to be perfectly centred
within their respective slits, makes the spectrophotometric and wavelength
calibration of the obtained spectra particularly complex. These challenges
notwithstanding, the sensitivity and multiplexing capabilities anticipated of
NIRSpec in MOS mode are unprecedented, and should enable significant progress
to be made in addressing a wide range of outstanding astrophysical problems.


title:
$L^p$ Boundedness of the Scattering Wave Operators of Schr\"odinger
  Dynamics -- Part 2

abstract:
  We give another proof of the $L^p$ boundedness of scattering wave operators,
at the low frequency part of the data. The proof also allows the control of the
commutator of multiplication by $|x|$ with the wave operator in $L^p$. The
method we develop here is geared to proving similar results for \emph{time
dependent potentials}, complementing previous work focused on the high
frequency part.


title:
The Near-Infrared Spectrograph (NIRSpec) on the James Webb Space
  Telescope III. Integral-field spectroscopy

abstract:
  The Near-Infrared Spectrograph (NIRSpec) on the James Webb Space Telescope
(JWST) offers the first opportunity to use integral-field spectroscopy from
space at near-infrared wavelengths. More specifically, NIRSpec's integral-field
unit can obtain spectra covering the wavelength range $0.6 - 5.3~\mu$m for a
contiguous 3.1 arcsec $\times$ 3.2 arcsec sky area at spectral resolutions of
$R \approx 100$, 1000, and 2700. In this paper we describe the optical and
mechanical design of the NIRSpec integral-field spectroscopy mode, together
with its expected performance. We also discuss a few recommended observing
strategies, some of which are driven by the fact that NIRSpec is a multipurpose
instrument with a number of different observing modes, which are discussed in
companion papers. We briefly discuss the data processing steps required to
produce wavelength- and flux-calibrated data cubes that contain the spatial and
spectral information. Lastly, we mention a few scientific topics that are bound
to benefit from this highly innovative capability offered by JWST/NIRSpec.


title:
The Near-Infrared Spectrograph (NIRSpec) on the James Webb Space
  Telescope IV. Capabilities and predicted performance for exoplanet
  characterization

abstract:
  The Near-Inrared Spectrograph (NIRSpec) on the James Webb Space Telescope
(JWST) is a very versatile instrument, offering multiobject and integral field
spectroscopy with varying spectral resolution ($\sim$30 to $\sim$3000) over a
wide wavelength range from 0.6 to 5.3 micron, enabling scientists to study many
science themes ranging from the first galaxies to bodies in our own Solar
System. In addition to its integral field unit and support for multiobject
spectroscopy, NIRSpec features several fixed slits and a wide aperture
specifically designed to enable high precision time-series and transit as well
as eclipse observations of exoplanets. In this paper we present its
capabilities regarding time-series observations, in general, and transit and
eclipse spectroscopy of exoplanets in particular. Due to JWST's large
collecting area and NIRSpec's excellent throughput, spectral coverage, and
detector performance, this mode will allow scientists to characterize the
atmosphere of exoplanets with unprecedented sensitivity.


title:
Exploratory analysis of text duplication in peer-review reveals
  peer-review fraud and paper mills

abstract:
  Comments received from referees during peer-review were analysed to determine
the rates of duplication and partial duplication. It is very unusual for 2
different referees to submit identical comments, so the rare cases where this
happens are of interest. In some cases, it appears that paper-mills create fake
referee accounts and use them to submit fake peer-review reports. These include
comments that are copied and pasted across multiple reviews. Searching for
duplication in referee comments is therefore an effective method to search for
misconduct generally, since the forms of misconduct committed by paper-mills go
beyond peer-review fraud. These search methods allow the automatic detection of
misconduct candidates which may then be investigated carefully to confirm if
misconduct has indeed taken place. There are innocent reasons why referees
might share template reports, so these methods are not intended to
automatically diagnose misconduct.


title: Giant topological and planar Hall effect in Cr$_{1/3}$NbS$_{2}$

abstract:
  Cr$_{1/3}$NbS$_{2}$ is a transition metal dichalcogenide that has been of
significant interest due to its ability to host a magnetic chiral soliton
lattice. Conventional and planar Hall measurements provide valuable insight
into the detection of exotic spin structures in chiral magnets. We show that
the presence of a giant planar Hall effect can be attributed to a tilted
soliton lattice in Cr$_{1/3}$NbS$_{2}$. Our detailed angular dependent study
shows the planar Hall effect and anisotropic magnetoresistance are
intrinsically linked in complex non-coplanar magnets. From the conventional
Hall signal we show the presence of a giant unconventional, likely topological
Hall component, that is the fingerprint of non-coplanar spin textures.


title:
Sensitivity driven experimental design to facilitate control of
  dynamical systems

abstract:
  Control of nonlinear dynamical systems is a complex and multifaceted process.
Essential elements of many engineering systems include high fidelity
physics-based modeling, offline trajectory planning, feedback control design,
and data acquisition strategies to reduce uncertainties. This article proposes
an optimization centric perspective which couples these elements in a cohesive
framework. We introduce a novel use of hyper-differential sensitivity analysis
to understand the sensitivity of feedback controllers to parametric uncertainty
in physics-based models used for trajectory planning. These sensitivities
provide a foundation to define an optimal experimental design which seeks to
acquire data most relevant in reducing demand on the feedback controller. Our
proposed framework is illustrated on the Zermelo navigation problem and a
hypersonic trajectory control problem using data from NASA's X-43 hypersonic
flight tests.


title:
A Robust, Modern Strategy for Treating Coherence Pathways in Unstable
  and Inhomogeneous Magnetic Resonance Experiments

abstract:
  Over recent decades, motivated either by practicality or the need to tap into
new types of measurements, the science of Magnetic Resonance has expanded into
more adverse conditions: deliberately chosen lower frequencies, inhomogeneous
fields, and/or time-variable fields. In particular, Overhauser Dynamic Nuclear
Polarization (ODNP) presents a case study that challenges previous expectations
and offers an interesting test-bed for further developments. For example, an
interest in the nanoscale heterogeneities of hydration dynamics demand
increasingly sophisticated and automated measurements deploying ODNP on a
modular, open source instrument operating at 15 MHz. ODNP demands the
acquisition and automated processing of large quantities of one dimensional NMR
spectra, which can present various problems: in particular, unambiguous
identification of signal in newly configured instruments presents a practical
challenge, while field drift tends to remain an issue even in fully configured
instruments.
  Recent advances in the capabilities of open-source libraries opened up the
opportunity to address these issues at the fundamental level, by developing a
specific schema that treats the phase cycle of a pulse as an explicit "phase
domain" dimension that Fourier transforms into the "coherence domain." In
particular, a standardized protocol for organizing and visualizing the
resulting data clearly presents all the information available from all
coherence transfer pathways of a phase-cycled experiment, with intelligible
results that don't rely on preliminary phase corrections. It thus organizes and
visualizes data in ways that more accurately reflect the rich physics of the
underlying NMR experiments, and that more fully bear out the original
fundamental concepts of coherence transfer pathways.


title: A Robot Web for Distributed Many-Device Localisation

abstract:
  We show that a distributed network of robots or other devices which make
measurements of each other can collaborate to globally localise via efficient
ad-hoc peer to peer communication. Our Robot Web solution is based on Gaussian
Belief Propagation on the fundamental non-linear factor graph describing the
probabilistic structure of all of the observations robots make internally or of
each other, and is flexible for any type of robot, motion or sensor. We define
a simple and efficient communication protocol which can be implemented by the
publishing and reading of web pages or other asynchronous communication
technologies. We show in simulations with up to 1000 robots interacting in
arbitrary patterns that our solution convergently achieves global accuracy as
accurate as a centralised non-linear factor graph solver while operating with
high distributed efficiency of computation and communication. Via the use of
robust factors in GBP, our method is tolerant to a high percentage of faults in
sensor measurements or dropped communication packets.


title:
Ultrafast dynamics in (TaSe$_4$)$_2$I triggered by valence and
  core-level excitation

abstract:
  In this work, we study the out-of-equilibrium dynamics of the paradigmatic
quasi-one-dimensional material (TaSe$_4$)$_2$I, that exhibits a transition into
an incommensurate CDW phase when cooled just below room temperature, namely at
T$_{\rm{CDW}} $= 263 K. We make use of both optical laser and free-electron
laser (FEL) based time-resolved spectroscopies in order to study the effect of
a selective excitation on the normal-state and on the CDW phases, by probing
the near-infrared/visible optical properties both along and perpendicularly to
the direction of the CDW, where the system is metallic and insulating,
respectively. Excitation of the core-levels by ultrashort X-ray FEL pulses at
47 eV and 119 eV induces reflectivity transients resembling those recorded when
only exciting the valence band of the compound - by near-infrared pulses at
1.55 eV - in the case of the insulating sub-system. Conversely, the metallic
sub-system displays a relaxation dynamics which depends on the energy of
photo-excitation. Moreover, excitation of the CDW amplitude mode is recorded
only for excitation at low-photon-energy. This fact suggests that the coupling
of light to ordered states of matter can predominantly be achieved when
directly injecting delocalized carriers in the valence band, rather than
localized excitations in the core levels. On a complementary side, table-top
experiments allow us to prove the quasi-unidirectional nature of the CDW phase
in (TaSe$_4$)$_2$I, whose fingerprints are detected along its $c$-axis only.
Our results provide new insights on the symmetry of the ordered phase of
(TaSe$_4$)$_2$I perturbed by a selective excitation, and suggest a novel
approach based on complementary table-top and FEL spectroscopies for the study
of complex materials.


title: Bow-Tie Structures of Twitter Discursive Communities

abstract:
  Bow-tie structures were introduced to describe the World Wide Web: in the
direct network in which the nodes are the websites and the edges are the
hyperlinks connecting them, the greatest number of nodes take part to a
bow-tie, i.e. a Weakly Connected Component (WCC) composed of 3 main sectors:
IN, OUT and SCC. SCC is the main Strongly Connected Component of WCC: it
contains the greatest subgraph in which each node is reachable by any other one
in the subgraph. The IN and OUT sectors are the set of nodes not included in
SCC that, respectively, can access and are accessible to nodes in SCC. In SCC
the greatest part of the websites can be found, while the search engines
belongs to IN, and the authorities, as Wikipedia, are in OUT. In the analysis
of Twitter debate, the recent literature focused on discursive communities,
i.e. clusters of accounts interacting among themselves via retweets. In the
present work, we studied discursive communities in 8 different thematic Twitter
data sets in various languages. Surprisingly, we observed that almost all
discursive communities therein display a bow-tie structure during political or
societal debates. Instead, they are absent when the argument of the discussion
is different as sport events, as in the case of Euro2020 Turkish and Italian
data sets. We furthermore analysed the quality of the content created in the
various sectors, using the domain annotation from the fact-checking website
Newsguard: it turns out that content with the lowest quality is the ones
produced and shared in SCC. In this sense, in discursive communities displaying
great OUT blocks, the greatest part of the accounts has access to a great
variety of contents, but their quality is, in general, quite low, creating the
phenomenon known as infodemic. In the present paper, we correlate the presence
of an infodemic to a peculiar network structure, i.e. a OUT-dominant bow-tie.


title:
Pressure-induced magnetic and topological transitions in
  non-centrosymmetric MnIn$_{2}$Te$_{4}$

abstract:
  The discovery of time-reversal-invariant topological states has drawn great
attention in recent decades. However, despite the potential of displaying a
variety of exotic physics, the study of magnetic topological phases lags behind
due to underlying added complexity of magnetism. In this work, we predict the
interplay of magnetism and topology in the non-centrosymmetric ternary
manganese compound MnIn$_2$Te$_4$, using first-principles calculations. At
ambient pressure, the ground state of the system is an antiferromagnetic
insulator. With the application of small hydrostatic pressure ($\sim$0.50 GPa),
it undergoes a magnetic transition and the ferromagnetic state becomes
energetically favourable. At $\sim$2.92 GPa, the system undergoes a transition
into a Weyl semimetallic phase, which hosts multiple Weyl points in the bulk
and is associated with non-trivial surface Fermi arcs. Remarkably, we discover
that the number of Weyl points in this system can be controlled by pressure and
that these manifest in an anomalous Hall conductivity (AHC). In addition to
proposing a new candidate magnetic topological material, our work demonstrates
that pressure can be an effective way to induce and control topological phases,
as well as AHC, in magnetic materials. These properties may allow our proposed
material to be used as a novel pressure-controlled Hall switch.


title: Symmetry-breaking induced frequency combs in graphene resonators

abstract:
  Nonlinearities are inherent to the dynamics of two-dimensional materials.
Phenomena like intermodal coupling already arise at amplitudes of only a few
nanometers, and a range of unexplored effects still awaits to be harnessed.
Here, we demonstrate a route for generating mechanical frequency combs in
graphene resonators undergoing symmetry-breaking forces. We use electrostatic
force to break the membrane's out-of-plane symmetry and tune its resonance
frequency towards a two-to-one internal resonance, thus achieving strong
coupling between two of its mechanical modes. When increasing the drive level,
we observe splitting of the fundamental resonance peak, followed by the
emergence of a frequency comb regime. We attribute the observed physics to a
non-symmetric restoring potential, and show that the frequency comb regime is
mediated by a Neimark bifurcation of the periodic solution. These results
demonstrate that mechanical frequency combs and chaotic dynamics in 2D material
resonators can emerge near internal resonances due to symmetry-breaking.


title:
Estimation of the tail index of Pareto-type distributions using
  regularisation

abstract:
  In this paper, we introduce reduced-bias estimators for the estimation of the
tail index of a Pareto-type distribution. This is achieved through the use of a
regularised weighted least squares with an exponential regression model for
log-spacings of top order statistics. The asymptotic properties of the proposed
estimators are investigated analytically and found to be asymptotically
unbiased, consistent and normally distributed. Also, the finite sample
behaviour of the estimators are studied through a simulations theory. The
proposed estimators were found to yield low bias and MSE. In addition, the
proposed estimators are illustrated through the estimation of the tail index of
the underlying distribution of claims from the insurance industry.


title:
Layer coupling between solutal and thermal convection in liquid metal
  batteries

abstract:
  For longer than one decade, liquid metal batteries (LMBs) are developed with
the primary aim to provide economic stationary energy storage. Featuring two
liquid metal electrodes separated by a molten salt electrolyte, LMBs operate at
elevated temperature as simple concentration cells. Therefore, efficient mass
transfer is a basic prerequisite for their economic operation. Understanding
these mechanisms cannot be limited at the single layer level. With this
motivation, the effects of solutal- and thermally-driven flow are studied, as
well as the flow coupling between the three liquid layers of the cell. It is
shown that solutal convection appears first and thermal convection much later.
While the presence of solutal flow depends on the mode of operation (charge or
discharge), the occurrence of thermal convection is dictated by the geometry
(thickness of layers). The coupling of the flow phenomena between the layers is
intriguing: while thermal convection is confined to its area of origin, i.e.
the electrolyte, solutal convection is able to drive flow in the positive
electrode and in the electrolyte.


title: A characterization of maximally entangled two-qubit states

abstract:
  As already known by Rana's result
\href{https://doi.org/10.1103/PhysRevA.87.054301}{[\pra {\bf87} (2013)
054301]}, all eigenvalues of any partial-transposed bipartite state fall within
the closed interval $[-\frac12,1]$. In this note, we study a family of
bipartite quantum states whose minimal eigenvalues of partial-transposed states
being $-\frac12$. For a two-qubit system, we find that the minimal eigenvalue
of its partial-transposed state is $-\frac12$ if and only if such two-qubit
state must be maximally entangled. However this result does not hold in general
for a two-qudit system when the dimensions of the underlying space are larger
than two.


title: Reducing the Vertex Cover Number via Edge Contractions

abstract:
  The CONTRACTION(vc) problem takes as input a graph $G$ on $n$ vertices and
two integers $k$ and $d$, and asks whether one can contract at most $k$ edges
to reduce the size of a minimum vertex cover of $G$ by at least $d$. Recently,
Lima et al. [JCSS 2021] proved, among other results, that unlike most of the
so-called blocker problems, CONTRACTION(vc) admits an XP algorithm running in
time $f(d) \cdot n^{O(d)}$. They left open the question of whether this problem
is FPT under this parameterization. In this article, we continue this line of
research and prove the following results:
  1. CONTRACTION(vc) is W[1]-hard parameterized by $k + d$. Moreover, unless
the ETH fails, the problem does not admit an algorithm running in time $f(k +
d) \cdot n^{o(k + d)}$ for any function $f$. In particular, this answers the
open question stated in Lima et al. [JCSS 2021] in the negative.
  2. It is NP-hard to decide whether an instance $(G, k, d)$ of CONTRACTION(vc)
is a yes-instance even when $k = d$, hence enhancing our understanding of the
classical complexity of the problem.
  3. CONTRACTION(vc) can be solved in time $2^{O(d)} \cdot n^{k - d + O(1)}$.
This XP algorithm improves the one of Lima et al. [JCSS 2021], which uses
Courcelle's theorem as a subroutine and hence, the $f(d)$-factor in the running
time is non-explicit and probably very large. On the other hard, it shows that
when $k=d$, the problem is FPT parameterized by $d$ (or by $k$).


title: Motion-Plane-Adaptive Inter Prediction in 360-Degree Video Coding

abstract:
  Inter prediction is one of the key technologies enabling the high compression
efficiency of modern video coding standards. 360-degree video needs to be
mapped to the 2D image plane prior to coding in order to allow compression
using existing video coding standards. The distortions that inevitably occur
when mapping spherical data onto the 2D image plane, however, impair the
performance of classical inter prediction techniques. In this paper, we propose
a motion-plane-adaptive inter prediction technique (MPA) for 360-degree video
that takes the spherical characteristics of 360-degree video into account.
Based on the known projection format of the video, MPA allows to perform inter
prediction on different motion planes in 3D space instead of having to work on
the - in theory arbitrarily mapped - 2D image representation directly. We
furthermore derive a motion-plane-adaptive motion vector prediction technique
(MPA-MVP) that allows to translate motion information between different motion
planes and motion models. Our proposed integration of MPA together with MPA-MVP
into the state-of-the-art H.266/VVC video coding standard shows significant
Bjontegaard Delta rate savings of 1.72% with a peak of 3.97% based on PSNR and
1.56% with a peak of 3.40% based on WS-PSNR compared to the VTM-14.2 baseline
on average.


title: A Search for Technosignatures toward the Galactic Centre at 150 MHz

abstract:
  This paper is the fourth in a series of low-frequency searches for
technosignatures. Using the Murchison Widefield Array over two nights we
integrate 7 hours of data toward the Galactic Centre (centred on the position
of Sagittarius A*) with a total field-of-view of 200 deg^2. We present a
targeted search toward 144 exoplanetary systems, at our best yet angular
resolution (75 arc seconds). This is the first technosignature search at a
frequency of 155 MHz toward the Galactic Centre (our previous central
frequencies have been lower). A blind search toward in excess of 3 million
stars toward the Galactic Centre and Galactic bulge is also completed, placing
an equivalent isotropic power limit <1.1X10^19W at the distance to the Galactic
Centre. No plausible technosignatures are detected.


title:
Differential Privacy for Symbolic Systems with Application to Markov
  Chains

abstract:
  Data-driven systems are gathering increasing amounts of data from users, and
sensitive user data requires privacy protections. In some cases, the data
gathered is non-numerical or symbolic, and conventional approaches to privacy,
e.g., adding noise, do not apply, though such systems still require privacy
protections. Accordingly, we present a novel differential privacy framework for
protecting trajectories generated by symbolic systems. These trajectories can
be represented as words or strings over a finite alphabet. We develop new
differential privacy mechanisms that approximate a sensitive word using a
random word that is likely to be near it. An offline mechanism is implemented
efficiently using a Modified Hamming Distance Automaton to generate whole
privatized output words over a finite time horizon. Then, an online mechanism
is implemented by taking in a sensitive symbol and generating a randomized
output symbol at each timestep. This work is extended to Markov chains to
generate differentially private state sequences that a given Markov chain could
have produced. Statistical accuracy bounds are developed to quantify the
accuracy of these mechanisms, and numerical results validate the accuracy of
these techniques for strings of English words.


title: Optimal Ratio for Data Splitting

abstract:
  It is common to split a dataset into training and testing sets before fitting
a statistical or machine learning model. However, there is no clear guidance on
how much data should be used for training and testing. In this article we show
that the optimal splitting ratio is $\sqrt{p}:1$, where $p$ is the number of
parameters in a linear regression model that explains the data well.


title:
Probabilistic Consensus on Feature Distribution for Multi-robot Systems
  with Markovian Exploration Dynamics

abstract:
  In this paper, we present a consensus-based decentralized multi-robot
approach to reconstruct a discrete distribution of features, modeled as an
occupancy grid map, that represent information contained in a bounded planar 2D
environment, such as visual cues used for navigation or semantic labels
associated with object detection. The robots explore the environment according
to a random walk modeled by a discrete-time discrete-state (DTDS) Markov chain
and estimate the feature distribution from their own measurements and the
estimates communicated by neighboring robots, using a distributed Chernoff
fusion protocol. We prove that under this decentralized fusion protocol, each
robot's feature distribution converges to the ground truth distribution in an
almost sure sense. We verify this result in numerical simulations that show
that the Hellinger distance between the estimated and ground truth feature
distributions converges to zero over time for each robot. We also validate our
strategy through Software-In-The-Loop (SITL) simulations of quadrotors that
search a bounded square grid for a set of visual features distributed on a
discretized circle.


title: Magnetized pole-mass of neutral $\rho$ meson within full RPA evaluation

abstract:
  In this work we calculate the pole-mass of the $\rho^0$ meson with different
spin projections $s_z=0,\pm 1$ in the context of the magnetized two-flavor
Nambu--Jona--Lasinio model. Making use of the mean field approximation to
obtain the effective quark mass as a function of the magnetic field, we apply
the random phase approximation (RPA) to the vector channel in order to
calculate the polarization function for each spin component. We adopt the
magnetic field independent regularization (MFIR) in our evaluations as a method
of separating divergences and the Pauli-Villars regularization for the vacuum
contributions. The $\rho^0$ meson mass with spin projection $s_z=1$ is always
catalysed with the magnitude of the magnetic field, showing good agreement with
Lattice QCD results. The mass projection $s_z=0$ has a non-monotonic behavior,
decreasing until the minimum at $eB\lesssim 0.15$ GeV$^2$, which is in contrast
with available LQCD data.


title: Mock theta functions and related combinatorics

abstract:
  In this paper we add to the literature on the combinatorial nature of the
mock theta functions, a collection of curious $q$-hypergeometric series
introduced by Ramanujan in his last letter to Hardy in 1920, which we now know
to be important examples of mock modular forms. Our work is inspired by Beck's
conjecture, now a theorem of Andrews, related to Euler's identity: the excess
in the number of parts in all partitions of $n$ into odd parts over the number
of partitions of $n$ into distinct parts is equal to the number of partitions
with only one (possibly repeated) even part and all other parts odd. We
establish Beck-type identities associated to partition identities due to
Andrews, Dixit, and Yee for the third order mock theta functions $\omega(q),
\nu(q)$, and $\phi(q)$. Our proofs are both analytic and combinatorial in
nature, and involve mock theta generating functions and combinatorial
bijections.


title:
A toy model for brain criticality: self-organized excitation/inhibition
  ratio and the role of network clustering

abstract:
  The critical brain hypothesis receives increasing support from recent
experimental results. It postulates that the brain is at a critical point
between an ordered and a chaotic regime, sometimes referred to as the "edge of
chaos." Another central observation of neuroscience is the principle of
excitation-inhibition balance: Certain brain networks exhibit a remarkably
constant ratio between excitation and inhibition. When this balance is
perturbed, the network shifts away from the critical point, as may for example
happen during epileptic seizures. However, it is as of yet unclear what
mechanisms balance the neural dynamics towards this excitation-inhibition ratio
that ensures critical brain dynamics. Here we introduce a simple yet
biologically plausible toy model of a self-organized critical neural network
with a self-organizing excitation to inhibition ratio. The model only requires
a neuron to have local information of its own recent activity and changes
connections between neurons accordingly. We find that the network evolves to a
state characterized by avalanche distributions following universal scaling laws
typical of criticality, and to a specific excitation to inhibition ratio. The
model connects the two questions of brain criticality and of a specific
excitation/inhibition balance observed in the brain to a common origin or
mechanism. From the perspective of the statistical mechanics of such networks,
the model uses the excitation/inhibition ratio as control parameter of a phase
transition, which enables criticality at arbitrary high connectivities. We find
that network clustering plays a crucial role for this phase transition to
occur.


title: Magnetization dynamics affected by phonon pumping

abstract:
  "Pumping" of phonons by a dynamic magnetization promises to extend the range
and functionality of magnonic devices. We explore the impact of phonon pumping
on room-temperature ferromagnetic resonance (FMR) spectra of bilayers of thin
yttrium iron garnet (YIG) films on thick gadolinium gallium garnet substrates
over a wide frequency range. At low frequencies the Kittel mode hybridizes
coherently with standing ultrasound waves of a bulk acoustic resonator to form
magnon polarons that induce rapid oscillations of the magnetic susceptibility,
as reported before. At higher frequencies, the phonon resonances overlap,
merging into a conventional FMR line, but with an increased line width. The
frequency dependence of the increased line broadening follows the predictions
from phonon pumping theory in the thick substrate limit. In addition, we find
substantial magnon-phonon coupling of a perpendicular standing spin wave (PSSW)
mode. This evidences the importance of the mode overlap between the acoustic
and magnetic modes, and provides a route towards engineering the magnetoelastic
mode coupling.


title:
Forecasting Environmental Data: An example to ground-level ozone
  concentration surfaces

abstract:
  Environmental problems are receiving increasing attention in socio-economic
and health studies. This in turn fosters advances in recording and data
collection of many related real-life processes. Available tools for data
processing are often found too restrictive as they do not account for the rich
nature of such data sets. In this paper, we propose a new statistical
perspective on forecasting spatial environmental data collected sequentially
over time. We treat this data set as a surface (functional) time series with a
possibly complicated geographical domain. By employing novel techniques from
functional data analysis we develop a new forecasting methodology. Our approach
consists of two steps. In the first step, time series of surfaces are
reconstructed from measurements sampled over some spatial domain using a finite
element spline smoother. In the second step, we adapt the dynamic functional
factor model to forecast a surface time series. The advantage of this approach
is that we can account for and explore simultaneously spatial as well as
temporal dependencies in the data. A forecasting study of ground-level ozone
concentration over the geographical domain of Germany demonstrates the
practical value of this new perspective, where we compare our approach with
standard functional benchmark models.


title: Quantum Computing for Heavy Quarkonium Spectroscopy

abstract:
  We report a first demonstration for the application of quantum computing to
heavy quarkonium spectroscopy study. Based on a Cornell-potential model for the
heavy quark and antiquark system, we show how this Hamiltonian problem can be
formulated and solved with the VQE approach on the IBM cloud quantum computing
platform. Errors due to a global depolarizing noise channel are corrected with
a zero-noise extrapolation method, resulting in good agreement with the
expected value. We also extend the calculation to excited states on a noiseless
quantum simulator by orthogonalization with respect to the ground state.


title: Policy Optimization for Stochastic Shortest Path

abstract:
  Policy optimization is among the most popular and successful reinforcement
learning algorithms, and there is increasing interest in understanding its
theoretical guarantees. In this work, we initiate the study of policy
optimization for the stochastic shortest path (SSP) problem, a goal-oriented
reinforcement learning model that strictly generalizes the finite-horizon model
and better captures many applications. We consider a wide range of settings,
including stochastic and adversarial environments under full information or
bandit feedback, and propose a policy optimization algorithm for each setting
that makes use of novel correction terms and/or variants of dilated bonuses
(Luo et al., 2021). For most settings, our algorithm is shown to achieve a
near-optimal regret bound.
  One key technical contribution of this work is a new approximation scheme to
tackle SSP problems that we call \textit{stacked discounted approximation} and
use in all our proposed algorithms. Unlike the finite-horizon approximation
that is heavily used in recent SSP algorithms, our new approximation enables us
to learn a near-stationary policy with only logarithmic changes during an
episode and could lead to an exponential improvement in space complexity.


title: Membership Inference Attacks and Defenses in Neural Network Pruning

abstract:
  Neural network pruning has been an essential technique to reduce the
computation and memory requirements for using deep neural networks for
resource-constrained devices. Most existing research focuses primarily on
balancing the sparsity and accuracy of a pruned neural network by strategically
removing insignificant parameters and retraining the pruned model. Such efforts
on reusing training samples pose serious privacy risks due to increased
memorization, which, however, has not been investigated yet.
  In this paper, we conduct the first analysis of privacy risks in neural
network pruning. Specifically, we investigate the impacts of neural network
pruning on training data privacy, i.e., membership inference attacks. We first
explore the impact of neural network pruning on prediction divergence, where
the pruning process disproportionately affects the pruned model's behavior for
members and non-members. Meanwhile, the influence of divergence even varies
among different classes in a fine-grained manner. Enlighten by such divergence,
we proposed a self-attention membership inference attack against the pruned
neural networks. Extensive experiments are conducted to rigorously evaluate the
privacy impacts of different pruning approaches, sparsity levels, and adversary
knowledge. The proposed attack shows the higher attack performance on the
pruned models when compared with eight existing membership inference attacks.
In addition, we propose a new defense mechanism to protect the pruning process
by mitigating the prediction divergence based on KL-divergence distance, whose
effectiveness has been experimentally demonstrated to effectively mitigate the
privacy risks while maintaining the sparsity and accuracy of the pruned models.


title:
Inverse nodal problems for Sturm-Liouville equation with nonlocal
  boundary conditions

abstract:
  In this paper, a Sturm--Liouville problem with some nonlocal boundary
conditions of the Bitsadze-Samarskii type is studied. We show that the
coefficients of the problem can be uniquely determined by a dense set of nodal
points. Moreover, we give an algorithm for the reconstruction of the potential
function and some other coefficients in the boundary conditions.


title: From graph to Riesz continuity

abstract:
  We show that every graph continuous family of unbounded operators in a
Hilbert space becomes Riesz continuous after multiplication by an appropriate
family of unitaries. This result leads to two corollaries for operators with
compact resolvents: (1) the identity map between the spaces of such operators
equipped with the Riesz and the graph topology is a homotopy equivalence; (2)
every graph continuous family of such operators acting between fibers of
Hilbert bundles becomes Riesz continuous in an appropriate trivializations of
the bundles.
  For self-adjoint operators, multiplication by unitaries should be replaced by
conjugation. In general, a graph continuous family of self-adjoint operators
with compact resolvents cannot be made Riesz continuous by an appropriate
conjugation. We obtain a partial analogue of the trivialization result above
for self-adjoint operators and describe obstructions to existence of such a
trivialization arising in the general case. These results are closely related
to the recent work of N. Ivanov arXiv:2111.15081.


title: Robust Semantic Communications Against Semantic Noise

abstract:
  Although the semantic communications have exhibited satisfactory performance
in a large number of tasks, the impact of semantic noise and the robustness of
the systems have not been well investigated. Semantic noise is a particular
kind of noise in semantic communication systems, which refers to the misleading
between the intended semantic symbols and received ones. In this paper, we
first propose a framework for the robust end-to-end semantic communication
systems to combat the semantic noise. Particularly, we analyze the causes of
semantic noise and propose a practical method to generate it. To remove the
effect of semantic noise, adversarial training is proposed to incorporate the
samples with semantic noise in the training dataset. Then, the masked
autoencoder is designed as the architecture of a robust semantic communication
system, where a portion of the input is masked. To further improve the
robustness of semantic communication systems, we design a discrete codebook
shared by the transmitter and the receiver for encoded feature representation.
Thus, the transmitter simply needs to transmit the indices of these features in
the codebook. Simulation results show that our proposed method significantly
improves the robustness of semantic communication systems against semantic
noise with significant reduction on the transmission overhead.


title:
Shared purity and concurrence of a mixture of ground and low-lying
  excited states as indicators of quantum phase transitions

abstract:
  We investigate the efficacy of shared purity, a measure of quantum
correlation that is independent of separability-entanglement paradigm, as a
quantum phase transition indicator in comparison with concurrence, a bipartite
entanglement measure. The order parameters are investigated for a mixture of
the ground state and low-lying excited states of the systems considered. In the
case of the one-dimensional J1-J2 Heisenberg quantum spin model and the
one-dimensional transverse-field quantum Ising model, shared purity turns out
to be as effective as concurrence in indicating quantum phase transitions. In
the two-dimensional J1-J2 Heisenberg quantum spin model, shared purity
indicates the two quantum phase transitions present in the model, while
concurrence detects only one of them. Moreover, we find diverging finite-size
scaling exponents for the order parameters near the transitions in odd- and
even-sized systems governed by the one-dimensional J1-J2 model, as had
previously been reported for quantum spins on odd- and even-legged ladders. It
is plausible that the divergence is related to a Mobius strip-like boundary
condition required for odd-sized systems, while for even-sized systems, the
usual periodic boundary condition is sufficient.


title:
The $q$-Lidstone series involving $q$-Bernoulli and $q$-Euler
  polynomials generated by the third Jackson $q$-Bessel function

abstract:
  n this paper, we present $q$-Bernoulli and $q$-Euler polynomials generated by
the third Jackson $q$-Bessel function to construct new types of $q$-Lidstone
expansion theorem. We prove that the entire function may be expanded in terms
of $q$-Lidstone polynomials which are $q$-Bernoulli polynomials and the
coefficients are the even powers of the $q$-derivative $\frac{\delta_q
f(z)}{\delta_q z}$ at $0$ and $1$. The other forms expand the function in
$q$-Lidstone polynomials based on $q$-Euler polynomials and the coefficients
contain the even and odd powers of the $q$-derivative $\frac{\delta_q
f(z)}{\delta_q z}$.


title:
Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors
  to Sequences

abstract:
  Modern graph neural networks (GNNs) use a message passing scheme and have
achieved great success in many fields. However, this recursive design
inherently leads to excessive computation and memory requirements, making it
not applicable to massive real-world graphs. In this work, we propose the
Neighbor2Seq to transform the hierarchical neighborhood of each node into a
sequence. This novel transformation enables the subsequent mini-batch training
for general deep learning operations, such as convolution and attention, that
are designed for grid-like data and are shown to be powerful in various
domains. Therefore, our Neighbor2Seq naturally endows GNNs with the efficiency
and advantages of deep learning operations on grid-like data by precomputing
the Neighbor2Seq transformations. We evaluate our method on a massive graph,
with more than 111 million nodes and 1.6 billion edges, as well as several
medium-scale graphs. Results show that our proposed method is scalable to
massive graphs and achieves superior performance across massive and
medium-scale graphs. Our code is available at
https://github.com/divelab/Neighbor2Seq.


title:
A Review of Landcover Classification with Very-High Resolution Remotely
  Sensed Optical Images-Analysis Unit,Model Scalability and Transferability

abstract:
  As an important application in remote sensing, landcover classification
remains one of the most challenging tasks in very-high-resolution (VHR) image
analysis. As the rapidly increasing number of Deep Learning (DL) based
landcover methods and training strategies are claimed to be the
state-of-the-art, the already fragmented technical landscape of landcover
mapping methods has been further complicated. Although there exists a plethora
of literature review work attempting to guide researchers in making an informed
choice of landcover mapping methods, the articles either focus on the review of
applications in a specific area or revolve around general deep learning models,
which lack a systematic view of the ever advancing landcover mapping methods.
In addition, issues related to training samples and model transferability have
become more critical than ever in an era dominated by data-driven approaches,
but these issues were addressed to a lesser extent in previous review articles
regarding remote sensing classification. Therefore, in this paper, we present a
systematic overview of existing methods by starting from learning methods and
varying basic analysis units for landcover mapping tasks, to challenges and
solutions on three aspects of scalability and transferability with a remote
sensing classification focus including (1) sparsity and imbalance of data; (2)
domain gaps across different geographical regions; and (3) multi-source and
multi-view fusion. We discuss in detail each of these categorical methods and
draw concluding remarks in these developments and recommend potential
directions for the continued endeavor.


title: Topological Analysis of Vector-Field Guided Path Following on Manifolds

abstract:
  A path-following control algorithm enables a system's trajectories under its
guidance to converge to and evolve along a given geometric desired path. There
exist various such algorithms, but many of them can only guarantee local
convergence to the desired path in its neighborhood. In contrast, the control
algorithms using a well-designed guiding vector field can ensure almost global
convergence of trajectories to the desired path; here, "almost" means that in
some cases, a measure-zero set of trajectories converge to the singular set
where the vector field becomes zero (with all other trajectories converging to
the desired path). In this paper, we first generalize the guiding vector field
from the Euclidean space to a general smooth Riemannian manifold. This
generalization can deal with path-following in some abstract configuration
space (such as robot arm joint space). Then we show several theoretical results
from a topological viewpoint. Specifically, we are motivated by the observation
that singular points of the guiding vector field exist in many examples where
the desired path is homeomorphic to the unit circle, but it is unknown whether
the existence of singular points always holds in general (i.e., is inherent in
the topology of the desired path). In the $n$-dimensional Euclidean space, we
provide an affirmative answer, and conclude that it is not possible to
guarantee global convergence to desired paths that are homeomorphic to the unit
circle. Furthermore, we show that there always exist \emph{non-path-converging
trajectories} (i.e., trajectories that do not converge to the desired path)
starting from the boundary of a ball containing the desired path in an
$n$-dimensional Euclidean space where $n \ge 3$. Examples are provided to
illustrate the theoretical results.


title: Stochastic polynomial chaos expansions to emulate stochastic simulators

abstract:
  In the context of uncertainty quantification, computational models are
required to be repeatedly evaluated. This task is intractable for costly
numerical models. Such a problem turns out to be even more severe for
stochastic simulators, the output of which is a random variable for a given set
of input parameters. To alleviate the computational burden, we develop a new
stochastic surrogate model called stochastic polynomial chaos expansions. To
this aim, we introduce a latent variable and an additional noise variable, on
top of the well-defined input variables, to represent the intrinsic
stochasticity. As a result, for a given set of input parameters, the model
output is given by a function of the latent variable with an additive noise,
thus a random variable. We develop an adaptive algorithm to construct such a
surrogate, which does not require repeated runs of the simulator for the same
input parameters. The performance of the proposed method is compared with the
generalized lambda model and a state-of-the-art kernel estimator on two case
studies in mathematical finance and epidemiology and on an analytical example
whose response distribution is bimodal. The results show that the proposed
method is able to accurately represent general response distributions, i.e.,
not only normal or unimodal ones. In terms of accuracy, it generally
outperforms both the generalized lambda model and the kernel density estimator.


title:
Monogamy and polygamy relations of quantum correlations for multipartite
  systems

abstract:
  We study the monogamy and polygamy inequalities of quantum correlations in
arbitrary dimensional multipartite quantum systems. We first derive the
monogamy inequality of the $\alpha$th ($0\leq\alpha\leq\frac{r}{2}, r\geq2$)
power of concurrence for any $2\otimes2\otimes2^{n-2}$ tripartite states and
generalize it to the $n$-qubit quantum states. In addition to concurrence, we
show that the monogamy relations are satisfied by other quantum correlation
measures such as entanglement of formation. Moreover, the polygamy inequality
of the $\beta$th ($\beta\leq0$) power of concurrence and the $\beta$th
($\beta\geq s, 0\leq s\leq1$) power of the negativity are presented for
$2\otimes2\otimes2^{n-2}$. We then obtain the polygamy inequalities of quantum
correlations for multipartite states. Finally, our results are shown to be
tighter than previous studies using detailed examples.


title:
Variance reduced stochastic optimization over directed graphs with row
  and column stochastic weights

abstract:
  This paper proposes AB-SAGA, a first-order distributed stochastic
optimization method to minimize a finite-sum of smooth and strongly convex
functions distributed over an arbitrary directed graph. AB-SAGA removes the
uncertainty caused by the stochastic gradients using a node-level variance
reduction and subsequently employs network-level gradient tracking to address
the data dissimilarity across the nodes. Unlike existing methods that use the
nonlinear push-sum correction to cancel the imbalance caused by the directed
communication, the consensus updates in AB-SAGA are linear and uses both row
and column stochastic weights. We show that for a constant step-size, AB-SAGA
converges linearly to the global optimal. We quantify the directed nature of
the underlying graph using an explicit directivity constant and characterize
the regimes in which AB-SAGA achieves a linear speed-up over its centralized
counterpart. Numerical experiments illustrate the convergence of AB-SAGA for
strongly convex and nonconvex problems.


title: FrePGAN: Robust Deepfake Detection Using Frequency-level Perturbations

abstract:
  Various deepfake detectors have been proposed, but challenges still exist to
detect images of unknown categories or GAN models outside of the training
settings. Such issues arise from the overfitting issue, which we discover from
our own analysis and the previous studies to originate from the frequency-level
artifacts in generated images. We find that ignoring the frequency-level
artifacts can improve the detector's generalization across various GAN models,
but it can reduce the model's performance for the trained GAN models. Thus, we
design a framework to generalize the deepfake detector for both the known and
unseen GAN models. Our framework generates the frequency-level perturbation
maps to make the generated images indistinguishable from the real images. By
updating the deepfake detector along with the training of the perturbation
generator, our model is trained to detect the frequency-level artifacts at the
initial iterations and consider the image-level irregularities at the last
iterations. For experiments, we design new test scenarios varying from the
training settings in GAN models, color manipulations, and object categories.
Numerous experiments validate the state-of-the-art performance of our deepfake
detector.