title:
Towards AutoQML: A Cloud-Based Automated Circuit Architecture Search
  Framework

abstract:
  The learning process of classical machine learning algorithms is tuned by
hyperparameters that need to be customized to best learn and generalize from an
input dataset. In recent years, Quantum Machine Learning (QML) has been gaining
traction as a possible application of quantum computing which may provide
quantum advantage in the future. However, quantum versions of classical machine
learning algorithms introduce a plethora of additional parameters and circuit
variations that have their own intricacies in being tuned.
  In this work, we take the first steps towards Automated Quantum Machine
Learning (AutoQML). We propose a concrete description of the problem, and then
develop a classical-quantum hybrid cloud architecture that allows for
parallelized hyperparameter exploration and model training.
  As an application use-case, we train a quantum Generative Adversarial neural
Network (qGAN) to generate energy prices that follow a known historic data
distribution. Such a QML model can be used for various applications in the
energy economics sector.


title: Diagnosing Batch Normalization in Class Incremental Learning

abstract:
  Extensive researches have applied deep neural networks (DNNs) in class
incremental learning (Class-IL). As building blocks of DNNs, batch
normalization (BN) standardizes intermediate feature maps and has been widely
validated to improve training stability and convergence. However, we claim that
the direct use of standard BN in Class-IL models is harmful to both the
representation learning and the classifier training, thus exacerbating
catastrophic forgetting. In this paper we investigate the influence of BN on
Class-IL models by illustrating such BN dilemma. We further propose BN Tricks
to address the issue by training a better feature extractor while eliminating
classification bias. Without inviting extra hyperparameters, we apply BN Tricks
to three baseline rehearsal-based methods, ER, DER++ and iCaRL. Through
comprehensive experiments conducted on benchmark datasets of Seq-CIFAR-10,
Seq-CIFAR-100 and Seq-Tiny-ImageNet, we show that BN Tricks can bring
significant performance gains to all adopted baselines, revealing its potential
generality along this line of research.


title:
Frequency Response from Aggregated V2G Chargers With Uncertain EV
  Connections

abstract:
  Fast frequency response (FR) is highly effective at securing frequency
dynamics after a generator outage in low inertia systems. Electric vehicles
(EVs) equipped with vehicle to grid (V2G) chargers could offer an abundant
source of FR in future. However, the uncertainty associated with V2G
aggregation, driven by the uncertain number of connected EVs at the time of an
outage, has not been fully understood and prevents its participation in the
existing service provision framework. To tackle this limitation, this paper,
for the first time, incorporates such uncertainty into system frequency
dynamics, from which probabilistic nadir and steady state frequency
requirements are enforced via a derived momeent-based distributionally-robust
chance constraint. Field data from over 25,000 chargers is analysed to provide
realistic parameters and connection forecasts to examine the value of FR from
V2G chargers in annual operation of the the GB 2030 system. The case study
demonstrates that uncertainty of EV connections can be effectively managed
through the proposed scheduling framework, which results in annual savings of
{\pounds}6,300 or 37.4 tCO2 per charger. The sensitivity of this value to
renewable capacity and FR delays is explored, with V2G capacity shown to be a
third as valuable as the same grid battery capacity.


title:
PyFIT3D and pyPipe3D -- The new version of the Integral Field
  Spectroscopy data analysis pipeline

abstract:
  We present a new version of the FIT3D and Pipe3D codes, two packages to
derive properties of the stellar populations and the ionized emission lines
from optical spectroscopy and integral field spectroscopy data respectively.
The new codes have been fully transcribed to Python from the original Perl and
C versions, modifying the algorithms when needed to make use of the unique
capabilities of this language with the main goals of (1) respecting as much as
possible the original philosophy of the algorithms, (2) maintaining a full
compatibility with the original version in terms of the format of the required
input and produced output files, and (3) improving the efficiency and accuracy
of the algorithms, and solving known (and newly discovered) bugs. The complete
package is freely distributed, with an available repository online. pyFIT3D and
pyPipe3D are fully tested with data of the most recent IFS data surveys and
compilations (e.g. CALIFA, MaNGA, SAMI and AMUSING++), and confronted with
simulations. We describe here the code, its new implementation, its accuracy in
recovering the parameters based on simulations, and a showcase of its
implementation on a particular dataset.


title:
APPLADE: Adjustable Plug-and-play Audio Declipper Combining DNN with
  Sparse Optimization

abstract:
  In this paper, we propose an audio declipping method that takes advantages of
both sparse optimization and deep learning. Since sparsity-based audio
declipping methods have been developed upon constrained optimization, they are
adjustable and well-studied in theory. However, they always uniformly promote
sparsity and ignore the individual properties of a signal. Deep neural network
(DNN)-based methods can learn the properties of target signals and use them for
audio declipping. Still, they cannot perform well if the training data have
mismatches and/or constraints in the time domain are not imposed. In the
proposed method, we use a DNN in an optimization algorithm. It is inspired by
an idea called plug-and-play (PnP) and enables us to promote sparsity based on
the learned information of data, considering constraints in the time domain.
Our experiments confirmed that the proposed method is stable and robust to
mismatches between training and test data.


title: Code Search based on Context-aware Code Translation

abstract:
  Code search is a widely used technique by developers during software
development. It provides semantically similar implementations from a large code
corpus to developers based on their queries. Existing techniques leverage deep
learning models to construct embedding representations for code snippets and
queries, respectively. Features such as abstract syntactic trees, control flow
graphs, etc., are commonly employed for representing the semantics of code
snippets. However, the same structure of these features does not necessarily
denote the same semantics of code snippets, and vice versa. In addition, these
techniques utilize multiple different word mapping functions that map query
words/code tokens to embedding representations. This causes diverged embeddings
of the same word/token in queries and code snippets. We propose a novel
context-aware code translation technique that translates code snippets into
natural language descriptions (called translations). The code translation is
conducted on machine instructions, where the context information is collected
by simulating the execution of instructions. We further design a shared word
mapping function using one single vocabulary for generating embeddings for both
translations and queries. We evaluate the effectiveness of our technique,
called TranCS, on the CodeSearchNet corpus with 1,000 queries. Experimental
results show that TranCS significantly outperforms state-of-the-art techniques
by 49.31% to 66.50% in terms of MRR (mean reciprocal rank).


title: Enriques involutions and Brauer classes

abstract:
  We prove that every element of order 2 in the Brauer group of a complex
Kummer surface X descends to an Enriques quotient of X. In 'generic' cases this
gives a bijection between the set Enr(X) of Enriques quotients of X up to
isomorphism and the set of Brauer classes of X of order 2. For some K3 surfaces
of Picard rank 20 we prove that the fibres of the map from Enr(X) to Br(X)[2]
above the non-zero points have the same order.


title:
Topological semimetal phases in a family of monolayer X3YZ6 (X=Nb,Ta,
  Y=Si,Ge,Sn, Z=S,Se,Te) with abundant nodal lines and nodes

abstract:
  The electronic and topological properties of single-layer X3YZ6 (X=Nb,Ta,
Y=Si,Ge,Sn, Z=S,Se,Te) materials have been studied with the aid of first
principles calculations. This kind of materials belong to topological
semimetals (TMs) with abundant nodal lines and nodes. Considering their similar
properties, we focus on the analysis of Ta3SnTe6 and Ta3SiSe6. The present of
spin-orbit coupling (SOC) leads to the transition from type-I nodal lines to
Dirac points as well as the disappear of type-II Dirac points. The
three-dimensional (3D) band diagrams reproduce vividly the characteristics of
nodes and nodal lines. The appearance of the flat bands in (110) edge states
further confirm their nontrivial topological properties. We also explore the
relationship among different nodal lines (nodes), crystal symmetry and SOC. The
type-I nodal lines are protected by Mz and My symmetry in the absent of SOC.
Symmetry breaking leads to band splitting even in the presence of SOC. The
single-layer X3YZ6 can be used as candidates for two-dimensional (2D) TMs and
provide a platform for further study of interesting physical phenomena.


title:
Schauder basis in Lipschitz free spaces over nets of
  $\mathcal{L}_\infty$-spaces

abstract:
  In the present note we give a construction (based on a retractional argument)
of a Schauder basis for the Lipschitz free space $\mathcal{F}(N)$, over a net
$N$ in any separable infinite dimensional $\mathcal{L}_\infty$-space $X$. In
particular, this yields the first example of an infinite dimensional Banach
space $X$ not containing $c_0$ with such a property.


title:
Language Inclusion for Boundedly-Ambiguous Vector Addition Systems is
  Decidable

abstract:
  We consider the problems of language inclusion and language equivalence for
Vector Addition Systems with States (VASSes) with the acceptance condition
defined by the set of accepting states (and more generally by some
upward-closed conditions). In general the problem of language equivalence is
undecidable even for one-dimensional VASSes, thus to get decidability we
investigate restricted subclasses. On one hand we show that the problem of
language inclusion of a VASS in k-ambiguous VASS (for any natural k) is
decidable and even in Ackermann. On the other hand we prove that the language
equivalence problem is Ackermann-hard already for deterministic VASSes. These
two results imply Ackermann-completeness for language inclusion and equivalence
in several possible restrictions. Some of our techniques can be also applied in
much broader generality in infinite-state systems, namely for some subclass of
well-structured transition systems.


title: A BiLSTM-CNN based Multitask Learning Approach for Fiber Fault Diagnosis

abstract:
  A novel multitask learning approach based on stacked bidirectional long
short-term memory (BiLSTM) networks and convolutional neural networks (CNN) for
detecting, locating, characterizing, and identifying fiber faults is proposed.
It outperforms conventionally employed techniques.


title: The Pareto cover problem

abstract:
  We introduce the problem of finding a set $B$ of $k$ points in $[0,1]^n$ such
that the expected cost of the cheapest point in $B$ that dominates a random
point from $[0,1]^n$ is minimized. We study the case where the coordinates of
the random points are independently distributed and the cost function is
linear. This problem arises naturally in various application areas where
customers' requests are satisfied based on predefined products, each
corresponding to a subset of features. We show that the problem is NP-hard
already for $k=2$ when each coordinate is drawn from $\{0,1\}$, and obtain an
FPTAS for general fixed $k$ under mild assumptions on the distributions.


title:
No One Left Behind: Inclusive Federated Learning over Heterogeneous
  Devices

abstract:
  Federated learning (FL) is an important paradigm for training global models
from decentralized data in a privacy-preserving way. Existing FL methods
usually assume the global model can be trained on any participating client.
However, in real applications, the devices of clients are usually
heterogeneous, and have different computing power. Although big models like
BERT have achieved huge success in AI, it is difficult to apply them to
heterogeneous FL with weak clients. The straightforward solutions like removing
the weak clients or using a small model to fit all clients will lead to some
problems, such as under-representation of dropped clients and inferior accuracy
due to data loss or limited model representation ability. In this work, we
propose InclusiveFL, a client-inclusive federated learning method to handle
this problem. The core idea of InclusiveFL is to assign models of different
sizes to clients with different computing capabilities, bigger models for
powerful clients and smaller ones for weak clients. We also propose an
effective method to share the knowledge among multiple local models with
different sizes. In this way, all the clients can participate in the model
learning in FL, and the final model can be big and powerful enough. Besides, we
propose a momentum knowledge distillation method to better transfer knowledge
in big models on powerful clients to the small models on weak clients.
Extensive experiments on many real-world benchmark datasets demonstrate the
effectiveness of the proposed method in learning accurate models from clients
with heterogeneous devices under the FL framework.


title: A Review of Topological Data Analysis for Cybersecurity

abstract:
  In cybersecurity it is often the case that malicious or anomalous activity
can only be detected by combining many weak indicators of compromise, any one
of which may not raise suspicion when taken alone. The path that such
indicators take can also be critical. This makes the problem of analysing
cybersecurity data particularly well suited to Topological Data Analysis (TDA),
a field that studies the high level structure of data using techniques from
algebraic topology, both for exploratory analysis and as part of a machine
learning workflow. By introducing TDA and reviewing the work done on its
application to cybersecurity, we hope to highlight to researchers a promising
new area with strong potential to improve cybersecurity data science.


title: Decoherence for Markov chains

abstract:
  The subspace generated by the eigenvectors pertaining to the peripheral
spectrum of any stochastic matrix is naturally equipped with a structure of a
(finite dimensional abelian) $C^*$-algebra, and the restriction of such a
stochastic matrix to this subspace, indeed a $C^*$-algebra under this canonical
new product, generates a conservative $C^*$-dynamical system.


title: Negative critical currents in single-channel Josephson junctions

abstract:
  We argue that negative critical currents arise generically in Josephson
junctions formed by single channel conductors. Specifically, we theoretically
study the Josephson coupling between two superconducting leads connected by a
one-dimensional conductor in the Coulomb blockade regime. We show that in the
clean regime the sign of the critical current alternates with the number of
electrons in the normal region. For odd occupancy the critical current is
negative even when the number of electrons is large.


title: 1-Jettiness with Jet axis at $O(\alpha_s)$ in Deep Inelastic Scattering

abstract:
  We present $O(\alpha_s)$ analytic predictions for event shape 1-jettiness
$\tau_1$ distribution aiming measurements in deep inelastic scattering process
at future Electron Ion Colliders. The result depends on conventional variables
$x$ and $Q$ as well as on $\tau_1$ and is relatively compact and easy to
implement for numerical calculation. Three different choices of axis, with
respect to which $\tau_1$ is measured are considered in the Breit frame. The
first is the one optimally adjusted to minimize $\tau_1$ and the second and
third are taken from anti-$k_T$ and Centauro jet algorithms defined with a jet
radius parameter $R$, respectively. We find that the first and second give the
same result at this order and are independent of $R$, while the third depends
on the radius. This fixed-order result provides a nonsingular contribution to
be combined with a singular log-resummed contribution to give the full spectrum
in $\tau_1$ space and also shows how fixed-order and resummation regions change
as a function of $x$ and $Q$.


title:
Explainability of Predictive Process Monitoring Results: Can You See My
  Data Issues?

abstract:
  Predictive business process monitoring (PPM) has been around for several
years as a use case of process mining. PPM enables foreseeing the future of a
business process through predicting relevant information about how a running
process instance might end, related performance indicators, and other
predictable aspects. A big share of PPM approaches adopts a Machine Learning
(ML) technique to address a prediction task, especially non-process-aware PPM
approaches. Consequently, PPM inherits the challenges faced by ML approaches.
One of these challenges concerns the need to gain user trust in the predictions
generated. The field of explainable artificial intelligence (XAI) addresses
this issue. However, the choices made, and the techniques employed in a PPM
task, in addition to ML model characteristics, influence resulting
explanations. A comparison of the influence of different settings on the
generated explanations is missing. To address this gap, we investigate the
effect of different PPM settings on resulting data fed into an ML model and
consequently to a XAI method. We study how differences in resulting
explanations may indicate several issues in underlying data. We construct a
framework for our experiments including different settings at each stage of PPM
with XAI integrated as a fundamental part. Our experiments reveal several
inconsistencies, as well as agreements, between data characteristics (and hence
expectations about these data), important data used by the ML model as a result
of querying it, and explanations of predictions of the investigated ML model.


title: Information extraction in photon-counting experiments

abstract:
  We demonstrate a comparison of different multiplexing architectures based on
quantum detector tomography. Using the purity of their measurement outcomes, we
gain insight about the photon-number resolving power of the devices. Further,
we calculate the information each measurement outcome can extract out of a
Hilbert space with given dimension. Our work confirms that more multiplexing
outcomes enable higher photon-number resolving power; however, the splitting
between those outcomes must be optimized as well.


title:
Controllable multiple beam splitting in Hermitian and non-Hermitian
  symmetric coupled waveguide systems

abstract:
  We investigate high-fidelity multiple beam splitting in Hermitian and
non-Hermitian symmetric coupled waveguides with one input and 2N output
waveguide channels. In Hermitian systems, we realize adiabatically light
splitting in resonant case based on the stimulated Raman adiabatic passage
(STIRAP) and arbitrary proportion from the middle waveguide to outer waveguides
in propagation coefficients mismatch case using shortcuts to adiabaticity (STA)
technique. In non-Hermitian systems with even waveguides being dissipative, the
compact and robust beam splitting can be achieved by eliminating the
non-adiabatic coupling via the non-Hermitian STA method. We further verify the
feasibility of our theoretical predictions by means of the beam propagation
method (BPM). The suggested multiple beam splitters open new opportunities for
the realization of on-chip high-bandwidth photonics with high fidelity in short
distances.


title:
The Distribution of the Number of Isolated Nodes in the 1-Dimensional
  Soft Random Geometric Graph

abstract:
  We study the number of isolated nodes in a soft random geometric graph whose
vertices constitute a Poisson process on the torus of length L (the line
segment [0,L] with periodic boundary conditions), and where an edge is present
between two nodes with a probability which depends on the distance between
them. Edges between distinct pairs of nodes are mutually independent. In a
suitable scaling regime, we show that the number of isolated nodes converges in
total variation to a Poisson random variable. The result implies an upper bound
on the probability that the random graph is connected.


title: Learning to Generalize across Domains on Single Test Samples

abstract:
  We strive to learn a model from a set of source domains that generalizes well
to unseen target domains. The main challenge in such a domain generalization
scenario is the unavailability of any target domain data during training,
resulting in the learned model not being explicitly adapted to the unseen
target domains. We propose learning to generalize across domains on single test
samples. We leverage a meta-learning paradigm to learn our model to acquire the
ability of adaptation with single samples at training time so as to further
adapt itself to each single test sample at test time. We formulate the
adaptation to the single test sample as a variational Bayesian inference
problem, which incorporates the test sample as a conditional into the
generation of model parameters. The adaptation to each test sample requires
only one feed-forward computation at test time without any fine-tuning or
self-supervised training on additional data from the unseen domains. Extensive
ablation studies demonstrate that our model learns the ability to adapt models
to each single sample by mimicking domain shifts during training. Further, our
model achieves at least comparable -- and often better -- performance than
state-of-the-art methods on multiple benchmarks for domain generalization.


title: Simulation-based Verification of SystemC-based VPs at the ESL

abstract:
  SystemC-based Virtual Prototypes (VPs) at the Electronic System Level (ESL)
are increasingly adopted by the semiconductor industry. The main reason is that
VPs are much earlier available, and their simulation is orders of magnitude
faster in comparison to the hardware models at lower levels of abstraction
(e.g. RTL). This leads designers to use VPs as reference models for early
design verification. Hence, the correctness of VPs is of utmost importance as
undetected errors may propagate to less abstract levels in the design process,
increasing the fixing cost and effort. In this paper, we introduce a
comprehensive simulation-based verification approach to automatically validate
the simulation behavior of a given SystemC-based VP against both the TLM-2.0
rules and its specifications, i.e. functional and timing behavior of
communications in the VP.


title:
Finite-amplitude elastic waves in viscoelastic channel flow from large
  to zero Reynolds number

abstract:
  Using branch continuation in the FENE-P model, we show that finite-amplitude
travelling waves borne out of the recently-discovered linear instability of
viscoelastic channel flow (Khalid et al. {\em J. Fluid Mech.} {\bf 915}, A43,
2021) are substantially subcritical reaching much lower Weissenberg ($Wi$)
numbers than on the neutral curve at a given Reynolds ($Re$) number over $Re
\in [0,3000]$. The travelling waves on the lower branch are surprisingly weak
indicating that viscolastic channel flow is susceptible to (nonlinear)
instability triggered by small finite amplitude disturbances for $Wi$ and $Re$
well below the neutral curve. The critical $Wi$ for these waves to appear in a
saddle node bifurcation decreases monotonically from, for example, $\approx 37$
at $Re=3000$ down to $\approx 7.5$ at $Re=0$ at the solvent-to-total-viscosity
ratio $\beta=0.9$. In this latter creeping flow limit, we also show that these
waves exist at $Wi \lesssim 50$ for higher polymer concentrations - $\beta \in
[0.5,0.97)$ -- where there is no known linear instability. Our results
therefore indicate that these travelling waves -- found in simulations and
named `arrowheads' by Dubief et al. {\em arXiv}.2006.06770 (2020) - exist much
more generally in $(Wi,Re, \beta)$ parameter space than their spawning neutral
curve and hence can either directly, or indirectly through their instabilities,
influence the dynamics seen far away from where the flow is linearly unstable.
Possible connections to elastic and elasto-inertial turbulence are discussed.


title:
Decorrelate Irrelevant, Purify Relevant: Overcome Textual Spurious
  Correlations from a Feature Perspective

abstract:
  Natural language understanding (NLU) models tend to rely on spurious
correlations (\emph{i.e.}, dataset bias) to achieve high performance on
in-distribution datasets but poor performance on out-of-distribution ones. Most
of the existing debiasing methods often identify and weaken these samples with
biased features (\emph{i.e.}, superficial surface features that cause such
spurious correlations). However, down-weighting these samples obstructs the
model in learning from the non-biased parts of these samples. To tackle this
challenge, in this paper, we propose to eliminate spurious correlations in a
fine-grained manner from a feature space perspective. Specifically, we
introduce Random Fourier Features and weighted re-sampling to decorrelate the
dependencies between features to mitigate spurious correlations. After
obtaining decorrelated features, we further design a mutual-information-based
method to purify them, which forces the model to learn features that are more
relevant to tasks. Extensive experiments on two well-studied NLU tasks
including Natural Language Inference and Fact Verification demonstrate that our
method is superior to other comparative approaches.


title:
Pressure tuning of localization and superconductivity in LaOPbBiS$_{3}$
  and La$_{2}$O$_{2}$Bi$_{3}$AgS$_{6}$

abstract:
  We report the effect of pressure on the electrical transport properties of
the four-layer-type bismuth chalcogenide semiconductors LaOPbBiS$_{3}$ and
La$_{2}$O$_{2}$Bi$_{3}$AgS$_{6}$ and present the discovery of a
pressure-induced superconductivity. In both compounds, the semiconductorlike
behavior concomitant with an anomaly in the more insulating state is gradually
suppressed with increasing pressure while the superconductivity develops. The
obtained phase diagrams, in sharp contrast with the interplay between the
conventional charge density waves and superconductivity, suggest that the
enhancement of the superconductivity is due to the disorder near the
localization threshold. These results indicate that pressure tuning of the
local disorder in four-layer-type bismuth chalcogenides provides an attractive
opportunity to study the interplay between disorder and superconductivity.


title:
Computing discrete invariants of varieties in positive characteristic.
  I. Ekedahl-Oort types of curves

abstract:
  We develop a method to compute the Ekedahl-Oort type of a curve C over a
field k of characteristic p (which is the isomorphism type of the p-kernel
group scheme J[p], where J is the Jacobian of C). Part of our method is
general, in that we introduce the new notion of a Hasse-Witt triple, which
re-encodes in a useful way the information contained in the Dieudonne module of
J[p]. For complete intersection curves we then give a simple method to compute
this Hasse-Witt triple. An implementation of this method is available in Magma.


title: An RKHS approach for pivotal inference in functional linear regression

abstract:
  We develop methodology for testing hypotheses regarding the slope function in
functional linear regression for time series via a reproducing kernel Hilbert
space approach. In contrast to most of the literature, which considers tests
for the exact nullity of the slope function, we are interested in the null
hypothesis that the slope function vanishes only approximately, where
deviations are measured with respect to the $L^2$-norm. An asymptotically
pivotal test is proposed, which does not require the estimation of nuisance
parameters and long-run covariances. The key technical tools to prove the
validity of our approach include a uniform Bahadur representation and a weak
invariance principle for a sequential process of estimates of the slope
function. Both scalar-on-function and function-on-function linear regression
are considered and finite-sample methods for implementing our methodology are
provided. We also illustrate the potential of our methods by means of a small
simulation study and a data example.


title:
On the impact of the geospace environment on so-lar-lithosphere coupling
  and earthquake occurrence

abstract:
  We have found that about two months after creating a new radiation belt in
the inner magnetosphere due to a geomagnetic storm, an increasing seismic
activity may occur near the magnetic field lines' footprint of a newly created
radiation belt. The Combined Release and Radiation Effects Satellite (CRRES)
detected a new radiation belt after a geomagnetic storm on March 24, 1991.
Shortly after that, on May 30, 1991, a strong M7.0 earthquake occurred in
Alaska in the footprint of geomagnetic line L~2.69. Additionally, on October
28, 2012, a strong M7.8 earthquake occurred in Canada near the footprint of
L~3.3, which was close to the magnetic lines of a new radiation belt detected
by a satellite "Van Allen Probes" after a geomagnetic storm on September 3,
2012. Seismic activity also increased near the magnetic field lines' footprint
of a newly created radiation belt around L~1.5-1.8 due to a geomagnetic storm
on June 21, 2015. We demonstrate the possible existence of two way of
solar-lithosphere coupling processes : (i) the disturbances in the lithosphere,
accompanying the earthquake preparation process, can modify the electric field
in the global electric circuit (GEC), which results in appearing of
disturbances in the ionosphere; and the vice-versa mechanism (ii) the solar
wind-generated disturbances in the magnetosphere and ionosphere, can modify the
electric field in the GEC, that will result in appearing of disturbances in the
lithosphere.


title:
Image translation of Ultrasound to Pseudo Anatomical Display Using
  Artificial Intelligence

abstract:
  Ultrasound is the second most used modality in medical imaging. It is cost
effective, hazardless, portable and implemented routinely in numerous clinical
procedures. Nonetheless, image quality is characterized by granulated
appearance, poor SNR and speckle noise. Specific for malignant tumors, the
margins are blurred and indistinct. Thus, there is a great need for improving
ultrasound image quality. We hypothesize that this can be achieved by
translation into a more realistic anatomic display, using neural networks. In
order to achieve this goal, the preferable approach would be to use a set of
paired images. However, this is practically impossible in our case. Therefore,
CycleGAN was used, to learn each domain properties separately and enforce cross
domain cycle consistency. The two datasets which were used for training the
model were "Breast Ultrasound Images" (BUSI) and a set of optic images of
poultry breast tissue samples acquired at our lab. The generated pseudo
anatomical images provide improved visual discrimination of the lesions with
clearer border definition and pronounced contrast. Furthermore, the algorithm
manages to overcome the acoustic shadows artifacts commonly appearing in
ultrasonic images. In order to evaluate the preservation of the anatomical
features, the lesions in the ultrasonic images and the generated pseudo
anatomical images were both automatically segmented and compared. This
comparison yielded median dice score of 0.78 for the benign tumors and 0.43 for
the malignancies. Median lesion center error of 2.38% and 8.42% for the benign
and malignancies respectively and median area error index of 0.77% and 5.06%
for the benign and malignancies respectively. In conclusion, these generated
pseudo anatomical images, which are presented in a more intuitive way, preserve
tissue anatomy and can potentially simplify the diagnosis and improve the
clinical outcome.


title: On the connection formula of a higher rank analog of Painlev\'{e} VI

abstract:
  In this paper, we derive the connection formula for the higher rank analog of
Painlev\'{e} VI arising from the isomonodromic deformation theory and the study
of Frobenius manifolds.


title:
HDC-MiniROCKET: Explicit Time Encoding in Time Series Classification
  with Hyperdimensional Computing

abstract:
  Classification of time series data is an important task for many application
domains. One of the best existing methods for this task, in terms of accuracy
and computation time, is MiniROCKET. In this work, we extend this approach to
provide better global temporal encodings using hyperdimensional computing (HDC)
mechanisms. HDC (also known as Vector Symbolic Architectures, VSA) is a general
method to explicitly represent and process information in high-dimensional
vectors. It has previously been used successfully in combination with deep
neural networks and other signal processing algorithms. We argue that the
internal high-dimensional representation of MiniROCKET is well suited to be
complemented by the algebra of HDC. This leads to a more general formulation,
HDC-MiniROCKET, where the original algorithm is only a special case. We will
discuss and demonstrate that HDC-MiniROCKET can systematically overcome
catastrophic failures of MiniROCKET on simple synthetic datasets. These results
are confirmed by experiments on the 128 datasets from the UCR time series
classification benchmark. The extension with HDC can achieve considerably
better results on datasets with high temporal dependence without increasing the
computational effort for inference.


title:
Wick Theorem and Hopf Algebra Structure in Causal Perturbative Quantum
  Field Theory

abstract:
  We consider the general framework of perturbative quantum field theory for
the pure Yang-Mills model. We give a more precise version of the Wick theorem
using Hopf algebra notations for chronological products and not for Feynman
graphs. Next we prove that Wick expansion property can be preserved for all
cases in order $ n = 2. $ However, gauge invariance is broken for chronological
products of Wick submonomials.


title:
Understanding and Improving Graph Injection Attack by Promoting
  Unnoticeability

abstract:
  Recently Graph Injection Attack (GIA) emerges as a practical attack scenario
on Graph Neural Networks (GNNs), where the adversary can merely inject few
malicious nodes instead of modifying existing nodes or edges, i.e., Graph
Modification Attack (GMA). Although GIA has achieved promising results, little
is known about why it is successful and whether there is any pitfall behind the
success. To understand the power of GIA, we compare it with GMA and find that
GIA can be provably more harmful than GMA due to its relatively high
flexibility. However, the high flexibility will also lead to great damage to
the homophily distribution of the original graph, i.e., similarity among
neighbors. Consequently, the threats of GIA can be easily alleviated or even
prevented by homophily-based defenses designed to recover the original
homophily. To mitigate the issue, we introduce a novel constraint -- homophily
unnoticeability that enforces GIA to preserve the homophily, and propose
Harmonious Adversarial Objective (HAO) to instantiate it. Extensive experiments
verify that GIA with HAO can break homophily-based defenses and outperform
previous GIA attacks by a significant margin. We believe our methods can serve
for a more reliable evaluation of the robustness of GNNs.


title:
Study of the processes $\chi_{cJ} \to \Xi^- \bar{\Xi}^+$ and $\Xi^0
  \bar{\Xi}^0$

abstract:
  Using $448.1\times 10 ^6$ $\psi(3686)$ decays collected with the BESIII
detector at the BEPCII $e^+e^-$ storage rings, the branching fractions and
angular distributions of the decays $\chi_{cJ}\to\Xi^-\bar{\Xi}^+$ and
$\Xi^0\bar{\Xi}^0$ $(J = 0, 1, 2)$ are measured based on a
partial-reconstruction technique. The decays $\chi_{c1}\to\Xi^0\bar{\Xi}^0$ and
$\chi_{c2}\to\Xi^0\bar{\Xi}^0$ are observed for the first time with statistical
significances of 7$\sigma$ and 15$\sigma$, respectively. The results of this
analysis are in good agreement with previous measurements and have
significantly improved precision.


title:
On the asymptotic behaviour of cosmic density-fluctuation power spectra
  of cold dark matter

abstract:
  We study the small-scale asymptotic behaviour of the cold dark matter density
fluctuation power spectrum in the Zel'dovich approximation, without introducing
an ultraviolet cut-off. Assuming an initially correlated Gaussian random field
and spectral index $0 < n_s < 1$, we derive the small-scale asymptotic
behaviour of the initial momentum-momentum correlations. This result is then
used to derive the asymptotics of the power spectrum in the Zel'dovich
approximation. Our main result is an asymptotic series, dominated by a $k^{-3}$
tail at large wave-numbers, containing higher-order terms that differ by
integer powers of $k^{n_s-1}$ and logarithms of $k$. Furthermore, we show that
dark matter power spectra with an ultraviolet cut-off develop an intermediate
range of scales, where the power spectrum is accurately described by the
asymptotics of dark matter without a cut-off. These results reveal information
about the mathematical structure that underlies the perturbative terms in
kinetic field theory and thus the non-linear power spectrum. We also discuss
the sensitivity of the small-scale asymptotics to the spectral index $n_s$.


title: Equivariant Passing-Bablok regression in quasilinear time

abstract:
  Passing-Bablok regression is a standard tool for method and assay comparison
studies thanks to its place in industry guidelines such as CLSI. Unfortunately,
its computational cost is high as a naive approach requires O(n2) time. This
makes it impossible to compute the Passing-Bablok regression estimator on large
datasets. Additionally, even on smaller datasets it can be difficult to perform
bootstrap-based inference. We introduce the first quasilinear time algorithm
for the equivariant Passing-Bablok estimator. In contrast to the naive
algorithm, our algorithm runs in O(n log(n)) expected time using O(n) space,
allowing for its application to much larger data sets. Additionally, we
introduce a fast estimator for the variance of the Passing-Bablok slope and
discuss statistical inference based on bootstrap and this variance estimate.
Finally, we propose a diagnostic plot to identify influential points in
Passing-Bablok regression. The superior performance of the proposed methods is
illustrated on real data examples of clinical method comparison studies.


title:
Holonomic control of a three-qubits system in an NV center using a
  near-term quantum computer

abstract:
  The holonomic approach to controlling (nitrogen-vacancy) NV-center qubits
provides an elegant way of theoretically devising universal quantum gates that
operate on qubits via calculable microwave pulses. There is, however, a lack of
simulated results from the theory of holonomic control of quantum registers
with more than two qubits describing the transition between the dark states. In
light of this, we have been experimenting with the IBM Quantum Experience
technology to determine the capabilities of simulating holonomic control of
NV-centers for three qubits describing an eight-level system that produces a
non-Abelian geometric phase. The tunability of the geometric phase via the
detuning frequency is demonstrated through the high fidelity (about 80%) of
3-qubit off-resonant holonomic gates over the on-resonant ones. The transition
between the dark states shows the alignment of the gate dark state with the
qubits initial state hence decoherence of the multi-qubit system is
well-controlled through a 0.33pi rotation. The electron return probability can
exhibit spin-orbit coupling-like behavior as observed in topological materials
based on the extra geometric phase.


title:
Where the Model Frequently Meets the Road: Combining Statistical,
  Formal, and Case Study Methods

abstract:
  This paper analyzes the working or default assumptions researchers in the
formal, statistical, and case study traditions typically hold regarding the
sources of unexplained variance, the meaning of outliers, parameter values,
human motivation, functional forms, time, and external validity. We argue that
these working assumptions are often not essential to each method, and that
these assumptions can be relaxed in ways that allow multimethod work to
proceed. We then analyze the comparative advantages of different combinations
of formal, statistical, and case study methods for various theory-building and
theory-testing research objectives. We illustrate these advantages and offer
methodological advice on how to combine different methods, through analysis and
critique of prominent examples of multimethod research.


title: Knowledge Extraction in Low-Resource Scenarios: Survey and Perspective

abstract:
  Knowledge Extraction (KE) which aims to extract structural information from
unstructured texts often suffers from data scarcity and emerging unseen types,
i.e., low-resource scenarios. Many neural approaches on low-resource KE have
been widely investigated and achieved impressive performance. In this paper, we
present a literature review towards KE in low-resource scenarios, and
systematically categorize existing works into three paradigms: (1) exploiting
higher-resource data, (2) exploiting stronger models, and (3) exploiting data
and models together. In addition, we describe promising applications and
outline some potential directions for future research. We hope that our survey
can help both the academic and industrial community to better understand this
field, inspire more ideas and boost broader applications.


title: Learning a Single Neuron for Non-monotonic Activation Functions

abstract:
  We study the problem of learning a single neuron $\mathbf{x}\mapsto
\sigma(\mathbf{w}^T\mathbf{x})$ with gradient descent (GD). All the existing
positive results are limited to the case where $\sigma$ is monotonic. However,
it is recently observed that non-monotonic activation functions outperform the
traditional monotonic ones in many applications. To fill this gap, we establish
learnability without assuming monotonicity. Specifically, when the input
distribution is the standard Gaussian, we show that mild conditions on $\sigma$
(e.g., $\sigma$ has a dominating linear part) are sufficient to guarantee the
learnability in polynomial time and polynomial samples. Moreover, with a
stronger assumption on the activation function, the condition of input
distribution can be relaxed to a non-degeneracy of the marginal distribution.
We remark that our conditions on $\sigma$ are satisfied by practical
non-monotonic activation functions, such as SiLU/Swish and GELU. We also
discuss how our positive results are related to existing negative results on
training two-layer neural networks.


title:
Graph Neural Network and Koopman Models for Learning Networked Dynamics:
  A Comparative Study on Power Grid Transients Prediction

abstract:
  Continuous monitoring of the spatio-temporal dynamic behavior of critical
infrastructure networks, such as the power systems, is a challenging but
important task. In particular, accurate and timely prediction of the
(electro-mechanical) transient dynamic trajectories of the power grid is
necessary for early detection of any instability and prevention of catastrophic
failures. Existing approaches for the prediction of dynamic trajectories either
rely on the availability of accurate physical models of the system, use
computationally expensive time-domain simulations, or are applicable only at
local prediction problems (e.g., a single generator). In this paper, we report
the application of two broad classes of data-driven learning models -- along
with their algorithmic implementation and performance evaluation -- in
predicting transient trajectories in power networks using only streaming
measurements and the network topology as input. One class of models is based on
the Koopman operator theory which allows for capturing the nonlinear dynamic
behavior via an infinite-dimensional linear operator. The other class of models
is based on the graph convolutional neural networks which are adept at
capturing the inherent spatio-temporal correlations within the power network.
Transient dynamic datasets for training and testing the models are synthesized
by simulating a wide variety of load change events in the IEEE 68-bus system,
categorized by the load change magnitudes, as well as by the degree of
connectivity and the distance to nearest generator nodes. The results confirm
that the proposed predictive models can successfully predict the
post-disturbance transient evolution of the system with a high level of
accuracy.


title: Almost-Optimal Sublinear-Time Edit Distance in the Low Distance Regime

abstract:
  We revisit the task of computing the edit distance in sublinear time. In the
$(k,K)$-gap edit distance problem the task is to distinguish whether the edit
distance of two strings is at most $k$ or at least $K$. It has been established
by Goldenberg, Krauthgamer and Saha (FOCS '19), with improvements by Kociumaka
and Saha (FOCS '20), that the $(k,k^2)$-gap problem can be solved in time
$\widetilde O(n/k+\operatorname{poly}(k))$. One of the most natural questions
in this line of research is whether the $(k,k^2)$-gap is best-possible for the
running time $\widetilde O(n/k+\operatorname{poly}(k))$.
  In this work we answer this question by significantly improving the gap.
Specifically, we show that in time $O(n/k+\operatorname{poly}(k))$ we can even
solve the $(k,k^{1+o(1)})$-gap problem. This is the first algorithm that breaks
the $(k,k^2)$-gap in this running time. Our algorithm is almost optimal in the
following sense: In the low distance regime ($k\le n^{0.19}$) our running time
becomes $O(n/k)$, which matches a known $n/k^{1+o(1)}$ lower bound for the
$(k,k^{1+o(1)})$-gap problem up to lower order factors.
  Our result also reveals a surprising similarity of Hamming distance and edit
distance in the low distance regime: For both, the $(k,k^{1+o(1)})$-gap problem
has time complexity $n/k^{1\pm o(1)}$ for small $k$.
  In contrast to previous work, which employed a subsampled variant of the
Landau-Vishkin algorithm, we instead build upon the algorithm of Andoni,
Krauthgamer and Onak (FOCS '10). We first simplify their approach and then show
how to to effectively prune their computation tree in order to obtain a
sublinear-time algorithm in the given time bound. Towards that, we use a
variety of structural insights on the (local and global) patterns that can
emerge during this process and design appropriate property testers to
effectively detect these patterns.


title: Categorical approach to the Baum-Connes conjecture for \'etale groupoids

abstract:
  We consider the equivariant Kasparov category associated to an \'etale
groupoid, and by leveraging its triangulated structure we study its
localization at the "weakly contractible" objects, extending previous work by
R. Meyer and R. Nest. We prove the subcategory of weakly contractible objects
is complementary to the localizing subcategory of projective objects, which are
defined in terms of "compactly induced" algebras with respect to certain proper
subgroupoids related to isotropy. The resulting "strong" Baum-Connes conjecture
implies the classical one, and its formulation clarifies several permanence
properties and other functorial statements. We present multiple applications,
including consequences for the Universal Coefficient Theorem, a generalized
"Going-Down" principle, injectivity results for groupoids that are amenable at
infinity, the Baum-Connes conjecture for group bundles, and a result about the
invariance of $K$-groups of twisted groupoid $C^*$-algebras under homotopy of
twists.


title: A Polyhedral Study of Lifted Multicuts

abstract:
  Fundamental to many applications in data analysis are the decompositions of a
graph, i.e. partitions of the node set into component-inducing subsets. One way
of encoding decompositions is by multicuts, the subsets of those edges that
straddle distinct components. Recently, a lifting of multicuts from a graph $G
= (V, E)$ to an augmented graph $\hat G = (V, E \cup F)$ has been proposed in
the field of image analysis, with the goal of obtaining a more expressive
characterization of graph decompositions in which it is made explicit also for
pairs $F \subseteq \tbinom{V}{2} \setminus E$ of non-neighboring nodes whether
these are in the same or distinct components. In this work, we study in detail
the polytope in $\mathbb{R}^{E \cup F}$ whose vertices are precisely the
characteristic vectors of multicuts of $\hat G$ lifted from $G$, connecting it,
in particular, to the rich body of prior work on the clique partitioning and
multilinear polytope.


title: VLDB 2021: Designing a Hybrid Conference

abstract:
  In 2020, while main database conferences one by one had to adopt a virtual
format as a result of the ongoing COVID-19 pandemic, we decided to hold VLDB
2021 in hybrid format. This paper describes how we defined the hybrid format
for VLDB 2021 going through the key design decisions. In addition, we list the
lessons learned from running such a conference. Our goal is to share this
knowledge with fellow conference organizers who target a hybrid conference
format as well, which is on its way to becoming the norm rather than the
exception. For readers who are more interested in the highlights rather than
details, a short version of this report appears in SIGMOD Record.


title: On Measuring Excess Capacity in Neural Networks

abstract:
  We study the excess capacity of deep networks in the context of supervised
classification. That is, given a capacity measure of the underlying hypothesis
class -- in our case, Rademacher complexity -- how much can we (a-priori)
constrain this class while maintaining an empirical error comparable to the
unconstrained setting. To assess excess capacity in modern architectures, we
first extend an existing generalization bound to accommodate function
composition and addition, as well as the specific structure of convolutions.
This then facilitates studying residual networks through the lens of the
accompanying capacity measure. The key quantities driving this measure are the
Lipschitz constants of the layers and the (2,1) group norm distance to the
initializations of the convolution weights. We show that these quantities (1)
can be kept surprisingly small and, (2) since excess capacity unexpectedly
increases with task difficulty, this points towards an unnecessarily large
capacity of unconstrained models.


title:
Local Potential Functional Embedding Theory: A Self-Consistent Flavor of
  Density Functional Theory for Lattices without Density Functionals

abstract:
  The recently proposed Householder transformed density-matrix functional
embedding theory (Ht-DMFET) [Sekaran et al., Phys. Rev. B 104, 035121 (2021)],
which is equivalent to (but formally simpler than) density matrix embedding
theory (DMET) in the non-interacting case, is revisited from the perspective of
density-functional theory (DFT). An in-principle-exact density-functional
version of Ht-DMFET is derived for the one-dimensional Hubbard lattice with a
single embedded impurity. On the basis of well-identified density-functional
approximations, a local potential functional embedding theory (LPFET) is
formulated and implemented. Even though LPFET performs better than Ht-DMFET in
the low-density regime, in particular when electron correlation is strong, both
methods are unable to describe the density-driven Mott-Hubbard transition, as
expected. These results combined with our formally exact density-functional
embedding theory reveal that a single statically embedded impurity can in
principle describe the gap opening, provided that the complementary correlation
potential (that describes the interaction of the embedding cluster with its
environment, which is simply neglected in both Ht-DMFET and LPFET) exhibits a
derivative discontinuity (DD) at half filling. The extension of LPFET to
multiple impurities (which would enable to circumvent the modeling of DDs) and
its generalization to quantum chemical Hamiltonians are left for future work.


title:
Accurate measures of regional lung air volumes from chest X-rays of
  small animals

abstract:
  We present a robust technique for calculating regional volume changes within
the lung from X-ray radiograph sequences captured during ventilation, without
the use of computed tomography (CT). This technique is based on the change in
transmitted X-ray intensity that occurs for each lung region as air displaces
the attenuating lung tissue. Lung air volumes calculated from X-ray intensity
changes showed a strong correlation ($R^2$=0.98) against the true volumes,
measured from high-resolution CT. This correlation enables us to accurately
convert projected intensity data into relative changes in lung air volume. We
have applied this technique to measure changes in regional lung volumes from
X-ray image sequences of mechanically ventilated, recently-deceased newborn
rabbits, without the use of CT. This method is suitable for biomedical research
studies and shows potential for clinical application.


title:
Enhanced Next-to-Leading-Order Corrections to Weak Annihilation
  $B$-Meson Decays

abstract:
  We accomplish the analytical computation of the pure weak annihilation
non-leptonic $B$-meson decay amplitudes at leading power in the heavy quark
expansion. The novel observation regarding such fundamental hadronic quantities
is that adding the missing hard-collinear contribution on top of the hard gluon
exchange effect eliminates rapidity divergences entering the convolution
integrals of factorization formulae. Subsequently we identify the perturbative
enhancement mechanism due to the penguin contractions of the current-current
operators from the weak effective Hamiltonian, which yields the significant
impacts on the CP violating observables.


title: Seshadri Constants Over Fields Of Characteristic Zero

abstract:
  Let $X$ be a smooth projective variety defined over a field $k$ of
characteristic $0$ and let $\mathcal{L}$ be a nef line bundle defined over $k$.
We prove that if $x\in X$ is a $k$-rational point then the Seshadri constant
$\epsilon(X, \mathcal{L}, x)$ over $\overline{k}$ is the same as that over $k$.
We show, by constructing families of examples, that there are varieties whose
global Seshadri constant $\epsilon(X)$ is zero. We also prove a result on the
existence of a Seshadri curve with a natural (and necessary) hypothesis.


title: Trianguline periods

abstract:
  Let $K$ be a finite extension of $\mathbf{Q}_p$. In this paper, we construct
a ring of periods $\mathbf{B}_{\mathrm{tri},K}^{an}$ such that $K$-analytic
trianguline representations of $\mathcal{G}_K$, the absolute Galois group of
$K$, are admissible for this ring, where admissibility is defined in a broader
sense that Fontaine's. We also prove a partial converse, so that
$\mathbf{B}_{\mathrm{tri},K}^{an}$-admissible representations are "almost
trianguline".


title: Bimorphic Floquet Topological Insulators

abstract:
  Topological theories have established a new set of rules that govern the
transport properties in a wide variety of wave-mechanical settings. In a marked
departure from the established approaches that induce Floquet topological
phases by specifically tailored discrete coupling protocols or helical lattice
motions, we introduce a new class of bimorphic Floquet topological insulators
that leverage connective chains with periodically modulated on-site potentials
to unlock new topological features in the system. In exploring a 'chain-driven'
generalization of the archetypical Floquet honeycomb lattice, we identify a
rich phase structure that can host multiple non-trivial topological phases
associated simultaneously with both Chern-type and anomalous chiral states.
Experiments carried out in photonic waveguide lattices reveal a unique and
strongly confined helical edge state that, owing to its origin in bulk flat
bands, can be set into motion in a topologically protected fashion, or halted
at will, without compromising its adherence to individual lattice sites.


title: Embedded Young Massive Star Clusters in the Antennae Merger

abstract:
  The properties of young massive clusters (YMCs) are key to understanding the
star formation mechanism in starburst systems, especially mergers. We present
ALMA high-resolution ($\sim$10 pc) continuum (100 and 345 GHz) data of YMCs in
the overlap region of the Antennae galaxy. We identify 6 sources in the overlap
region, including two sources that lie in the same giant molecular cloud (GMC).
These YMCs correspond well with radio sources in lower resolution continuum
(100 and 220 GHz) images at GMC scales ($\sim$60 pc). We find most of these
YMCs are bound clusters through virial analysis. We estimate their ages to be
$\sim$1 Myr and to be either embedded or just beginning to emerge from their
parent cloud. We also compare each radio source with Pa$\beta$ source and find
they have consistent total ionizing photon numbers, which indicates they are
tracing the same physical source. By comparing the free-free emission at
$\sim$10 pc scale and $\sim$60 pc scale, we find that $\sim$50% of the
free-free emission in GMCs actually comes from these YMCs. This indicates that
roughly half of the stars in massive GMCs are formed in bound clusters. We
further explore the mass correlation between YMCs and GMCs in the Antennae and
find it generally agrees with the predictions of the star cluster simulations.
The most massive YMC has a stellar mass that is 1% - 5% of its host GMC mass.


title:
Quantum speed limit time: role of coherence as a dynamical witness to
  distinguish multi-qubit entangled states

abstract:
  The minimum evolution time between multi-qubit quantum states is estimated
for non-Markovian quantum channels. We consider the maximally coherent pure and
mixed states as well as multi-qubit $X$ states as initial states and discuss
the impacts of initial coherence on their speed of evolution for both dephasing
and dissipative processes. The role of the non-zero value of initial coherence
under information backflow conditions for the dissipative process is revealed
by the flow of quantum speed limit time. The trade-off between mixedness and
coherence on the speed limit time reveals the nature of the quantum process the
states undergo. The complementarity effect between mixedness and coherence is
more prominent in the quantum dissipation process. The parametric trajectory of
speed limit time vividly depicts the difference in the evolution of pure and
mixed initial states. Our investigation of quantum speed limit time on
multi-qubit entangled $X$ states reveals that $\tau_{QSL}$ can be identified as
a potential dynamical witness to distinguish multi-qubit states in the course
of evolution.


title: Modeling Strong Physically Unclonable Functions with Metaheuristics

abstract:
  Evolutionary algorithms have been successfully applied to attacking
Physically Unclonable Functions (PUFs). CMA-ES is recognized as the most
powerful option for a type of attack called the reliability attack. While there
is no reason to doubt the performance of CMA-ES, the lack of comparison with
different metaheuristics and results for the challenge-response pair-based
attack leaves open questions if there are better-suited metaheuristics for the
problem.
  In this paper, we take a step back and systematically evaluate several
metaheuristics for the challenge-response pair-based attack on strong PUFs. Our
results confirm that CMA-ES has the best performance, but we also note several
other algorithms with similar performance while having smaller computational
costs. More precisely, if we provide a sufficient number of challenge-response
pairs to train the algorithm, various configurations show good results.
Consequently, we conclude that EAs represent a strong option for
challenge-response pair-based attacks on PUFs.


title: NeVerMore: Exploiting RDMA Mistakes in NVMe-oF Storage Applications

abstract:
  This paper presents a security analysis of the InfiniBand architecture, a
prevalent RDMA standard, and NVMe-over-Fabrics (NVMe-oF), a prominent protocol
for industrial disaggregated storage that exploits RDMA protocols to achieve
low-latency and high-bandwidth access to remote solid-state devices. Our work,
NeVerMore, discovers new vulnerabilities in RDMA protocols that unveils several
attack vectors on RDMA-enabled applications and the NVMe-oF protocol, showing
that the current security mechanisms of the NVMe-oF protocol do not address the
security vulnerabilities posed by the use of RDMA. In particular, we show how
an unprivileged user can inject packets into any RDMA connection created on a
local network controller, bypassing security mechanisms of the operating system
and its kernel, and how the injection can be used to acquire unauthorized block
access to NVMe-oF devices. Overall, we implement four attacks on RDMA protocols
and seven attacks on the NVMe-oF protocol and verify them on the two most
popular implementations of NVMe-oF: SPDK and the Linux kernel. To mitigate the
discovered attacks we propose multiple mechanisms that can be implemented by
RDMA and NVMe-oF providers.


title:
Reasoning with fuzzy and uncertain evidence using epistemic random fuzzy
  sets: general framework and practical models

abstract:
  We introduce a general theory of epistemic random fuzzy sets for reasoning
with fuzzy or crisp evidence. This framework generalizes both the
Dempster-Shafer theory of belief functions, and possibility theory. Independent
epistemic random fuzzy sets are combined by the generalized
product-intersection rule, which extends both Dempster's rule for combining
belief functions, and the product conjunctive combination of possibility
distributions. We introduce Gaussian random fuzzy numbers and their
multi-dimensional extensions, Gaussian random fuzzy vectors, as practical
models for quantifying uncertainty about scalar or vector quantities.
Closed-form expressions for the combination, projection and vacuous extension
of Gaussian random fuzzy numbers and vectors are derived.


title:
Formulating Beurling LASSO for Source Separation via Proximal Gradient
  Iteration

abstract:
  Beurling LASSO generalizes the LASSO problem to finite Radon measures
regularized via their total variation. Despite its theoretical appeal, this
space is hard to parametrize, which poses an algorithmic challenge. We propose
a formulation of continuous convolutional source separation with Beurling LASSO
that avoids the explicit computation of the measures and instead employs the
duality transform of the proximal mapping.


title:
The $L^p$-boundedness of wave operators for four dimensional
  Schr\"odinger operators with threshold resonances

abstract:
  We prove that the low energy parts of the wave operators $W_\pm$ for
Schr\"odinger operators $H = -\lap + V(x)$ on $\R^4$ are bounded in $
L^p(\R^4)$ for $1<p\leq 2$ and are unbounded for $2<p\leq \infty$ if $H$ has
resonances at the threshold. If $H$ has eigenfunctions only at the threshold,
it has recently been proved that they are bounded in $L^p(\R^4)$ for $1\leq
p<4$ in general and for $1\leq p<\infty$ if all threshold eigenfunctions $\ph$
satisfy $\int_{\R^4}x_j V(x) \ph(x)dx=0$ for $1\leq j\leq 4$. We prove in this
case that they are unbounded in $L^p(\R^4)$ for $4<p<\infty$ unless the latter
condition is satisfied. It is long known that the high energy parts are bounded
in $L^p(\R^4)$ for all $1\leq p\leq \infty$ and that the same holds for $W_\pm$
if $H$ has no eigenfunctions nor resonances at the threshold.


abstract:
  Entanglement-assisted concatenated quantum codes (EACQCs), constructed by
concatenating two quantum codes, are proposed. These EACQCs show several
advantages over the standard concatenated quantum codes (CQCs). Several
families of EACQCs that, unlike standard CQCs, can beat the nondegenerate
Hamming bound for entanglement-assisted quantum error correction codes
(EAQECCs) are derived. Further, a number of EACQCs with better parameters than
the best known standard quantum error correction codes (QECCs) and EAQECCs are
also derived. In particular, several catalytic EACQCs with better parameters
than the best known QECCs of the same length and net transmission are
constructed. Furthermore, each catalytic EACQC consumes only one or two ebits.
It is also shown that EACQCs make entanglement-assisted quantum communication
possible even if the ebits are noisy. Finally, it is shown that EACQCs can
outperform CQCs in entanglement fidelity over depolarizing channels if the
ebits are less noisy than the qubits. Moreover, the threshold error probability
of EACQCs is larger than that of CQCs when the error probability of ebits is
sufficiently lower than that of qubits. Therefore EACQCs are not only
competitive in quantum communication but also applicable in fault-tolerant
quantum computation.


title: Retractions and the bounded approximation property in Banach spaces

abstract:
  In the present paper we prove that a necessary condition for a Banach space
$X$ to admit a generating compact Lipschitz retract $K$, which satisfies an
additional mild assumption on its shape, is that $X$ enjoys the Bounded
Approximation Property. This is a partial solution to a question raised by
Godefroy and Ozawa.


title:
Enhanced Reflectivity Change and Phase Shift of Polarized Light: Double
  Parameter Multilayer Sensor

abstract:
  Herein, the concept of point of darkness based on polarized light phase
difference and absorption of light is demonstrated by simulations using low
refractive index and extinction coefficient semiconductor and dielectric, and
high refractive index nonoxidizing metal multilayer thin film structures.
Several multilayer sensor configurations show great sensitivity to thickness
and refractive index variation of the detectable material by measuring the
reflectivity ratio {\Psi} and phase shift {\Delta}. Focus is on such
multilayers, which have sensitivity to both parameters ({\Psi}, {\Delta}) in
the visible spectral range, thus opening the possibility for further research
on a new biomedical sensor development with enhanced double parameter sensing.


title: Extended Unconstrained Features Model for Exploring Deep Neural Collapse

abstract:
  The modern strategy for training deep neural networks for classification
tasks includes optimizing the network's weights even after the training error
vanishes to further push the training loss toward zero. Recently, a phenomenon
termed "neural collapse" (NC) has been empirically observed in this training
procedure. Specifically, it has been shown that the learned features (the
output of the penultimate layer) of within-class samples converge to their
mean, and the means of different classes exhibit a certain tight frame
structure, which is also aligned with the last layer's weights. Recent papers
have shown that minimizers with this structure emerge when optimizing a
simplified "unconstrained features model" (UFM) with a regularized
cross-entropy loss. In this paper, we further analyze and extend the UFM.
First, we study the UFM for the regularized MSE loss, and show that the
minimizers' features can be more structured than in the cross-entropy case.
This affects also the structure of the weights. Then, we extend the UFM by
adding another layer of weights as well as ReLU nonlinearity to the model and
generalize our previous results. Finally, we empirically demonstrate the
usefulness of our nonlinear extended UFM in modeling the NC phenomenon that
occurs with practical networks.


title: Latent Outlier Exposure for Anomaly Detection with Contaminated Data

abstract:
  Anomaly detection aims at identifying data points that show systematic
deviations from the majority of data in an unlabeled dataset. A common
assumption is that clean training data (free of anomalies) is available, which
is often violated in practice. We propose a strategy for training an anomaly
detector in the presence of unlabeled anomalies that is compatible with a broad
class of models. The idea is to jointly infer binary labels to each datum
(normal vs. anomalous) while updating the model parameters. Inspired by outlier
exposure (Hendrycks et al., 2018) that considers synthetically created, labeled
anomalies, we thereby use a combination of two losses that share parameters:
one for the normal and one for the anomalous data. We then iteratively proceed
with block coordinate updates on the parameters and the most likely (latent)
labels. Our experiments with several backbone models on three image datasets,
30 tabular data sets, and a video anomaly detection benchmark showed consistent
and significant improvements over the baselines.


title:
Forced waves for a three-species predator-prey system with nonlocal
  dispersal in a shifting environment

abstract:
  We consider a three-species predator-prey system involving two competing
predators and one prey. The species diffuse with nonlocal dispersal kernels
with possibly non-compact support, and they interact in a heterogeneous
environment moving with a positive forced speed such that the environment is
favorable to the prey in the absence of predators far ahead of the shifting
boundary and it is unfavorable far behind. Such systems arise in the modeling
of population dynamics under the effect of a shifting environment, such as
climate change. We show on the one hand the existence of waves connecting the
trivial state to the unique constant positive coexistence state for any value
of the forced speed. On the other hand, we show the existence of critical
positive speeds for the existence of waves connecting the trivial state to the
states corresponding to the absence of one or two predators.


title:
Wafer-scale low-disorder 2DEG in $^{28}$Si/SiGe without an epitaxial Si
  cap

abstract:
  We grow $^{28}$Si/SiGe heterostructures by reduced-pressure chemical vapor
deposition and terminate the stack without an epitaxial Si cap but with an
amorphous Si-rich layer obtained by exposing the SiGe barrier to dichlorosilane
at 500 {\deg}C. As a result, $^{28}$Si/SiGe heterostructure field-effect
transistors feature a sharp semiconductor/dielectric interface and support a
two-dimensional electron gas with enhanced and more uniform transport
properties across a 100 mm wafer. At T = 1.7 K we measure a high mean mobility
of (1.8$\pm$0.5)$\times$10$^5$ cm$^2$/Vs and a low mean percolation density of
(9$\pm$1)$\times$10$^{10}$ cm$^{-2}$. From the analysis of Shubnikov-de Haas
oscillations at T = 190 mK we obtain a long mean single particle relaxation
time of (8.1$\pm$0.5) ps, corresponding to a mean quantum mobility and quantum
level broadening of (7.5$\pm$0.6)$\times$10$^4$ cm$^{2}$/Vs and (40$\pm$3)
$\mu$eV, respectively, and a small mean Dingle ratio of (2.3$\pm$0.2),
indicating reduced scattering from long range impurities and a low-disorder
environment for hosting high-performance spin-qubits.


title:
Rare top-quark decays $t \to cg(g)$ in the aligned two-Higgs-doublet
  model

abstract:
  We update the Standard Model predictions for the branching ratios of the rare
top-quark decays $t \to cg(g)$, and evaluate the maximum rates that can be
reached in the aligned as well as in the four conventional two-Higgs-doublet
models (2HDMs) with $\mathcal{Z}_2$ symmetries. Taking into account the
relevant constraints on the model parameters from precision flavour
observables, we find that the branching ratios of $t \to cg$ and $t \to cgg$
decays can reach up to $3.36\times 10^{-9}$ and $2.95\times 10^{-9}$
respectively, being therefore of the same order, in the aligned 2HDM (A2HDM).
This is obviously different from the SM case, where the predicted branching
ratio of the three-body decay $t \to cgg$ is about two orders of magnitude
larger than that of the two-body decay $t \to cg$. On the other hand, compared
with the SM predictions, no significant enhancements are observed in the four
conventional 2HDMs with $\mathcal{Z}_2$ symmetries for the branching ratios of
these two decays. Nevertheless, the predicted branching ratios of $t \to cg$
and $t \to cgg$ decays in the A2HDM are still out of the expected sensitivities
of the future HL-LHC and FCC-hh.


title: Factorization with a logarithmic energy spectrum of a central potential

abstract:
  We propose a method to factor numbers based on two interacting bosonic atoms
in a central potential where the single-particle spectrum depends
logarithmically on the radial quantum numbers of the zero angular momentum
states. The bosons initially prepared in the ground state are excited by a
sinusoidally time-dependent interaction into a state characterized by the
quantum numbers which represent the factors of a number encoded in the
frequency of the perturbation. We also discuss the full single-particle
spectrum and limitations of our method caused by decoherence.


title:
High-performance and Low-power Transistors Based on Anisotropic
  Monolayer $\beta$-TeO$_2$

abstract:
  Two-dimensional (2D) semiconductors offer a promising prospect for
high-performance and energy-efficient devices especially in the sub-10 nm
regime. Inspired by the successful fabrication of 2D $\beta$-TeO$_2$ and the
high on/off ratio and high air-stability of fabricated field effect transistors
(FETs) [Nat. Electron. 2021, 4, 277], we provide a comprehensive investigation
of the electronic structure of monolayer $\beta$-TeO$_2$ and the device
performance of sub-10 nm metal oxide semiconductors FETs (MOSFETs) based on
this material. The anisotropic electronic structure of monolayer
$\beta$-TeO$_2$ plays a critical role in the anisotropy of transport properties
for MOSFETs. We show that the 5.2-nm gate-length n-type MOSFET holds an
ultra-high on-state current exceeding 3700 {\mu}A/{\mu}m according to
International Roadmap for Devices and Systems (IRDS) 2020 goals for
high-performance devices, which is benefited by the highly anisotropic electron
effective mass. Moreover, monolayer $\beta$-TeO$_2$ MOSFETs can fulfill the
IRDS 2020 goals for both high-performance and low-power devices in terms of
on-state current, sub-threshold swing, delay time, and power-delay product.
This study unveils monolayer $\beta$-TeO$_2$ as a promising candidate for
ultra-scaled devices in future nanoelectronics.


title: Investigation of the MicroBooNE neutrino cross sections on Argon

abstract:
  Experimental data of charged current neutrino cross sections on argon have
recently appeared. They are classified in terms of the detected particles, such
as one muon (the inclusive cross section) or one muon, zero pion and one single
proton (CC0$\pi$1p) in the final states. We test our model, which allows a
description of different reaction vertices, against the CC0$\pi$1p, the
CC0$\pi$Np and the CC inclusive measurements. For this last channel, a new
aspect is the recent availability of data in terms of the energy transfer to
the nucleus, which allows a better separation of the different reaction
mechanisms. Globally we find a good agreement with data and we explain the
deterioration of agreement in specific kinematical conditions by the absence in
our model of two-pion production and other inelastic channels, more important
for MicroBooNE than for T2K. We discuss also the correspondence between the
quasielastic vertex and the CC0$\pi$1p measurement, as well as the dominance of
the multinucleon emission channel in the difference between CC0$\pi$Np and
CC0$\pi$1p data.


title: Refined RBF-FD analysis of non-Newtonian natural convection

abstract:
  In this paper we present a refined RBF-FD solution for a non-Newtonian fluid
in a closed differentially heated cavity. The problem at hand is governed by
three coupled nonlinear partial differential equations, namely heat transport,
momentum transport and mass continuity. The non-Newtonian behaviour is modelled
with the Ostwald-de Weele power law and the buoyancy with the Boussinesq
approximation. The problem domain is discretised with scattered nodes without
any requirement for a topological relation between them. This allows a trivial
generalisation of the solution procedure to complex irregular 3D domains, which
is also demonstrated by solving the problem in a 2D and 3D geometry mimicking
the porous filter. The results in 2D are compared with two reference solutions
that use the Finite volume method in a conjunction with two different
stabilisation techniques (upwind and QUICK), where we achieved good agreement
with the reference data. The refinement is implemented on top of a dedicated
meshless node positioning algorithm using piecewise linear node density
function that ensures sufficient node density in the centre of the domain while
maximising the node density in a boundary layer where the most intense dynamic
is expected. The results show that with a refined approach, up to 5 times fewer
nodes are needed to obtain the results with the same accuracy compared to the
regular discretisation. The paper also discusses the convergence for different
scenarios for up to $2 \cdot 10^5$ nodes, the behaviour of the flow in the
boundary layer, the behaviour of the viscosity and the geometric flexibility of
the proposed solution procedure.

categories: math.NA cs.NA physics.comp-ph physics.flu-dyn


title: Massive Unsourced Random Access: Exploiting Angular Domain Sparsity

abstract:
  This paper investigates the unsourced random access (URA) scheme to
accommodate numerous machine-type users communicating to a base station
equipped with multiple antennas. Existing works adopt a slotted transmission
strategy to reduce system complexity; they operate under the framework of
coupled compressed sensing (CCS) which concatenates an outer tree code to an
inner compressed sensing code for slot-wise message stitching. We suggest that
by exploiting the MIMO channel information in the angular domain, redundancies
required by the tree encoder/decoder in CCS can be removed to improve spectral
efficiency, thereby an uncoupled transmission protocol is devised. To perform
activity detection and channel estimation, we propose an
expectation-maximization-aided generalized approximate message passing
algorithm with a Markov random field support structure, which captures the
inherent clustered sparsity structure of the angular domain channel. Then,
message reconstruction in the form of a clustering decoder is performed by
recognizing slot-distributed channels of each active user based on similarity.
We put forward the slot-balanced K-means algorithm as the kernel of the
clustering decoder, resolving constraints and collisions specific to the
application scene. Extensive simulations reveal that the proposed scheme
achieves a better error performance at high spectral efficiency compared to the
CCS-based URA schemes.


title: Optimizing over Serial Dictatorships

abstract:
  Motivated by the success of the {\em serial dictatorship} mechanism in social
choice settings, we explore its usefulness in tackling various combinatorial
optimization problems. We do so by considering an abstract model, in which a
set of agents are asked to {\em act} in a particular ordering, called the {\em
action sequence}. Each agent acts in a way that gives her the maximum possible
value, given the actions of the agents who preceded her in the action sequence.
Our goal is to compute action sequences that yield approximately optimal total
value to the agents (a.k.a., {\em social welfare}). We assume {\em query
access} to the value $v_i(S)$ that the agent i gets when she acts after the
agents in the ordered set $S$.
  We establish tight bounds on the social welfare that can be achieved using
polynomially many queries. Even though these bounds show a marginally sublinear
approximation of optimal social welfare in general, excellent approximations
can be obtained when the valuations stem from an underlying combinatorial
domain. Indicatively, when the valuations are defined using bipartite
matchings, arborescences in directed graphs, and satisfiability of Boolean
expressions, simple query-efficient algorithms yield $2$-approximations. We
discuss issues related to truthfulness and show how some of our algorithms can
be implemented truthfully using VCG-like payments. Finally, we introduce and
study the {\em price of serial dictatorship}, a notion that provides an
optimistic measure of the quality of combinatorial optimization solutions
generated by action sequences.


title: Learning to Adapt to Light

abstract:
  Light adaptation or brightness correction is a key step in improving the
contrast and visual appeal of an image. There are multiple light-related tasks
(for example, low-light enhancement and exposure correction) and previous
studies have mainly investigated these tasks individually. However, it is
interesting to consider whether these light-related tasks can be executed by a
unified model, especially considering that our visual system adapts to external
light in such way. In this study, we propose a biologically inspired method to
handle light-related image-enhancement tasks with a unified network (called
LA-Net). First, a frequency-based decomposition module is designed to decouple
the common and characteristic sub-problems of light-related tasks into two
pathways. Then, a new module is built inspired by biological visual adaptation
to achieve unified light adaptation in the low-frequency pathway. In addition,
noise suppression or detail enhancement is achieved effectively in the
high-frequency pathway regardless of the light levels. Extensive experiments on
three tasks -- low-light enhancement, exposure correction, and tone mapping --
demonstrate that the proposed method almost obtains state-of-the-art
performance compared with recent methods designed for these individual tasks.


title:
Measuring Unintended Memorisation of Unique Private Features in Neural
  Networks

abstract:
  Neural networks pose a privacy risk to training data due to their propensity
to memorise and leak information. Focusing on image classification, we show
that neural networks also unintentionally memorise unique features even when
they occur only once in training data. An example of a unique feature is a
person's name that is accidentally present on a training image. Assuming access
to the inputs and outputs of a trained model, the domain of the training data,
and knowledge of unique features, we develop a score estimating the model's
sensitivity to a unique feature by comparing the KL divergences of the model's
output distributions given modified out-of-distribution images. Our results
suggest that unique features are memorised by multi-layer perceptrons and
convolutional neural networks trained on benchmark datasets, such as MNIST,
Fashion-MNIST and CIFAR-10. We find that strategies to prevent overfitting
(e.g.\ early stopping, regularisation, batch normalisation) do not prevent
memorisation of unique features. These results imply that neural networks pose
a privacy risk to rarely occurring private information. These risks can be more
pronounced in healthcare applications if patient information is present in the
training data.


title:
Reversible data hiding with dual pixel-value-ordering and1minimum
  prediction error expansion

abstract:
  Pixel Value Ordering (PVO) holds an impressive property for high fidelity
Reversible Data Hiding (RDH). In this paper, we introduce a dual-PVO (dPVO) for
Prediction Error Expansion(PEE), and thereby develop a new RDH scheme to offer
a better rate-distortion performance. Particularly, we propose to embed in two
phases: forward and backward. In the forward phase, PVO with classic PEE is
applied to every non-overlapping image block of size 1x3. In the backward
phase,minimum-set and maximum-set of pixels are determined from the pixels
predicted in the forward phase. The minimum set only contains the lowest
predicted pixels and the maximum set contains the largest predicted pixels of
each image block. Proposed dPVO withPEE is then applied to both sets, so that
the pixel values of the minimum set are increased and that of the maximum set
are decreased by a unit value. Thereby, the pixels predicted in the forward
embedding can partially be restored to their original values resulting in both
better-embedded image quality and a higher embedding rate. Experimental results
have recorded a promising rate-distortion performance of our scheme with a
significant improvement of embedded image quality at higher embedding rates
compared to the popular and state-of-the-art PVO-based RDHschemes.


title:
Acoustic analog to multiple avoided-crossings in two coupled acoustic
  cavities

abstract:
  A cylindrical pipe with closed ends and with a partition in-between exhibits
acoustic modes in the two, thus formed, one-dimensional cavities at certain
frequencies. A partial transmission through the partition leads to interaction
between the two cavities' modes and to multiple avoided crossings between
modes' frequencies as a function of the partition position. This is analogous
to a quantum system that has two multi-level and interacting sub-systems and
thus exhibits multiple avoided crossings. Such an acoustic analog is realized
and studied by measuring the sound transmission as a function of frequency
through a pipe with a partially transmitting and movable partition. An
excellent agreement is obtained between the experimental results and a simple
model based on sound wave transmission and reflection at different interfaces.


title: A note on hospital financing: local financing vs. central financing

abstract:
  This note tries to study how hospital behaviors, with reference to
interhospital collaboration or competition, could be affected by hospital
financing systems. For that this note simulates two scenarios which start with
the following baseline scenario: a State, with a set of hospitals, each with
all types of wards at a basic level. The evolution of this baseline scenario
consists in the evolution of hospitals, that is, in the possibility of
hospitals to make some of their wards excel. The State has a budget, for the
evolution of this baseline scenario, which can be used by two financing
systems: either by a "local financing", i.e., by splitting the budget among the
hospitals so that each hospital is managing its own portion of the budget by
pursuing the individual benefit, or by a "central financing", i.e., by not
splitting the budget among the hospitals so that the State is the sole manager
of the budget, by pursuing the benefit of the whole community. The conclusions
seem to be that: in the local financing system hospitals tend to diversify
their excellences, while in the central financing system the State tends to
create poles of excellence.


title: Paraphrasing Magritte's Observation

abstract:
  Contrast Sensitivity of the human visual system can be explained from certain
low-level vision tasks (like retinal noise and optical blur removal), but not
from others (like chromatic adaptation or pure reconstruction after simple
bottlenecks). This conclusion still holds even under substantial change in
stimulus statistics, as for instance considering cartoon-like images as opposed
to natural images (Li et al. Journal of Vision, 2022, Preprint
arXiv:2103.00481).
  In this note we present a method to generate original cartoon-like images
compatible with the statistical training used in (Li et al., 2022). Following
the classical observation in (Magritte, 1929), the stimuli generated by the
proposed method certainly are not what they represent: Ceci n'est pas une pipe.
The clear distinction between representation (the stimuli generated by the
proposed method) and reality (the actual object) avoids eventual problems for
the use of the generated stimuli in academic, non-profit, publications.


title: An auto-locking ultra-stable laser with sub-hertz linewidth

abstract:
  We report in detail the design process and performance of an auto-locking
ultra-stable laser with sub-hertz linewidth at the first time. The laser
frequency is automatically stabilized to an optical reference cavity with a
home-made controller, which is based on a combination of digital circuit and
analog circuit. The digital circuit is used for diagnosing and manipulating the
state of the ultra-stable laser, and the analog circuit is used for
demodulating the discriminate signal and servo control. A method of searching
the transmission signal in the closed-loop state instead of the open-loop state
is proposed to reduce the locking time and improve the reliable of the
auto-locking ultra-stable laser. The median time of 16.6s is obtained after 157
times of relocking, and the probability of less than 20 s is more than 86%. The
median linewidth of 1.08 Hz is obtained, and the fractional frequency
instability is less than 3.4E-15 at integration time between 0.1 and 100 s. The
performance of this system demonstrates that will be used as an important
subsystem to transfer the optical clock signal.


title:
Non-linear Cherenkov Muon Spectrometer Using Multi-Layer Pressurized
  C3F8 Gas Radiators

abstract:
  A new Cherenkov muon spectrometer using diversely sized C3F8 gas radiators
and nonlinear threshold muon momentum levels was studied using Geant4
simulations. Despite its compact size (<1m3), the performance of the
spectrometer was significantly improved over the earlier design (linear and
uniform) in two aspects, (i) a stable momentum measurement resolution and (ii)
a balanced expected Cherenkov photon yields in all radiators. In addition, CO2
gas radiators are replaced by heavier gas radiators, C3F8, and the sizes of
radiators for high momentum levels (low gas pressure) are enlarged to increase
the Cherenkov photon yields. Specifically, the absolute momentum resolution,
{\sigma}p, for all levels is replaced by the relative momentum resolution,
{\sigma}p/p. To demonstrate the functionality of our proposed Cherenkov muon
spectrometer, we presented the results of classification rates as a function of
muon momentum with various discriminator levels and reconstructed cosmic ray
muon spectrum. When the combination of two or more discriminators are used, the
average classification rate is 90.08%. In addition, our proposed Cherenkov muon
spectrometer successfully reconstructed the cosmic ray muon spectrum and the
results show a good agreement with the actual spectrum within 1{\sigma} in most
momentum levels.


title: Sparse polynomial interpolation and division in soft-linear time

abstract:
  Given a way to evaluate an unknown polynomial with integer coefficients, we
present new algorithms to recover its nonzero coefficients and corresponding
exponents. As an application, we adapt this interpolation algorithm to the
problem of computing the exact quotient of two given polynomials. These methods
are efficient in terms of the bit-length of the sparse representation, that is,
the number of nonzero terms, the size of coefficients, the number of variables,
and the logarithm of the degree. At the core of our results is a new Monte
Carlo randomized algorithm to recover an integer polynomial $f(x)$ given a way
to evaluate $f(\theta) \bmod m$ for any chosen integers $\theta$ and $m$. This
algorithm has nearly-optimal bit complexity, meaning that the total bit-length
of the probes, as well as the computational running time, is softly linear
(ignoring logarithmic factors) in the bit-length of the resulting sparse
polynomial. To our knowledge, this is the first sparse interpolation algorithm
with soft-linear bit complexity in the total output size. For integer
polynomials, the best previously known results have at least a cubic dependency
on the bit-length of the exponents.


title: Estimating Software Reliability Using Size-biased Modelling

abstract:
  Software reliability estimation is one of the most active areas of research
in software testing. Since time between failures (TBF) has often been
challenging to record, software testing data are commonly recorded as
test-case-wise in a discrete set up. We have developed a Bayesian generalised
linear mixed model (GLMM) based on software testing detection data and a
size-biased strategy which not only estimates the software reliability, but
also estimates the total number of bugs present in the software. Our approach
provides a flexible, unified modelling framework and can be adopted to various
real-life situations. We have assessed the performance of our model via
simulation study and found that each of the key parameters could be estimated
with a satisfactory level of accuracy. We have also applied our model to two
empirical software testing data sets. While there can be other fields of study
for application of our model (e.g., hydrocarbon exploration), we anticipate
that our novel modelling approach to estimate software reliability could be
very useful for the users and can potentially be a key tool in the field of
software reliability estimation.


title:
An alternative paradigm of fault diagnosis in dynamic systems:
  orthogonal projection-based methods

abstract:
  In this paper, we propose a new paradigm of fault diagnosis in dynamic
systems as an alternative to the well-established observer-based framework. The
basic idea behind this work is to (i) formulate fault detection and isolation
as projection of measurement signals onto (system) subspaces in Hilbert space,
and (ii) solve the resulting problems by means of projection methods with
orthogonal projection operators and gap metric as major tools. In the new
framework, fault diagnosis issues are uniformly addressed both in the
model-based and data-driven fashions. Moreover, the design and implementation
of the projection-based fault diagnosis systems, from residual generation to
threshold setting, can be unifiedly handled. Thanks to the well-defined
distance metric for projections in Hilbert subspaces, the projection-based
fault diagnosis systems deliver optimal fault detectability. In particular, a
new type of residual-driven thresholds is proposed, which significantly
increases the fault detectability. In this work, various design schemes are
proposed, including a basic projection-based fault detection scheme, a fault
detection scheme for feedback control systems, fault classification as well as
two modified fault detection schemes. As a part of our study, relations to the
existing observer-based fault detection systems are investigated, which
showcases that, with comparable online computations, the proposed
projection-based detection methods offer improved detection performance.


title:
A Search for correlations between turbulence and star formation in
  THINGS galaxies

abstract:
  The spatial range for feedback from star formation varies from molecular
cloud disruption on parsec scales to supershells and disk blowout on kiloparsec
scales. The relative amounts of energy and momentum given to these scales is
important for understanding the termination of star formation in any one region
and the origin of interstellar turbulence and disk stability in galaxies as a
whole. Here we measure for eleven THINGS galaxies the excess kinetic energy,
velocity dispersion and surface density of HI gas associated with regions of
excess star formation, where the excess is determined from the difference
between the observed local value and the azimuthal average. We find small
decreases in the excess kinetic energy and velocity dispersion in regions of
excess star formation rate density, suggesting that most of the feedback energy
does not go into local HI motion. Most likely it disrupts molecular clouds and
dissipates rapidly at high gas density. Some could also be distributed over
larger regions, filling in spaces between the peaks of star formation and
contributing to other energy sources from self-gravity and spiral arm shocks.


title: Disorder-induced linear magnetoresistance in Sr-doped Bi2Se3 thin films

abstract:
  Sr-doped Bi2Se3 thin films was known as a potential candidate of topological
superconductor. The magnetoresistance (MR) of SrxBi2Se3 films with various
doping concentrations x were found to be dominated by weak antilocalization
(WAL) at low magnetic fields, whereas the classical MR, which originally
dominated the MR, was almost completely suppressed. In contrast, the MR of all
samples has been observed to be dominated by linear magnetoresistance (LMR) at
high magnetic fields. The LMR, having the linear dependence on carrier
mobility, can be successfully explained by the Parish-Littlewood model. This
indicates that LMR originates from mobility fluctuation induced by Sr dopant
atoms in doped Bi2Se3 films.


title: Shock Interaction in Plane Symmetry

abstract:
  We consider the problem of interaction of two oncoming shocks in plane
symmetry for a barotropic fluid. We establish a local in time solution after
the point of interaction, thereby determining the state behind the emerged
shocks which originate at the interaction point. The location of these shocks
in space time being unknown the problem constitutes a double free boundary
problem, i.e.~complete data for the problem is only given in a single point,
the interaction point. This article can be viewed as an extension of the
article arXiv:2112.15266 which deals with the reflection of a shock on a wall.


title:
Typography-MNIST (TMNIST): an MNIST-Style Image Dataset to Categorize
  Glyphs and Font-Styles

abstract:
  We present Typography-MNIST (TMNIST), a dataset comprising of 565,292
MNIST-style grayscale images representing 1,812 unique glyphs in varied styles
of 1,355 Google-fonts. The glyph-list contains common characters from over 150
of the modern and historical language scripts with symbol sets, and each
font-style represents varying subsets of the total unique glyphs. The dataset
has been developed as part of the CognitiveType project which aims to develop
eye-tracking tools for real-time mapping of type to cognition and to create
computational tools that allow for the easy design of typefaces with cognitive
properties such as readability. The dataset and scripts to generate MNIST-style
images for glyphs in different font styles are freely available at
https://github.com/aiskunks/CognitiveType.