title:
Understanding Saliency Prediction with Deep Convolutional Neural
  Networks and Psychophysical Models

abstract:
  Convolutional neural networks (CNNs) have achieved great success in natural
image saliency prediction. The primary goal of this study is to investigate the
performance of saliency prediction in CNN with psychophysical synthetic images.
Is it still as decent as natural images in terms of performance? In the
meantime, it can be used to investigate the relationship between CNNs and human
vision, mainly low-level vision functions. Are CNNs, on the other hand, exact
replicas of human visual function? This study used CNNs and Fourier and
Spectral models inspired by low-level vision systems to investigate saliency
prediction on psychophysical synthetic images rather than natural images.
According to our findings, saliency prediction models inspired by Fourier and
Spectral theory outperformed current pre-trained deep neural networks on
psychophysical images. However, psychophysical models were more unstable in
noise than pre-trained deep neural networks. Meanwhile, we suggested that
investigating CNNs with psychophysical methods could benefit visual
neuroscience and artificial neural network studies.


title: Analytic continuation over complex landscapes

abstract:
  In this paper we follow up the study of 'complex complex landscapes,' rugged
landscapes of many complex variables. Unlike real landscapes, the
classification of saddles by index is trivial. Instead, the spectrum of
fluctuations at stationary points determines their topological stability under
analytic continuation of the theory. Topological changes, which occur at
so-called Stokes points, proliferate among saddles with marginal (flat)
directions and are suppressed otherwise. This gives a direct interpretation of
the gap or 'threshold' energy -- which in the real case separates saddles from
minima -- as the level where the spectrum of the hessian matrix of stationary
points develops a gap. This leads to different consequences for the analytic
continuation of real landscapes with different structures: the global minima of
'one step replica-symmetry broken' landscapes lie beyond a threshold, their
hessians are gapped, and are locally protected from Stokes points, whereas
those of 'many step replica-symmetry broken' have gapless hessians and Stokes
points immediately proliferate. A new matrix ensemble is found, playing the
role that GOE plays for real landscapes in determining the topological nature
of saddles.

categories: cond-mat.stat-mech cond-mat.dis-nn math-ph math.MP


title: Edge observables of the Maxwell-Chern-Simons theory

abstract:
  We analyze the Lagrangian and Hamiltonian formulations of the
Maxwell-Chern-Simons theory defined on a manifold with boundary for two
different sets of boundary equations derived from a variational principle. We
pay special attention to the identification of the infinite chains of boundary
constraints and their resolution. We identify edge observables and their
algebra (which corresponds to the well-known $U(1)$ Kac-Moody algebra). Without
performing any gauge fixing, and using the Hodge-Morrey theorem, we solve the
Hamilton equations whenever possible. In order to give explicit solutions, we
consider the particular case in which the fields are defined on a $2$-disk.
Finally, we study the Fock quantization of the system and discuss the quantum
edge observables and states.


title: Skyhook: Towards an Arrow-Native Storage System

abstract:
  With the ever-increasing dataset sizes, several file formats such as Parquet,
ORC, and Avro have been developed to store data efficiently, save the network,
and interconnect bandwidth at the price of additional CPU utilization. However,
with the advent of networks supporting 25-100 Gb/s and storage devices
delivering 1, 000, 000 reqs/sec, the CPU has become the bottleneck trying to
keep up feeding data in and out of these fast devices. The result is that data
access libraries executed on single clients are often CPU-bound and cannot
utilize the scale-out benefits of distributed storage systems. One attractive
solution to this problem is to offload data-reducing processing and filtering
tasks to the storage layer. However, modifying legacy storage systems to
support compute offloading is often tedious and requires an extensive
understanding of the system internals. Previous approaches re-implemented
functionality of data processing frameworks and access libraries for a
particular storage system, a duplication of effort that might have to be
repeated for different storage systems. This paper introduces a new design
paradigm that allows extending programmable object storage systems to embed
existing, widely used data processing frameworks and access libraries into the
storage layer with no modifications. In this approach, data processing
frameworks and access libraries can evolve independently from storage systems
while leveraging distributed storage systems scale-out and availability
properties. We present Skyhook, an example implementation of our design
paradigm using Ceph, Apache Arrow, and Parquet. We provide a brief performance
evaluation of Skyhook and discuss key results.


title: Ejection-accretion connection in NLS1 AGN 1H 1934-063

abstract:
  Accretion and ejection of matter in active galactic nuclei (AGN) are tightly
connected phenomena and represent fundamental mechanisms regulating the growth
of the central supermassive black hole and the evolution of the host galaxy.
However, the exact physical processes involved are not yet fully understood. We
present a high-resolution spectral analysis of a simultaneous \xmm\ and
\nustar\ observation of the narrow line Seyfert 1 (NLS1) AGN 1H 1934-063,
during which the X-ray flux dropped by a factor of $\sim6$ and subsequently
recovered within 140 kiloseconds. By means of the time-resolved and
flux-resolved X-ray spectroscopy, we discover a potentially variable warm
absorber and a relatively stable ultra-fast outflow (UFO,
$v_\mathrm{UFO}\sim-0.075\,c$) with a mild ionization state
($\log(\xi/\mathrm{erg\,cm\,s^{-1})}\sim1.6$). The detected emission lines
(especially a strong and broad feature around 1\,keV) are of unknown origin and
cannot be explained with emission from plasmas in photo- or
collisional-ionization equilibrium. Such emission lines could be well described
by a strongly blueshifted ($z\sim-0.3$) secondary reflection off the base of
the equatorial outflows, which may reveal the link between the reprocessing of
the inner accretion flow photons and the ejection. However, this scenario
although being very promising is only tentative and will be tested with future
observations.


title:
Hybrid Feature- and Similarity-Based Models for Prediction and
  Interpretation using Large-Scale Observational Data

abstract:
  Introduction: Large-scale electronic health record(EHR) datasets often
include simple informative features like patient age and complex data like care
history that are not easily represented as individual features. Such complex
data have the potential to both improve the quality of risk assessment and to
enable a better understanding of causal factors leading to those risks. We
propose a hybrid feature- and similarity-based model for supervised learning
that combines feature and kernel learning approaches to take advantage of rich
but heterogeneous observational data sources to create interpretable models for
prediction and for investigation of causal relationships. Methods: The proposed
hybrid model is fit by convex optimization with a sparsity-inducing penalty on
the kernel portion. Feature and kernel coefficients can be fit sequentially or
simultaneously. We compared our models to solely feature- and similarity-based
approaches using synthetic data and using EHR data from a primary health care
organization to predict risk of loneliness or social isolation. We also present
a new strategy for kernel construction that is suited to high-dimensional
indicator-coded EHR data. Results: The hybrid models had comparable or better
predictive performance than the feature- and kernel-based approaches in both
the synthetic and clinical case studies. The inherent interpretability of the
hybrid model is used to explore client characteristics stratified by kernel
coefficient direction in the clinical case study; we use simple examples to
discuss opportunities and cautions of the two hybrid model forms when causal
interpretations are desired. Conclusion: Hybrid feature- and similarity-based
models provide an opportunity to capture complex, high-dimensional data within
an additive model structure that supports improved prediction and
interpretation relative to simple models and opaque complex models.


title:
PaC-trees: Supporting Parallel and Compressed Purely-Functional
  Collections

abstract:
  Many modern programming languages are shifting toward a functional style for
collection interfaces such as sets, maps, and sequences. Functional interfaces
offer many advantages, including being safe for parallelism and providing
simple and lightweight snapshots. However, existing high-performance functional
interfaces such as PAM, which are based on balanced purely-functional trees,
incur large space overheads for large-scale data analysis due to storing every
element in a separate node in a tree.
  This paper presents PaC-trees, a purely-functional data structure supporting
functional interfaces for sets, maps, and sequences that provides a significant
reduction in space over existing approaches. A PaC-tree is a balanced binary
search tree which blocks the leaves and compresses the blocks using arrays. We
provide novel techniques for compressing and uncompressing the blocks which
yield practical parallel functional algorithms for a broad set of operations on
PaC-trees such as union, intersection, filter, reduction, and range queries
which are both theoretically and practically efficient.
  Using PaC-trees we designed CPAM, a C++ library that implements the full
functionality of PAM, while offering significant extra functionality for
compression. CPAM consistently matches or outperforms PAM on a set of
microbenchmarks on sets, maps, and sequences while using about a quarter of the
space. On applications including inverted indices, 2D range queries, and 1D
interval queries, CPAM is competitive with or faster than PAM, while using
2.1--7.8x less space. For static and streaming graph processing, CPAM offers
1.6x faster batch updates while using 1.3--2.6x less space than the
state-of-the-art graph processing system Aspen.


title: Understanding the compromise between skyline and ranking queries

abstract:
  Skyline and Ranking queries have gained great popularity in the recent years.
These two techniques are crucial for multi-criteria decision support
applications, which are now more popular than ever before. Skyline and Ranking
queries are, however, affected by well-known limitations. In the past recent
years, the database community provided numerous studies in this field with the
aim to overcome the weaknesses of these two approaches. This survey introduces
the reader to Skyline and Ranking queries, explaining the concepts on which
they are based, with the intent to present the compromise between the two
techniques: flexible skylines.


title:
Acacia-Bonsai: A Modern Implementation of Downset-Based LTL
  Realizability

abstract:
  We describe our implementation of downset-manipulating algorithms used to
solve the realizability problem for linear temporal logic (LTL). These
algorithms were introduced by Filiot et al.~in the 2010s and implemented in the
tools Acacia and Acacia+ in C and Python. We identify degrees of freedom in the
original algorithms and provide a complete rewriting of Acacia in C++20
articulated around genericity and leveraging modern techniques for better
performances. These techniques include compile-time specialization of the
algorithms, the use of SIMD registers to store vectors, and several
preprocessing steps, some relying on efficient Binary Decision Diagram (BDD)
libraries. We also explore different data structures to store downsets. The
resulting tool is competitive against comparable modern tools.


title:
Partial H\"{o}lder Regularity for Solutions of a Class of
  Cross-Diffusion Systems with Entropy Structure

abstract:
  In this article we show a $C^{0,\alpha}$-partial regularity result for
solutions of a certain class of cross-diffusion systems with entropy structure.
Under slightly more stringent conditions on the system, we are able to obtain a
$C^{1,\alpha}$-partial regularity result. Amongst others, our results yield the
partial $C^{1,\alpha}$-regularity of weak solutions of the Maxwell-Stefan
system, as well as the partial $C^{1,\alpha}$-regularity of bounded weak
solutions of the Shigesada-Kawasaki-Teramoto model. The classical partial
regularity theory for nonlinear parabolic systems as developed by Giaquinta and
Struwe in the 80s proceeds by Campanato iteration which relies on energy
methods. Our analysis here centers around the insight that, in the Campanato
iteration strategy, we can replace the use of energy estimates by "entropy
dissipation inequalities" and the use of the squared $L^2$-distance to measure
the distance between functions by the use of the "relative entropy". In order
for our strategy to work, it is necessary to regularize the entropy structure
of the cross-diffusion system, thereby introducing a new technical tool, which
we call the "glued entropy".


title: On the expected number of real roots of polynomials and exponential sums

abstract:
  The expected number of real projective roots of orthogonally invariant random
homogeneous real polynomial systems is known to be equal to the square root of
the B\'ezout number. A similar result is known for random multi-homogeneous
systems, invariant through a product of orthogonal groups. In this note, those
results are generalized to certain families of sparse polynomial systems, with
no orthogonal invariance assumed.


title: Dark matter in the NMSSM with small $\lambda$ and $\kappa$

abstract:
  The NMSSM provides an excellent dark matter candidate, usually, the lightest
neutralino ($\tilde{\chi}^0_1$) being the lightest supersymmetric particle
(LSP), in the universe. It is a mixture of the bino, the neutral wino, the
neutral higgsinos, and the singlino. In this work, we investigate the features
of the lightest neutralino by concentrating on a specific region of the NMSSM
parameter space with small values of both $\lambda$ and $\kappa$, taking into
account theoretical and experimental constraints, including relic density,
direct and indirect detection constraints. We have found that the LSP dark
matter is a singlino-dominated neutralino for most of the sample points of the
chosen scenario. Thus, contrary to expectations, the NMSSM is not comparable to
the MSSM in terms of dark matter properties as $\lambda$ and $\kappa$ are very
small. The LSP can also be either higgsino-dominated or bino-dominated in some
parameter space. The most important parameters affecting the mass and
properties of the LSP are $\kappa$, $\lambda/\kappa$, and $\mu_{\rm eff}$.
Further, we notice that $\sigma^{SI}_p \approx 1.02$ $\sigma^{SI}_n$ and
$\sigma^{SD}_p \approx 0.76$ $\sigma^{SD}_n$ for all the surviving sample
points.


title:
Universal AMG Accelerated Embedded Boundary Method Without Small Cell
  Stiffness

abstract:
  We develop a universally applicable embedded boundary finite difference
method, which results in a symmetric positive definite linear system and does
not suffer from small cell stiffness. Our discretization is efficient for the
wave, heat and Poisson's equation with Dirichlet boundary conditions. When the
system needs to be inverted we can use the conjugate gradient method,
accelerated by algebraic multigrid techniques. A series of numerical tests for
the wave, heat and Poisson's equation and applications to shape optimization
problems verify the accuracy, stability, and efficiency of our method. Our fast
computational techniques can be extended to moving boundary problems (e.g.
Stefan problem), to the Navier-Stokes equations, and to the Grad-Shafranov
equations for which problems are posed on domains with complex geometry and
fast simulations are very important.


title:
Yellow Post-Asymptotic-Giant-Branch Stars as Standard Candles. I.
  Calibration of the Luminosity Function in Galactic Globular Clusters

abstract:
  We use results of a survey for low-surface-gravity stars in Galactic (and
LMC) globular clusters to show that "yellow" post-asymptotic-giant-branch
(yPAGB) stars are likely to be excellent extragalactic standard candles,
capable of producing distances to early-type galaxies that are accurate to a
few percent. We show that the mean bolometric magnitude of the 10 known yPAGB
stars in globular clusters is <Mbol> = -3.38 +/- 0.03, a value that is ~0.2 mag
brighter than that predicted from the latest post-horizontal-branch
evolutionary tracks. More importantly, we show that the observed dispersion in
the distribution is only 0.10 mag, i.e., smaller than the scatter for
individual Cepheids. We describe the physics that can produce such a small
dispersion, and show that, if one restricts surveys to the color range 0 <
(B-V)0 < 0.5, then samples of non-variable yPAGB stars can be identified quite
easily with a minimum of contamination. The bright absolute V magnitudes of
these stars (<Mv> = -3.37) make them, by far, the visually brightest objects in
old stellar populations and ideal Population II standard candles for
measurements out to ~10 Mpc with current instrumentation. A Hubble Space
Telescope survey in the halos of galaxies in the M81 and Sculptor groups could
therefore serve as an effective cross-check on both the Cepheid and TRGB
distance scales.


title: Finding Trolls Under Bridges: Preliminary Work on a Motif Detector

abstract:
  Motifs are distinctive recurring elements found in folklore that have
significance as communicative devices in news, literature, press releases, and
propaganda. Motifs concisely imply a large constellation of culturally-relevant
information, and their broad usage suggests their cognitive importance as
touchstones of cultural knowledge, making their detection a worthy step toward
culturally-aware natural language processing tasks. Until now, folklorists and
others interested in motifs have only extracted motifs from narratives
manually. We present a preliminary report on the development of a system for
automatically detecting motifs. We briefly describe an annotation effort to
produce data for training motif detection, which is on-going. We describe our
in-progress architecture in detail, which aims to capture, in part, how people
determine whether or not a motif candidate is being used in a motific way. This
description includes a test of an off-the-shelf metaphor detector as a feature
for motif detection, which achieves a F1 of 0.35 on motifs and a macro-average
F1 of 0.21 across four categories which we assign to motif candidates.


title: A Post Auto-regressive GAN Vocoder Focused on Spectrum Fracture

abstract:
  Generative adversarial networks (GANs) have been indicated their superiority
in usage of the real-time speech synthesis. Nevertheless, most of them make use
of deep convolutional layers as their backbone, which may cause the absence of
previous signal information. However, the generation of speech signals
invariably require preceding waveform samples in its reconstruction, as the
lack of this can lead to artifacts in generated speech. To address this
conflict, in this paper, we propose an improved model: a post auto-regressive
(AR) GAN vocoder with a self-attention layer, which merging self-attention in
an AR loop. It will not participate in inference, but can assist the generator
to learn temporal dependencies within frames in training. Furthermore, an
ablation study was done to confirm the contribution of each part. Systematic
experiments show that our model leads to a consistent improvement on both
objective and subjective evaluation performance.


title: Clusters determine local fluctuations of random walks on graphs

abstract:
  The evolution of many stochastic systems is accurately described by random
walks on graphs. We here explore the close connection between local
steady-state fluctuations of random walks and the global structure of the
underlying graph. Fluctuations are quantified by the counting statistics for
traversals of the random walk along graph edges. The variance-to-mean ratio of
the counting statistics is typically lowered if two end vertices of an edge
belong to different clusters in the sense of spectral clustering. In
particular, we relate the fluctuations to the algebraic connectivity and the
Fiedler vector of the graph. Building on these results we suggest a centrality
score based on fluctuations of random walks. Our findings imply that local
fluctuations of continuous-time Markov processes on discrete state space depend
strongly on the global topology of the underlying graph in addition to the
specific transition rates.


title: The nuclear shell model towards the drip lines

abstract:
  Applications of configuration mixing methods for nuclei near the proton and
neutron drip lines are discussed. A short review of magic numbers is presented.
Prospects for advances in the regions of four new "outposts" are highlighted:
$^{28}$O, $^{42}$Si, $^{60}$Ca and $^{78}$Ni. Topics include: shell gaps,
single-particle properties, islands-of-inversion, collectivity, neutron decay,
neutron halos, two-proton decay, effective charge, and quenching in knockout
reactions.


title:
Collective motion of active particles exhibiting non-reciprocal
  orientational interactions

abstract:
  We present a Brownian dynamics study of a 2d bath of active particles
interacting among each other through usual steric interactions and,
additionally, via non-reciprocal avoidant orientational interactions. As
expected, in most cases such interactions disrupt the motility-induced particle
clustering in active baths. Surprisingly, however, we find that the active
particles can self-organize into collectively moving flocks if the range of
non-reciprocal interactions is close to that of steric interactions. We observe
that the flocking motion can manifest itself through a variety of structural
forms, spanning from single dense bands to multiple moderately-dense stripes.
The flocking order parameter is found to be only weakly dependent on the
underlying flock structure.


title: Unique Optima of the Delsarte Linear Program

abstract:
  The Delsarte linear program is used to bound the size of codes given their
block length $n$ and minimal distance $d$ by taking a linear relaxation from
codes to quasicodes. We study for which values of $(n,d)$ this linear program
has a unique optimum: while we show that it does not always have a unique
optimum, we prove that it does if $d>n/2$ or if $d \leq 2$. Introducing the
Krawtchouk decomposition of a quasicode, we prove there exist optima to the
$(n,2e)$ and $(n-1,2e-1)$ linear programs that have essentially identical
Krawtchouk decompositions, revealing a parity phenomenon among the Delsarte
linear programs. We generalize the notion of extending and puncturing codes to
quasicodes, from which we see that this parity relationship is given by
extending/puncturing. We further characterize these pairs of optima, in
particular demonstrating that they exhibit a symmetry property, effectively
halving the number of decision variables.


title:
Is there a background population of high-albedo objects in
  geosynchronous orbits around Earth?

abstract:
  Old, digitized astronomical images taken before the human spacefaring age
offer a unique view of the sky devoid of known artificial satellites. In this
paper, we have carried out the first optical searches ever for non-terrestrial
artifacts near the Earth following the method proposed in Villarroel et al.
(2022). We use images contained in the First Palomar Sky Survey to search for
simultaneous (during a plate exposure time) transients that in addition to
being point-like, are aligned. We provide a shortlist of the most promising
candidates of aligned transients, that must be examined with the help of a
microscope to separate celestial sources from plate defects with coincidentally
star-like brightness profiles. We further explore one possible, but not unique,
interpretation in terms of fast reflections off high-albedo objects in
geosynchronous orbits around Earth. If a future study rules out each multiple
transient candidate, the estimated surface density becomes an upper limit of
$<10^{-9}$ objects km$^{-2}$ non-terrestrial artifacts in geosynchronous orbits
around Earth. Finally, we conclude that observations and analysis of multiple,
simultaneously appearing and vanishing light sources on the sky merit serious
further attention, regardless of their origin.


title: ASQA: Factoid Questions Meet Long-Form Answers

abstract:
  An abundance of datasets and availability of reliable evaluation metrics have
resulted in strong progress in factoid question answering (QA). This progress,
however, does not easily transfer to the task of long-form QA, where the goal
is to answer questions that require in-depth explanations. The hurdles include
(i) a lack of high-quality data, and (ii) the absence of a well-defined notion
of the answer's quality. In this work, we address these problems by (i)
releasing a novel dataset and a task that we call ASQA (Answer Summaries for
Questions which are Ambiguous); and (ii) proposing a reliable metric for
measuring performance on ASQA. Our task focuses on factoid questions that are
ambiguous, that is, have different correct answers depending on interpretation.
Answers to ambiguous questions should synthesize factual information from
multiple sources into a long-form summary that resolves the ambiguity. In
contrast to existing long-form QA tasks (such as ELI5), ASQA admits a clear
notion of correctness: a user faced with a good summary should be able to
answer different interpretations of the original ambiguous question. We use
this notion of correctness to define an automated metric of performance for
ASQA. Our analysis demonstrates an agreement between this metric and human
judgments, and reveals a considerable gap between human performance and strong
baselines.


title:
Hollow-core fiber Characterization with Correlation-Optical Time Domain
  Reflectometry

abstract:
  Using a Correlation-OTDR, we characterized the temperature-induced group
delay variations of two nested antiresonant nodeless hollow core fibers. The
temperature sensitivity of both is substantially less than for SSMF with some
dependency on coating type.


title:
Development of an automated millifluidic platform and data-analysis
  pipeline for rapid electrochemical corrosion measurements: a pH study on
  Zn-Ni

abstract:
  We describe the development of a millifluidic based scanning droplet cell
platform for rapid and automated corrosion. This system allows for measurement
of corrosion properties (e.g., open circuit potential, corrosion current
through Tafel and linear polarization resistance measurements, and cyclic
voltammograms) on a localized section of a planar sample. Our system is highly
automated and flexible, allowing for scripted changing and mixing of solutions
and point-to-point motion on the sample. We have also created an automated data
analysis pipeline. Here we demonstrate this tool by corroding a plate of
electroplated Zn$_{85}$Ni$_{15}$ alloy over a range of pH values and correlate
our results with XPS measurements and literature.


title: A Survey of Impedance Measurement Methods in Power Electronics

abstract:
  Impedance is one of the vital parameters that provides useful information for
many power electronics related applications. A lot of impedance measurement
methods in power electronics have been reported. However, a comprehensive
investigation among these methods in terms of their characteristics,
advantages, and limitations has not been found in the literature. In order to
bridge this gap, a survey of the impedance measurement methods is conducted in
this paper. These methods are introduced, discussed, and then classified into
different categories depending on the measurement modes, principles, and
instruments. Moreover, recommendations for the future research on the impedance
measurement are also presented.


title: Nuclear phase retrieval spectroscopy using resonant x-ray scattering

abstract:
  Light-matter interaction is exploited in spectroscopic techniques to access
information about molecular, atomic or nuclear constituents of the sample of
interest. While scattered light carries both amplitude and phase information of
the electromagnetic field, most of the time the latter is lost in intensity
measurements. However, often the phase information is paramount to reconstruct
the desired information of the target, as it is well known from coherent x-ray
imaging. Here we introduce a new phase retrieval algorithm which allows us to
reconstruct the field phase information from two-dimensional time- and
energy-resolved spectra. We apply this method to the particular case of x-ray
scattering off M\"ossbauer nuclei at a synchrotron radiation source. Knowledge
of the phase allows also for an excellent reconstruction of the energy spectra
from experimental data, which could not be achieved with this resolution
otherwise. Our approach provides an efficient novel data analysis tool which
will benefit x-ray quantum optics and M\"ossbauer spectroscopy with synchrotron
radiation alike.


title:
Slope stability predictions on spatially variable random fields using
  machine learning surrogate models

abstract:
  Random field Monte Carlo (MC) reliability analysis is a robust stochastic
method to determine the probability of failure. This method, however, requires
a large number of numerical simulations demanding high computational costs.
This paper explores the efficiency of different machine learning (ML)
algorithms used as surrogate models trained on a limited number of random field
slope stability simulations in predicting the results of large datasets. The MC
data in this paper require only the examination of failure or non-failure,
circumventing the time-consuming calculation of factors of safety. An extensive
dataset is generated, consisting of 120,000 finite difference MC slope
stability simulations incorporating different levels of soil heterogeneity and
anisotropy. The Bagging Ensemble, Random Forest and Support Vector classifiers
are found to be the superior models for this problem amongst 9 different models
and ensemble classifiers. Trained only on 0.47% of data (500 samples), the ML
model can classify the entire 120,000 samples with an accuracy of %85 and AUC
score of %91. The performance of ML methods in classifying the random field
slope stability results generally reduces with higher anisotropy and
heterogeneity of soil. The ML assisted MC reliability analysis proves a robust
stochastic method where errors in the predicted probability of failure using %5
of MC data is only %0.46 in average. The approach reduced the computational
time from 306 days to less than 6 hours.


title:
Highly efficient reliability analysis of anisotropic heterogeneous
  slopes: Machine Learning aided Monte Carlo method

abstract:
  Machine Learning (ML) algorithms are increasingly used as surrogate models to
increase the efficiency of stochastic reliability analyses in geotechnical
engineering. This paper presents a highly efficient ML aided reliability
technique that is able to accurately predict the results of a Monte Carlo (MC)
reliability study, and yet performs 500 times faster. A complete MC reliability
analysis on anisotropic heterogeneous slopes consisting of 120,000 simulated
samples is conducted in parallel to the proposed ML aided stochastic technique.
Comparing the results of the complete MC study and the proposed ML aided
technique, the expected errors of the proposed method are realistically
examined. Circumventing the time-consuming computation of factors of safety for
the training datasets, the proposed technique is more efficient than previous
methods. Different ML models, including Random Forest (RF), Support Vector
Machine (SVM) and Artificial Neural Networks (ANN) are presented, optimised and
compared. The effects of the size and type of training and testing datasets are
discussed. The expected errors of the ML predicted probability of failure are
characterised by different levels of soil heterogeneity and anisotropy. Using
only 1% of MC samples to train ML surrogate models, the proposed technique can
accurately predict the probability of failure with mean errors limited to 0.7%.
The proposed technique reduces the computational time required for our study
from 306 days to only 14 hours, providing 500 times higher efficiency.


title: Specifying Prior Distributions in Reliability Applications

abstract:
  Especially when facing reliability data with limited information (e.g., a
small number of failures), there are strong motivations for using Bayesian
inference methods. These include the option to use information from
physics-of-failure or previous experience with a failure mode in a particular
material to specify an informative prior distribution. Another advantage is the
ability to make statistical inferences without having to rely on specious (when
the number of failures is small) asymptotic theory needed to justify
non-Bayesian methods. Users of non-Bayesian methods are faced with multiple
methods of constructing uncertainty intervals (Wald, likelihood, and various
bootstrap methods) that can give substantially different answers when there is
little information in the data. For Bayesian inference, there is only one
method -- but it is necessary to provide a prior distribution to fully specify
the model. Much work has been done to find default or objective prior
distributions that will provide inference methods with good (and in some cases
exact) frequentist coverage properties. This paper reviews some of this work
and provides, evaluates, and illustrates principled extensions and adaptations
of these methods to the practical realities of reliability data (e.g.,
non-trivial censoring).


title:
Towards a Predictive HFB+QRPA Framework for Deformed Nuclei: Selected
  Tools and Technique

abstract:
  Reliable predictions of the static and dynamic properties of a nucleus
require a fully microscopic description of both ground and excited states of
this complicated many-body quantum system. Predictive calculations are key to
understanding such systems and are important ingredients for simulating stellar
environments and for enabling a variety of contemporary nuclear applications.
Challenges that theory has to address include accounting for nuclear
deformation and the ability to describe medium-mass and heavy nuclei. Here, we
perform a study of nuclear states in an Hartree-Fock-Bogoliubov (HFB) and
Quasiparticle Random Phase Approximation (QRPA) framework that utilizes an
axially-symmetric deformed basis. We present some useful techniques for testing
the consistency of such calculations and for interpreting the results.


title: The Dirac-Goodman-Pollack Conjecture

abstract:
  In one of their seminal articles on allowable sequences, Goodman and Pollack
gave combinatorial generalizations for three problems in discrete geometry, one
of which being the Dirac conjecture. According to this conjecture, any set of
$n$ noncollinear points in the plane has a point incident to at least $c n$
connecting lines determined by the set. The notion of allowable sequences of
permutations provides a natural combinatorial setting for analyzing these
problems. Within this formalism, the conjectured generalization reads as
follows: \emph{Any nontrivial allowable $n$-sequence $\Sigma$ has a local
sequence $\Lambda_i$ whose period is at least $c n$.} The conjecture is
confirmed here with a concrete bound. Several related problems are discussed.


title:
The contribution of non-central radio galaxies to AGN feedback in rich
  galaxy clusters

abstract:
  We present a combined radio/X-ray study of six massive galaxy clusters, aimed
at determining the potential for heating of the intra-cluster medium (ICM) by
non-central radio galaxies. Since X-ray cavities associated with the radio
lobes of non-central galaxies are generally not detectable, we use Giant
Metrewave Radio Telescope 610~MHz observations to identify jet sources and
estimate their size, and Chandra data to estimate the pressure of the
surrounding ICM. In the radio, we detect 4.5% of galaxies above the
spectroscopic survey limit (M*K+2.0) of the Arizona cluster redshift survey
(ACReS) which covers five of our six clusters. Approximately one tenth of these
are extended radio sources. Using star formation (SF) rates determined from
mid-infrared data, we estimate the expected contribution to radio luminosity
from the stellar population of each galaxy, and find that most of the
unresolved or poorly-resolved radio sources are likely star formation
dominated. The relatively low frequency and good spatial resolution of our
radio data allows us to trace star formation emission down to galaxies of
stellar mass ~10^9.5 Msol. We estimate the enthalpy of the (AGN dominated)
jet/lobe and tailed sources, and place limits on the energy available from
unresolved radio jets. We find jet powers in the range ~10^43-10^46 erg/s,
comparable to those of brightest cluster galaxies. Our results suggest that
while cluster-central sources are the dominant factor balancing ICM cooling
over the long term, non-central sources may have a significant impact, and that
further investigation is possible and warranted.


title:
Temperature effects on the structure and mechanical properties of vapor
  deposited a-SiO2

abstract:
  Amorphous silica (a-SiO2) exhibits unique thermo-mechanical behaviors that
set it apart from other glasses. However, there is still limited understanding
of how this mechanical behavior is related to the atomic structure and to the
preparation conditions of a-SiO2. Here, we used electron beam (e-beam) physical
vapor deposition (PVD) to prepare a series of a-SiO2 films grown at different
substrate temperatures and then combined molecular simulations with Positronium
Annihilation Lifetime Spectroscopy and nanoindentation experiments to establish
relations among processing, structure, and mechanical response of the films.
Specifically, we found that increase in the growth temperature leads to
increase in the elastic moduli and hardness of the films. The relative porosity
in the films also increases while the a-SiO2 network itself becomes denser,
resulting in an overall increase in density despite increased porosity. In
addition, we found that the a-SiO2 films exhibit the same anomalous temperature
dependence of elastic modulus as bulk a-SiO2. However, the rate of increase in
the elastic modulus with the measurement temperature was found to depend on the
density of the a-SiO2 network and therefore on the growth temperature. Our
findings provide new insights into the influence of the atomic network
structure on the anomalous thermomechanical behavior of a-SiO2 and in turn
guidance to control the mechanical properties of a-SiO2 films.


title:
A Tutorial on Solution Properties of State Space Models of Dynamical
  Systems

abstract:
  The starting point of analysis of state space models is investigating
existence, uniqueness and solution properties such as the semigroup property,
and various formulas for the solutions. Several concepts such as the state
transition matrix, the matrix exponential, the variations of constants formula
(the Cauchy formula), the Peano-Baker series, and the Picard iteration are used
to characterize solutions. In this note, a tutorial treatment is given where
all of these concepts are shown to be various manifestations of a single
abstract method, namely solving equations using an operator Neumann series
involving the Volterra operator of forward integration. The matrix exponential,
the Peano-Baker series, the Picard iteration, and the Cauchy formula can be
"discovered" naturally from this Neumann series. The convergence of the series
and iterations is a consequence of the key property of asymptotic nilpotence of
the Volterra operator. This property is an asymptotic version of the nilpotence
property of a strictly-lower-triangular matrix.


title:
AGQA 2.0: An Updated Benchmark for Compositional Spatio-Temporal
  Reasoning

abstract:
  Prior benchmarks have analyzed models' answers to questions about videos in
order to measure visual compositional reasoning. Action Genome Question
Answering (AGQA) is one such benchmark. AGQA provides a training/test split
with balanced answer distributions to reduce the effect of linguistic biases.
However, some biases remain in several AGQA categories. We introduce AGQA 2.0,
a version of this benchmark with several improvements, most namely a stricter
balancing procedure. We then report results on the updated benchmark for all
experiments.


title:
Optimal Membership Inference Bounds for Adaptive Composition of Sampled
  Gaussian Mechanisms

abstract:
  Given a trained model and a data sample, membership-inference (MI) attacks
predict whether the sample was in the model's training set. A common
countermeasure against MI attacks is to utilize differential privacy (DP)
during model training to mask the presence of individual examples. While this
use of DP is a principled approach to limit the efficacy of MI attacks, there
is a gap between the bounds provided by DP and the empirical performance of MI
attacks. In this paper, we derive bounds for the \textit{advantage} of an
adversary mounting a MI attack, and demonstrate tightness for the widely-used
Gaussian mechanism. We further show bounds on the \textit{confidence} of MI
attacks. Our bounds are much stronger than those obtained by DP analysis. For
example, analyzing a setting of DP-SGD with $\epsilon=4$ would obtain an upper
bound on the advantage of $\approx0.36$ based on our analyses, while getting
bound of $\approx 0.97$ using the analysis of previous work that convert
$\epsilon$ to membership inference bounds.
  Finally, using our analysis, we provide MI metrics for models trained on
CIFAR10 dataset. To the best of our knowledge, our analysis provides the
state-of-the-art membership inference bounds for the privacy.


title:
Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From
  Learned Pairwise Affinity

abstract:
  Open-world instance segmentation is the task of grouping pixels into object
instances without any pre-determined taxonomy. This is challenging, as
state-of-the-art methods rely on explicit class semantics obtained from large
labeled datasets, and out-of-domain evaluation performance drops significantly.
Here we propose a novel approach for mask proposals, Generic Grouping Networks
(GGNs), constructed without semantic supervision. Our approach combines a local
measure of pixel affinity with instance-level mask supervision, producing a
training regimen designed to make the model as generic as the data diversity
allows. We introduce a method for predicting Pairwise Affinities (PA), a
learned local relationship between pairs of pixels. PA generalizes very well to
unseen categories. From PA we construct a large set of pseudo-ground-truth
instance masks; combined with human-annotated instance masks we train GGNs and
significantly outperform the SOTA on open-world instance segmentation on
various benchmarks including COCO, LVIS, ADE20K, and UVO. Code is available on
project website: https://sites.google.com/view/generic-grouping/.


title: SRMD: Sparse Random Mode Decomposition

abstract:
  Signal decomposition and multiscale signal analysis provide many useful tools
for time-frequency analysis. We proposed a random feature method for analyzing
time-series data by constructing a sparse approximation to the spectrogram. The
randomization is both in the time window locations and the frequency sampling,
which lowers the overall sampling and computational cost. The sparsification of
the spectrogram leads to a sharp separation between time-frequency clusters
which makes it easier to identify intrinsic modes, and thus leads to a new
data-driven mode decomposition. The applications include signal representation,
outlier removal, and mode decomposition. On the benchmark tests, we show that
our approach outperforms other state-of-the-art decomposition methods.


title:
Prediction of motor insurance claims occurrence as an imbalanced machine
  learning problem

abstract:
  The insurance industry, with its large datasets, is a natural place to use
big data solutions. However it must be stressed, that significant number of
applications for machine learning in insurance industry, like fraud detection
or claim prediction, deals with the problem of machine learning on an
imbalanced data set. This is due to the fact that frauds or claims are rare
events when compared with the entire population of drivers. The problem of
imbalanced learning is often hard to overcome. Therefore, the main goal of this
work is to present and apply various methods of dealing with an imbalanced
dataset in the context of claim occurrence prediction in car insurance. In
addition, the above techniques are used to compare the results of machine
learning algorithms in the context of claim occurrence prediction in car
insurance. Our study covers the following techniques: logistic-regression,
decision tree, random forest, xgBoost, feed-forward network. The problem is the
classification one.


title: Generalized Integrals and Solvability

abstract:
  Based on previous work we consturct an equation (Lagrange equation) and
relate it with a system of generalized integrals and differential equations in
such a way to provide useful evaluations and connections between them.


title: Topological approach to diagonalization algorithms

abstract:
  In this paper we prove that there exists an asymptotical diagonalization
algorithm for a class of sparse Hermitian (or real symmetric) matrices if and
only if the matrices become Hessenberg matrices after some permutation of rows
and columns. The proof is based on Morse theory, Roberts' theorem on
indifference graphs, toric topology, and computer-based homological
calculations.


title: Analysing and visualising bike sharing demand with outliers

abstract:
  Bike-sharing is a popular component of sustainable urban mobility. It
requires anticipatory planning, e.g. of terminal locations and inventory, to
balance expected demand and capacity. However, external factors such as extreme
weather or glitches in public transport, can cause demand to deviate from
baseline levels. Identifying such outliers keeps historic data reliable and
improves forecasts. In this paper we show how outliers can be identified by
clustering terminals and applying a functional depth analysis. We apply our
analysis techniques to the Washington D.C. Capital Bikeshare data set as the
running example throughout the paper, but our methodology is general by design.
Furthermore, we offer an array of meaningful visualisations to communicate
findings and highlight patterns in demand. Last but not least, we formulate
managerial recommendations on how to use both the demand forecast and the
identified outliers in the bike-sharing planning process.


title:
Liuer Mihou: A Practical Framework for Generating and Evaluating
  Grey-box Adversarial Attacks against NIDS

abstract:
  Due to its high expressiveness and speed, Deep Learning (DL) has become an
increasingly popular choice as the detection algorithm for Network-based
Intrusion Detection Systems (NIDSes). Unfortunately, DL algorithms are
vulnerable to adversarial examples that inject imperceptible modifications to
the input and cause the DL algorithm to misclassify the input. Existing
adversarial attacks in the NIDS domain often manipulate the traffic features
directly, which hold no practical significance because traffic features cannot
be replayed in a real network. It remains a research challenge to generate
practical and evasive adversarial attacks.
  This paper presents the Liuer Mihou attack that generates practical and
replayable adversarial network packets that can bypass anomaly-based NIDS
deployed in the Internet of Things (IoT) networks. The core idea behind Liuer
Mihou is to exploit adversarial transferability and generate adversarial
packets on a surrogate NIDS constrained by predefined mutation operations to
ensure practicality. We objectively analyse the evasiveness of Liuer Mihou
against four ML-based algorithms (LOF, OCSVM, RRCF, and SOM) and the
state-of-the-art NIDS, Kitsune. From the results of our experiment, we gain
valuable insights into necessary conditions on the adversarial transferability
of anomaly detection algorithms. Going beyond a theoretical setting, we replay
the adversarial attack in a real IoT testbed to examine the practicality of
Liuer Mihou. Furthermore, we demonstrate that existing feature-level
adversarial defence cannot defend against Liuer Mihou and constructively
criticise the limitations of feature-level adversarial defences.


title: DT2CAM: A Decision Tree to Content Addressable Memory Framework

abstract:
  Decision trees are considered one of the most powerful tools for data
classification. Accelerating the decision tree search is crucial for
on-the-edge applications that have limited power and latency budget. In this
paper, we propose a Content Addressable Memory (CAM) Compiler for Decision Tree
(DT) inference acceleration. We propose a novel "adaptive-precision" scheme
that results in a compact implementation and enables an efficient bijective
mapping to Ternary Content Addressable Memories while maintaining high
inference accuracies. In addition, a Resistive-CAM (ReCAM) functional
synthesizer is developed for mapping the decision tree to the ReCAM and
performing functional simulations for energy, latency, and accuracy
evaluations. We study the decision tree accuracy under hardware non-idealities
including device defects, manufacturing variability, and input encoding noise.
We test our framework on various DT datasets including \textit{Give Me Some
Credit}, \textit{Titanic}, and \textit{COVID-19}. Our results reveal up to
{42.4\%} energy savings and up to 17.8x better energy-delay-area product
compared to the state-of-art hardware accelerators, and up to 333 million
decisions per sec for the pipelined implementation.


title:
Integrating Distributed Energy Resources: Optimal Prosumer Decisions and
  Impacts of Net Metering Tariffs

abstract:
  The rapid growth of the behind-the-meter (BTM) distributed generation has led
to initiatives to reform the net energy metering (NEM) policies to address
pressing concerns of rising electricity bills, fairness of cost allocation, and
the long-term growth of distributed energy resources. This article presents an
analytical framework for the optimal prosumer consumption decision using an
inclusive NEM X tariff model that covers existing and proposed NEM tariff
designs. The structure of the optimal consumption policy lends itself to near
closed-form optimal solutions suitable for practical energy management systems
that are responsive to stochastic BTM generation and dynamic pricing. The short
and long-run performance of NEM and feed-in tariffs (FiT) are considered under
a sequential rate-setting decision process. Also presented are numerical
results that characterize social welfare distributions, cross-subsidies, and
long-run solar adoption performance for selected NEM policy designs.


title:
A bifurcation problem for a one-dimensional p-Laplace elliptic problem
  with non-odd absorption

abstract:
  In this paper we study the existence of solutions of a one-dimensional
eigenvalue problem
  $-\left(|\phi_x|^{p-2}\phi_x\right)_x=\lambda
\left(|\phi|^{q-2}\phi-f(\phi)\right)$ such that $\phi(0)=\phi(1)=0$, where
$p,q>1$, $\lambda$ is a positive real parameter and $f$ is a continuous (not
necessarily odd) function. Our goal is to give a complete description of
solutions of this problem. We completely characterize the set of solutions of
this problem, which may be uncountable. For $1<p\neq 2$, the existing results
treat only the case when $f$ is either odd and a power (see \cite{TAYA}) or
when $p=q$ (\cite{Guedda-Veron}). Our method of proof rely on a careful
analysis of the phase diagram associated with this equation, refining the
regularity results of \cite{otani} and characterizing the exact points where we
may have $C^2$ regularity of solutions including some points $\chi\in (0,1)$
for which $\phi_x(\chi)=0$.


title:
AdaTest:Reinforcement Learning and Adaptive Sampling for On-chip
  Hardware Trojan Detection

abstract:
  This paper proposes AdaTest, a novel adaptive test pattern generation
framework for efficient and reliable Hardware Trojan (HT) detection. HT is a
backdoor attack that tampers with the design of victim integrated circuits
(ICs). AdaTest improves the existing HT detection techniques in terms of
scalability and accuracy of detecting smaller Trojans in the presence of noise
and variations. To achieve high trigger coverage, AdaTest leverages
Reinforcement Learning (RL) to produce a diverse set of test inputs.
Particularly, we progressively generate test vectors with high reward values in
an iterative manner. In each iteration, the test set is evaluated and
adaptively expanded as needed. Furthermore, AdaTest integrates adaptive
sampling to prioritize test samples that provide more information for HT
detection, thus reducing the number of samples while improving the sample
quality for faster exploration. We develop AdaTest with a Software/Hardware
co-design principle and provide an optimized on-chip architecture solution.
AdaTest's architecture minimizes the hardware overhead in two ways:(i)
Deploying circuit emulation on programmable hardware to accelerate reward
evaluation of the test input; (ii) Pipelining each computation stage in AdaTest
by automatically constructing auxiliary circuit for test input generation,
reward evaluation, and adaptive sampling. We evaluate AdaTest's performance on
various HT benchmarks and compare it with two prior works that use logic
testing for HT detection. Experimental results show that AdaTest engenders up
to two orders of test generation speedup and two orders of test set size
reduction compared to the prior works while achieving the same level or higher
Trojan detection rate.


title:
Using quasar and gamma-ray burst measurements to constrain cosmological
  dark energy models

abstract:
  Observational evidence for the accelerated expansion of the universe requires
dark energy for its explanation if general relativity is an accurate model of
gravity. However, dark energy is a mysterious quantity and we do not know much
about its nature so understanding dark energy is an exciting scientific
challenge. Cosmological dark energy models are fairly well tested in the low
and high redshift parts of the universe. The highest of the low redshift,
$z\sim2.3$, region is probed by baryon acoustic oscillation (BAO) measurements
and the only high redshift probe is the cosmic microwave background anisotropy
which probes the $z\sim1100$ part of redshift space. In the intermediate
redshift range $2.3 < z < 1100$ there are only a handful of observational
probes and cosmological models are poorly tested in this region.
  In this thesis we constrain three pairs of general relativistic cosmological
dark energy models using observational data which reach beyond the current BAO
limit. We use quasar X-ray and UV flux measurements, the current version of
these data span $0.009 \leq z \leq 7.5413$. We have discovered that most of
these data cannot be standardized using the proposed method. However, the lower
redshift part, $z \lesssim 1.5-1.7$, of these data are standardizable and can
be used to derive lower-$z$ cosmological constraints. Another data set we use
are gamma-ray burst measurements which span $0.3399 \leq z \leq 8.2$.
Cosmological constraints derived from these data are significantly weaker than,
but consistent with, those obtained from better-established cosmological
probes. We also study and standardize 78 reverberation-measured Mg II time-lag
quasars in the redshift range $0.0033 \leq z \leq 1.89$ by using their
radius-luminosity relation. We also study 118 reverberation-measured H$\beta$
time-lag quasars which span $0.0023 \leq z \leq 0.89$.


title:
Do you know "saudade"? The importance of a cultural and language-based
  emotion approach for HCI

abstract:
  Today most technologies and interfaces are designed to be global. We argue
that if emotional aspects are incorporated during the design phase of
technologies and interfaces - or in technologies for recognizing users'
emotions such as affective computing - culture and language should be taken as
relevant criteria.


title:
Baseline Computation for Attribution Methods Based on Interpolated
  Inputs

abstract:
  We discuss a way to find a well behaved baseline for attribution methods that
work by feeding a neural network with a sequence of interpolated inputs between
two given inputs. Then, we test it with our novel Riemann-Stieltjes Integrated
Gradient-weighted Class Activation Mapping (RSI-Grad-CAM) attribution method.


title: Lefschetz theorems in flat cohomology and applications

abstract:
  We prove a version of the Lefschetz hyperplane theorem for fppf cohomology
with coefficients in any finite flat commutative group scheme over the ground
field. As consequences, we establish new Lefschetz results for the Picard
scheme. We then use Godeaux-Serre varieties to give a number of examples of
pathological behavior of families in positive characteristic.


title:
On the dynamics of credit history and social interaction features, and
  their impact on creditworthiness assessment performance

abstract:
  For more than a half-century, credit risk management has used credit scoring
models in each of its well-defined stages to manage credit risk. Application
scoring is used to decide whether to grant a credit or not, while behavioral
scoring is used mainly for portfolio management and to take preventive actions
in case of default signals. In both cases, network data has recently been shown
to be valuable to increase the predictive power of these models, especially
when the borrower's historical data is scarce or not available. This study aims
to understand the creditworthiness assessment performance dynamics and how it
is influenced by the credit history, repayment behavior, and social network
features. To accomplish this, we introduced a machine learning classification
framework to analyze 97.000 individuals and companies from the moment they
obtained their first loan to 12 months afterward. Our novel and massive dataset
allow us to characterize each borrower according to their credit behavior, and
social and economic relationships. Our research shows that borrowers' history
increases performance at a decreasing rate during the first six months and then
stabilizes. The most notable effect on perfomance of social networks features
occurs at loan application; in personal scoring, this effect prevails a few
months, while in business scoring adds value throughout the study period. These
findings are of great value to improve credit risk management and optimize the
use of traditional information and alternative data sources.


title: Generalized Composition via Nerves: Models and Algebra

abstract:
  The well-known conditions for a simplicial set to be the nerve of a small
category generalize with respect to two parameters: the dimension n of the
things which compose, and the position i of the thing which is the result of
the composition. In the nerve of a small category, the dimension of the things
which compose (maps) is n=1. Compositions are 2-simplices (commutative
triangles) in which the position of the composite is the 1-simplex opposite
vertex i=1. These conditions generalize to all n>1 and i in {0 , ... , n+1}. We
call such a simplicial set an (n,i)-composer. This paper explores two aspects
of composers: models and the algebra of such generalized composition.
  Models: we develop a family of set-based models for composers analogous to
sets-and-functions models for ordinary categories. The key to obtaining
set-based models of (n,i)-composers is to observe that in the nerve of a
category of sets-and-functions, a 1-simplex is a binary relation with certain
properties on its two faces, that is, its codomain and domain. In a model of an
(n,i)-composer, each k-simplex is a (k+1)-ary relation on its faces having
certain properties, with special attention to k=n (things which compose) and
k=n+1 (a composition of things). The development of these models is guided by
requiring: (i) the usual sets-and-functions model, i.e. when n=1 and i=1,
belongs to the family; (ii) the things which compose have properties which
resemble and generalize properties possessed by ordinary functions.
  Algebra: we explore some notions which are inspired by the algebra of
ordinary composition. These include "comma-composers" generalizing comma
categories, a composer structure for function complexes, representables, and a
generalization of universal map. In its current form, this part of the paper is
provisional in content and organization.


title: Center-of-Mass Corrections in Associated Particle Imaging

abstract:
  Associated Particle Imaging utilizes the inelastic scattering of neutrons
produced in deuterium-tritium fusion reactions to obtain 3D isotopic
distributions within an object. The locations of the inelastic scattering
centers are calculated by measuring the arrival time and the position of the
associated particle, the alpha particle in the fusion reaction, and the arrival
time of the prompt gamma created in the scattering event. While the neutron and
its associated particle move in opposite directions in the center-of-mass (COM)
system, in the laboratory system the angle is slightly less than 180 degrees,
and the COM movement must be taken into account in the reconstruction of the
scattering location. Furthermore, due to energy loss of ions in the target, the
fusion reactions are produced by ions of different energies, and thus the COM
velocity varies, resulting in an uncertainty in the reconstructed positions. In
this paper, we analyze the COM corrections to this reconstruction by simulating
the energy loss of beam ions in the target material and identifying sources of
uncertainty in these corrections. We show that an average COM velocity can be
used in the reconstruction and discuss errors as a function of ion beam energy,
composition, and alpha detection location. When accounting for the COM effect,
the mean of the reconstructed locations can be considered a systematic error
leading to shift/tilt in the reconstruction. However, the distribution of
reconstructed locations also have a spread that will introduce an error in the
reconstruction that cannot be corrected. In this paper, we will use the known
stopping powers of ions in materials and reaction cross-sections to examine
this effect.


title: Hierarchical Text-Conditional Image Generation with CLIP Latents

abstract:
  Contrastive models like CLIP have been shown to learn robust representations
of images that capture both semantics and style. To leverage these
representations for image generation, we propose a two-stage model: a prior
that generates a CLIP image embedding given a text caption, and a decoder that
generates an image conditioned on the image embedding. We show that explicitly
generating image representations improves image diversity with minimal loss in
photorealism and caption similarity. Our decoders conditioned on image
representations can also produce variations of an image that preserve both its
semantics and style, while varying the non-essential details absent from the
image representation. Moreover, the joint embedding space of CLIP enables
language-guided image manipulations in a zero-shot fashion. We use diffusion
models for the decoder and experiment with both autoregressive and diffusion
models for the prior, finding that the latter are computationally more
efficient and produce higher-quality samples.


title:
An excess chemical potential for binary hard-sphere mixtures from
  integral equation theory

abstract:
  We solve the site-site Ornstein-Zernike equation using the Percus-Yevick
closure for binary hard-sphere mixture. We calculate an excess chemical
potential for the mixture's diameter ratios of 0.3, 0.5, 0.6 and 0.9, and at
packing fraction of 0.49 using the analytical expression. Our numerical results
are in good agreement with those in the literature.


title: Reinforcement learning on graphs: A survey

abstract:
  Graph mining tasks arise from many different application domains, ranging
from social networks, transportation to E-commerce, etc., which have been
receiving great attention from the theoretical and algorithmic design
communities in recent years, and there has been some pioneering work employing
the research-rich Reinforcement Learning (RL) techniques to address graph data
mining tasks. However, these graph mining methods and RL models are dispersed
in different research areas, which makes it hard to compare them. In this
survey, we provide a comprehensive overview of RL and graph mining methods and
generalize these methods to Graph Reinforcement Learning (GRL) as a unified
formulation. We further discuss the applications of GRL methods across various
domains and summarize the method descriptions, open-source codes, and benchmark
datasets of GRL methods. Furthermore, we propose important directions and
challenges to be solved in the future. As far as we know, this is the latest
work on a comprehensive survey of GRL, this work provides a global view and a
learning resource for scholars. In addition, we create an online open-source
for both interested scholars who want to enter this rapidly developing domain
and experts who would like to compare GRL methods.


title:
Are You Really Muted?: A Privacy Analysis of Mute Buttons in Video
  Conferencing Apps

abstract:
  Video conferencing apps (VCAs) make it possible for previously private spaces
-- bedrooms, living rooms, and kitchens -- into semi-public extensions of the
office. For the most part, users have accepted these apps in their personal
space without much thought about the permission models that govern the use of
their private data during meetings. While access to a device's video camera is
carefully controlled, little has been done to ensure the same level of privacy
for accessing the microphone. In this work, we ask the question: what happens
to the microphone data when a user clicks the mute button in a VCA? We first
conduct a user study to analyze users' understanding of the permission model of
the mute button. Then, using runtime binary analysis tools, we trace raw audio
flow in many popular VCAs as it traverses the app from the audio driver to the
network. We find fragmented policies for dealing with microphone data among
VCAs -- some continuously monitor the microphone input during mute, and others
do so periodically. One app transmits statistics of the audio to its telemetry
servers while the app is muted. Using network traffic that we intercept en
route to the telemetry server, we implement a proof-of-concept background
activity classifier and demonstrate the feasibility of inferring the ongoing
background activity during a meeting -- cooking, cleaning, typing, etc. We
achieved 81.9% macro accuracy on identifying six common background activities
using intercepted outgoing telemetry packets when a user is muted.


title:
Ab Initio Prediction of Excited State and Polaron Effects in Transient
  XUV Measurements of $\alpha$-Fe$_2$O$_3$

abstract:
  Transient X-ray and extreme ultraviolet (XUV) spectroscopies have become
invaluable tools for studying photoexcited dynamics due to their sensitivity to
carrier occupations and local chemical or structural changes. One of the most
studied mate-rials using transient XUV spectroscopy is $\alpha$-Fe$_2$O$_3$
because of its rich photoexcited dynamics, including small polaron formation.
The interpretation of carrier and polaron effects in $\alpha$-Fe$_2$O$_3$ is
currently done using a semi-empirical method that is not transferrable to most
materials. Here, an ab initio, Bethe-Salpeter equation (BSE) approach is
developed that can incorporate photoexcited state effects for arbitrary
materials systems. The accuracy of this approach is proven by calculating the
XUV absorption spectra for the ground, photoexcited, and polaron states of
$\alpha$-Fe$_2$O$_3$. Furthermore, the theoretical approach allows for the
projection of the core-valence excitons and different components of the X-ray
transition Hamiltonian onto the band structure, providing new insights into old
measurements. From this information, a physical intuition about the origins and
nature of the transient XUV spectra can be built. A route to extracting
electron and hole energies is even shown possible for highly angular momentum
split XUV peaks. This method is easily generalized to K, L, M, and N edges to
provide a general approach for analyzing transient X-ray absorption or
reflection data.


title: Impossible Triangle: What's Next for Pre-trained Language Models?

abstract:
  Recent development of large-scale pre-trained language models (PLM) have
significantly improved the capability of models in various NLP tasks, in terms
of performance after task-specific fine-tuning and zero-shot / few-shot
learning. However, many of such models come with a dauntingly huge size that
few institutions can afford to pre-train, fine-tune or even deploy, while
moderate-sized models usually lack strong generalized few-shot learning
capabilities. In this paper, we first elaborate the current obstacles of using
PLM models in terms of the Impossible Triangle: 1) moderate model size, 2)
state-of-the-art few-shot learning capability, and 3) state-of-the-art
fine-tuning capability. We argue that all existing PLM models lack one or more
properties from the Impossible Triangle. To remedy these missing properties of
PLMs, various techniques have been proposed, such as knowledge distillation,
data augmentation and prompt learning, which inevitably brings additional work
to the application of PLMs in real scenarios. We then offer insights into
future research directions of PLMs to achieve the Impossible Triangle, and
break down the task into several key phases.


title:
Timeloops: Automatic System Call Policy Learning for Containerized
  Microservices

abstract:
  In this paper we introduce Timeloops a novel technique for automatically
learning system call filtering policies for containerized microservices
applications. At run-time, Timeloops automatically learns which system calls a
program should be allowed to invoke while rejecting attempts to call spurious
system calls. Further, Timeloops addresses many of the shortcomings of
state-of-the-art static analysis-based techniques, such as the ability to
generate tight filters for programs written in interpreted languages such as
PHP, Python, and JavaScript. Timeloops has a simple and robust implementation
because it is mainly built out of commodity, and proven, technologies such as
seccomp-BPF, systemd, and Podman containers, with fewer than 500 lines of code.
We demonstrate the utility of Timeloops by learning system calls for individual
services and two microservices benchmark applications, which utilize popular
technologies like Python Flask, Nginx (with PHP and Lua modules), Apache
Thrift, Memcached, Redis, and MongoDB. Further, the amortized performance of
Timeloops is similar to that of an unhardened system while producing a smaller
system call filter than state-of-the-art static analysis-based techniques.


title:
Formation of black widows through ultra-compact X-ray binaries with He
  star companions

abstract:
  Black widows (BWs) are a type of eclipsing millisecond pulsars (MSPs) with
companion masses $\lesssim 0.05\,\rm M_\odot$, which can be used to study the
accretion history and the radiation of pulsars, as well as the origin of
isolated MSPs. Recent observations indicate that there are two sub-types of
BWs. One is the BWs with companion masses $M_2$ $\lesssim 0.01\,\rm M_\odot$,
whereas another with $M_2$ $\sim$ $0.01-0.05\,\rm M_\odot$. However, the origin
of the former is still highly uncertain. In this paper, we investigated the
formation of BWs with $M_2$ $\lesssim 0.01\,\rm M_\odot$ through ultra-compact
X-ray binaries (UCXBs) with He star companions, in which a neutron star (NS)
accretes material from a He star through Roche-lobe overflow. By considering
different He star masses and evaporation efficiencies with the stellar
evolution code Modules for Experiments in Stellar Astrophysics (MESA), we
evolved a series of NS+He star systems that can undergo UCXB stage. We found
that this channel can explain the formation of BWs with $M_2$ $\lesssim
0.01\,\rm M_\odot$ within the Hubble time, especially three widely studied BWs,
i.e. PSRs J1719-1438, J2322-2650 and J1311-3430. We also found that X-ray
irradiation feedback does not affect the evolutionary tracks of evaporation
process. Our simulations indicate that the origin of BWs with $M_2$ $\lesssim
0.01\,\rm M_\odot$ is different with another sub-type of BWs, and that the UCXB
channel with He star companions are the potential progenitors of isolated MSPs.
In addition, the present work suggests that the BWs with $M_2$ $\lesssim
0.01\,\rm M_\odot$ may not be produced by redback systems.


title: Chaos, complexity, and intermittent turbulence in space plasmas

abstract:
  Intermittent turbulence is key for understanding the stochastic nonlinear
dynamics of space, astrophysical, and laboratory plasmas. We review the
observation and theory of chaos and complexity in plasmas, and elucidate their
links to intermittent plasma turbulence. First, we present evidence of magnetic
reconnection and intermittent magnetic turbulence in coronal mass ejections in
the solar corona and solar wind via remote and in situ observations. The
signatures of turbulent magnetic reconnection, i.e., bifurcated current sheet,
reconnecting jet, parallel/anti-parallel Alfv\'en waves, and spiky dynamical
pressure pulse, as well as fully-developed Kolmogorov intermittent turbulence,
are detected at the leading edge of an interplanetary coronal mass ejection and
the interface region of two interplanetary magnetic flux ropes. Methods for
quantifying the degree of coherence, amplitude-phase synchronization, and
multifractality of nonlinear multiscale fluctuations are discussed. The
stochastic chaotic nature of Alfv\'en intermittent structures driven by
magnetic reconnection is determined by a complexity-entropy analysis. Next, we
discuss the theory of chaos, intermittency, and complexity for nonlinear
Alfv\'en waves, and parametric decay and modulational wave-wave interactions,
in the absence/presence of noise. The transition from order to chaos is studied
using the bifurcation diagram. Two types of plasma chaos are considered: type-I
Pomeau-Manneville intermittency and crisis-induced intermittency. The role of
transient structures known as chaotic saddles in deterministic and stochastic
chaos in plasmas is investigated. Alfv\'en complexity associated with
noise-induced intermittency, in the presence of multistability, is studied.


title: CWcollab: A Context-Aware Web-Based Collaborative Multimedia System

abstract:
  Remote collaboration tools for conferencing and presentation are gaining
significant popularity during the COVID-19 pandemic period. Most prior work has
issues, such as a) limited support for media types, b) lack of interactivity,
for example, an efficient replay mechanism, c) large bandwidth consumption for
screen sharing tools. In this paper, we propose a general-purpose multimedia
collaboration platform-CWcollab. It supports collaboration on general
multimedia by using simple messages to represent media controls with an
object-prioritized synchronization approach. Thus, CWcollab can not only
support fine-grained accurate collaboration, but also rich functionalities such
as replay of these collaboration events. The evaluation shows hundreds of
kilobytes can be enough to store the events in a collaboration session for
accurate replays, compared with hundreds of megabytes of Google Hangouts.


title: Thermodynamics of PNJL at zero temperature in a strong magnetic field

abstract:
  In this paper, the deconfinement and chiral restoration transitions in strong
magnetic field is realized at zero temperature in the Polyakov
Nambu$-$Jona-Lasinio model. We provide the thermodynamic treatment to mimic the
deconfinement phase transition at zero temperature together with the entangled
scalar and vector interactions coupled with the Polyakov loop. The magnetic
catalysis is found by a rising behavior of the critical chemical potential for
the first-order deconfinement phase transition. While the magnetic catalysis on
the chiral restoration could convert to inverse magnetic catalysis under the
running coupling interaction ansatz. Furthermore, the stronger magnetic field
makes the possible quarkyonic phase window to be enlarged under the running
coupling interaction.


title:
Simultaneous Lane-Keeping and Obstacle Avoidance by Combining Model
  Predictive Control and Control Barrier Functions

abstract:
  In this work, we combine {Model Predictive Control} (MPC) and Control Barrier
Function (CBF) design {methods} to create a hierarchical control law for
simultaneous lane-keeping (LK) and obstacle avoidance (OA): at the low level,
MPC performs LK via trajectory tracking during nominal operation; and at the
high level, different CBF-based safety filters that ensure both LK and OA are
designed and compared across some practical scenarios. In particular, we show
that Exponential Safety (ESf) and Prescribed-Time Safety (PTSf) filters, which
override the MPC control when necessary, result in feasible Quadratic Programs
when safety is prioritized appropriately. We additionally investigate control
designs subject to input constraints by using Input-Constrained-CBFs. Finally,
we compare the performance of combinations of ESf, PTSf, and their
input-constrained counterparts with respect to the LK and OA goals in two
simulation studies for early- and late-detected obstacle scenarios.


title:
Lax equations for relativistic ${\rm GL}(NM,{\mathbb C})$ Gaudin models
  on elliptic curve

abstract:
  We describe the most general ${\rm GL}_{NM}$ classical elliptic
finite-dimensional integrable system, which Lax matrix has $n$ simple poles on
elliptic curve. For $M=1$ it reproduces the classical inhomogeneous spin chain,
for $N=1$ it is the Gaudin type (multispin) extension of the spin
Ruijsenaars-Schneider model, and for $n=1$ the model of $M$ interacting
relativistic ${\rm GL}_N$ tops emerges in some particular case. In this way we
present a classification for relativistic Gaudin models on ${\rm GL}$-bundles
over elliptic curve. As a by-product we describe the inhomogeneous Ruijsenaars
chain. We show that this model can be considered as a particular case of
multispin Ruijsenaars-Schneider model when residues of the Lax matrix are of
rank one. An explicit parametrization of the classical spin variables through
the canonical variables is obtained for this model. Finally, the most general
${\rm GL}_{NM}$ model is also described through $R$-matrices satisfying
associative Yang-Baxter equation. This description provides the trigonometric
and rational analogues of ${\rm GL}_{NM}$ models.


title:
A Three-phase Augmented Classifiers Chain Approach Based on
  Co-occurrence Analysis for Multi-Label Classification

abstract:
  As a very popular multi-label classification method, Classifiers Chain has
recently been widely applied to many multi-label classification tasks. However,
existing Classifier Chains methods are difficult to model and exploit the
underlying dependency in the label space, and often suffer from the problems of
poorly ordered chain and error propagation. In this paper, we present a
three-phase augmented Classifier Chains approach based on co-occurrence
analysis for multi-label classification. First, we propose a co-occurrence
matrix method to model the underlying correlations between a label and its
precedents and further determine the head labels of a chain. Second, we propose
two augmented strategies of optimizing the order of labels of a chain to
approximate the underlying label correlations in label space, including Greedy
Order Classifier Chain and Trigram Order Classifier Chain. Extensive
experiments were made over six benchmark datasets, and the experimental results
show that the proposed augmented CC approaches can significantly improve the
performance of multi-label classification in comparison with CC and its popular
variants of Classifier Chains, in particular maintaining lower computational
costs while achieving superior performance.


title: Practical considerations for specifying a super learner

abstract:
  Common tasks encountered in epidemiology, including disease incidence
estimation and causal inference, rely on predictive modeling. Constructing a
predictive model can be thought of as learning a prediction function, i.e., a
function that takes as input covariate data and outputs a predicted value. Many
strategies for learning these functions from data are available, from
parametric regressions to machine learning algorithms. It can be challenging to
choose an approach, as it is impossible to know in advance which one is the
most suitable for a particular dataset and prediction task at hand. The super
learner (SL) is an algorithm that alleviates concerns over selecting the one
"right" strategy while providing the freedom to consider many of them, such as
those recommended by collaborators, used in related research, or specified by
subject-matter experts. It is an entirely pre-specified and data-adaptive
strategy for predictive modeling. To ensure the SL is well-specified for
learning the prediction function, the analyst does need to make a few important
choices. In this Education Corner article, we provide step-by-step guidelines
for making these choices, walking the reader through each of them and providing
intuition along the way. In doing so, we aim to empower the analyst to tailor
the SL specification to their prediction task, thereby ensuring their SL
performs as well as possible. A flowchart provides a concise, easy-to-follow
summary of key suggestions and heuristics, based on our accumulated experience,
and guided by theory.


title:
Influence of Co and Mn on Electronic and Magnetic properties of Ni2MnGa
  Heusler alloy

abstract:
  The ferromagnetic Heusler alloy $Ni_2MnGa$ had been of major interest in the
past few years because of its magnetic properties which can be easily tuned.
The $Ni_2MnGa$ Heusler alloys are intermetallic alloy with $L2_1$ structure.
Here we report a detailed investigation of the effect of doping of Co and Mn in
Ni2MnGa. Magnetic properties and electronic structure of
$Ni_{2-x}Co_xMnGa_{1-y}Mn_y$ Heusler alloys have been studied by using Green's
function-based KKR-CPA method based DFT calculations. We will show the
magnetization can be tuned depending on the Co and Mn occumencies. We will also
discuss the critical temperature, magnetic interactions and magnetic stability
of the systems.


title: Effective Drift Estimates for Random Walks on Graph Products

abstract:
  We find uniform lower bounds on the drift for a large family of random walks
on graph products, of the form $ \mathbb{P} (|Z_{n}| \leq \kappa n) \leq e
^{-\kappa n} $ for $ \kappa > 0 $. This includes the simple random walk for a
right-angled Artin group with a sparse defining graph. This is done by
extending an argument of Gou\"{e}zel, along with the combinatorial notion of a
piling introduced by Crisp, Godelle, and Wiest. We do not use any moment
conditions, instead considering random walks which alternate between one
measure uniformly distributed on vertex groups, and another measure over which
we make almost no assumptions.


title:
Retrieval of Scientific and Technological Resources for Experts and
  Scholars

abstract:
  Institutions of higher learning, research institutes and other scientific
research units have abundant scientific and technological resources of experts
and scholars, and these talents with great scientific and technological
innovation ability are an important force to promote industrial upgrading. The
scientific and technological resources of experts and scholars are mainly
composed of basic attributes and scientific research achievements. The basic
attributes include information such as research interests, institutions, and
educational work experience. However, due to information asymmetry and other
reasons, the scientific and technological resources of experts and scholars
cannot be connected with the society in a timely manner, and social needs
cannot be accurately matched with experts and scholars. Therefore, it is very
necessary to build an expert and scholar information database and provide
relevant expert and scholar retrieval services. This paper sorts out the
related research work in this field from four aspects: text relation
extraction, text knowledge representation learning, text vector retrieval and
visualization system.


title:
Isolated singularities for fractional Lane-Emden equations in the
  Serrin's supercritical case

abstract:
  In this paper, we give a classification of the isolated singularities of
positive solutions to the semilinear fractional elliptic equations $$(E)
\quad\quad (-\Delta)^s u = |x|^{\theta} u^{p}\quad {\rm in}\ \
B_1\setminus\{0\},\quad u= h\quad{\rm in}\ \ \mathbb{R}^N\setminus B_1,\quad $$
where $s\in(0,1)$, $\theta>-2s$, $p>\frac{N+\theta}{N-2s}$, $B_1$ is the unit
ball centered at the origin of $\mathbb{R}^N$ with $N>2s$. $h$ is a nonnegative
H\"older continuous function in $\mathbb{R}^N\setminus B_1$. Our analysis of
isolated singularities of $(E)$ is based on an integral upper bounds and the
study of the Poisson problem with the fractional Hardy operators. It is worth
noting that our classification of isolated singularity holds in the Sobolev
super critical case $p>\frac{N+2s+2\theta}{N-2s}$ for $s\in(0,1]$ under
suitable assumption of $h$.


title: Feasibility of Kitaev quantum spin liquids in ultracold polar molecules

abstract:
  Ultracold atoms and molecules trapped in optical lattices are expected to
serve as simulators of strongly correlated systems and topological states of
matter. A fascinating example is to realize the Kitaev quantum spin liquid by
using ultracold polar molecules. However, although experimental implementation
of the Kitaev-type interaction was proposed, the stability of the Kitaev
quantum spin liquid has not been fully investigated thus far. Here we study a
quantum spin model with long-range angle-dependent Kitaev-type interactions
proposed for the polar molecules, by the pseudofermion functional
renormalization group method. We reveal that the ground state is magnetically
ordered in both ferromagnetic and antiferromagnetic models regardless of the
spatial anisotropy of the interactions, while the isotropic case is most
frustrated and closest to the realization of the Kitaev quantum spin liquid.
Furthermore, by introducing a cutoff in the interaction range, we clarify how
the Kitaev quantum spin liquid is destroyed by the long-range interactions. The
results urge us to reconsider the feasibility of the Kitaev quantum spin liquid
in ultracold polar molecules.


title:
HIT at SemEval-2022 Task 2: Pre-trained Language Model for Idioms
  Detection

abstract:
  The same multi-word expressions may have different meanings in different
sentences. They can be mainly divided into two categories, which are literal
meaning and idiomatic meaning. Non-contextual-based methods perform poorly on
this problem, and we need contextual embedding to understand the idiomatic
meaning of multi-word expressions correctly. We use a pre-trained language
model, which can provide a context-aware sentence embedding, to detect whether
multi-word expression in the sentence is idiomatic usage.


title:
Multi-critical point and unified description of broken-symmetry phases
  in spin-1/2 anti-ferromagnets on a square lattice

abstract:
  We show that several distinct broken-symmetry phases in a spin-1/2
anti-ferromagnet on a square lattice with easy-plane anisotropy, including
valence bond solid, chiral spin liquid, and the XY-ordered state, can all be
accessed by perturbing a multi-critical point with two massless Dirac fermions
coupled to a level-one Chern-Simons gauge field. This allows for a unified
description of these phases, as well as the phase transitions between them. In
a specific phase transition our analysis provide a lattice realization of one
of the recently proposed fermion-boson duality, thus lending support to it. We
also briefly discuss the relation between our work and the long-sought
de-confined criticality in such systems.


title:
High-energy studies of the 3HWC J1954+286 region: likely Gamma-ray
  detection of the supernova remnant G65.1+0.6

abstract:
  We carry out high-energy studies of the region of the Galactic TeV source
3HWC J1954+286, whose location coincides with those of PSR~J1954+2836 and
supernova remnant (SNR) G65.1+0.6. Analyzing the GeV $\gamma$-ray data obtained
with the Large Area Telescope (LAT) onboard {\it the Fermi Gamma-ray Space
Telescope}, we are able to separate the pulsar's emission from that of the
region. Excess power-law--like emission of a $\sim 6\sigma$ significance level
at the region is found, for which we explain as arising from the SNR~G65.1+0.6.
Given the low-significance detection, either a hadronic or a leptonic model can
provide a fit to the power-law spectrum. Considering the properties of the
pulsar and the SNR, we discuss the possible origin of the TeV source, and
suggest that it is likely the TeV halo associated with the pulsar.


title:
Almost sharp wave kinetic theory of multidimensional KdV type equations
  with $d\ge 3$

abstract:
  In this work we study the random series expansion of a multidimensional KdV
type equation, the Zakharov-Kuznetsov (ZK) equation. The equation is given with
random initial data and periodic boundary condition with period $L$. We derive
the $3$-wave kinetic equation on the inertial range using this series expansion
for $t\lesssim L^{-\varepsilon}T_{\text{kin}}$ which is almost sharp. Compared
to the paper of Staffilani and Tran [28], our result works for low dimension
case $d\ge 3$.


title:
Multi-grating design for integrated single-atom trapping, manipulation,
  and readout

abstract:
  An on-chip multi-grating device is proposed to interface single-atoms and
integrated photonic circuits, by guiding and focusing lasers to the area with
~10um above the chip for trapping, state manipulation, and readout of single
Rubidium atoms. For the optical dipole trap, two 850 nm laser beams are
diffracted and overlapped to form a lattice of single-atom dipole trap, with
the diameter of optical dipole trap around 2.7um. Similar gratings are designed
for guiding 780 nm probe laser to excite and also collect the fluorescence of
87Rb atoms. Such a device provides a compact solution for future applications
of single atoms, including the single photon source, single-atom quantum
register, and sensor.


title:
A quantum generative model for multi-dimensional time series using
  Hamiltonian learning

abstract:
  Synthetic data generation has proven to be a promising solution for
addressing data availability issues in various domains. Even more challenging
is the generation of synthetic time series data, where one has to preserve
temporal dynamics, i.e., the generated time series must respect the original
relationships between variables across time. Recently proposed techniques such
as generative adversarial networks (GANs) and quantum-GANs lack the ability to
attend to the time series specific temporal correlations adequately. We propose
using the inherent nature of quantum computers to simulate quantum dynamics as
a technique to encode such features. We start by assuming that a given time
series can be generated by a quantum process, after which we proceed to learn
that quantum process using quantum machine learning. We then use the learned
model to generate out-of-sample time series and show that it captures unique
and complex features of the learned time series. We also study the class of
time series that can be modeled using this technique. Finally, we
experimentally demonstrate the proposed algorithm on an 11-qubit trapped-ion
quantum machine.


title: Pregroups and hyperbolicity

abstract:
  In this paper we study hyperbolicty of the universal group $U(P)$ of a
pregroup $P$. Given a finitely generated group $G$ and a pregroup $P$ such that
$G \simeq U(P)$, we provide a particular set of axioms on $P$ which ensure that
$G$ is word-hyperbolic.


title:
Group theory analysis of phonons in Y$_3$-Cr$_2$-X$_3$ with (X,Y) $\in$
  {F,Cl,Br,I}

abstract:
  A contrastive invastigation of the symmetry aspects of phonons in monolayer
chromic halide and their Janus structure Y$_3$-Cr$_2$-X$_3$ with (X,Y) $\in$
{F,Cl,Br,I} by group theory is presented in this study. We first calculate the
irreducible representation classification of all phonons at $\Gamma$ point
based on group theory. Then the infrared and Raman activity of optic phonons,
Raman tensors and the possible polarization assignments of R active phonons
also deduced. Besides, the compatibility relation of phonon irreducible
representation between Y$_3$-Cr$_2$-X$_3$ and X$_3$-Cr$_2$-X$_3$ monolayer is
offered, which is important for optic spectra identification of mirror
asymmetric Janus Y$_3$-Cr$_2$-X$_3$ monolayer from mirror symmetric
X$_3$-Cr$_2$-X$_3$ monolayer. In addition, we also provide the phonon
eigenfunctions and corresponding schematic representations of the eigenvectors
for both F$_3$-Cr$_2$-I$_3$ and I$_3$-Cr$_2$-I$_3$ monolayer as example. At
last, the spin-phonon coupling, the magneto-optical effect of infrared and
Raman active phonons and chiral phonons in Y$_3$-Cr$_2$-X$_3$ with (X,Y) $\in$
{F,Cl,Br,I} are discussed comparatively. Our results not only provide a
detailed guiding map for experimental characterization of Y$_3$-Cr$_2$-X$_3$
with (X,Y) $\in$ {F,Cl,Br,I} monolayer, but also reveal related effects of
optic phonons in Janus Y$_3$-Cr$_2$-X$_3$ can be tracked by their infrared and
Raman spectra.


title:
An End-to-End Analysis of EMFI on Bit-sliced Post-Quantum
  Implementations

abstract:
  Bit-slicing is a software implementation technique that treats an N-bit
processor datapath as N parallel single-bit datapaths. The natural spatial
redundancy of bit-sliced software can be used to build countermeasures against
implementation attacks. While the merits of bit-slicing for side-channel
countermeasures have been studied before, their application for protection of
post-quantum algorithms against fault injection is still unexplored. We present
an end-to-end analysis of the efficacy of bit-slicing to detect and thwart
electromagnetic fault injection (EMFI) attacks on post-quantum cryptography
(PQC). We study Dilithium, a digital signature finalist of the NIST PQC
competition. We present a bit-slice-redundant design for the Number-Theoretic
Transform (NTT), the most complex and compute-intensive component in Dilithium.
We show a data-redundant countermeasure for NTT which offers two concurrent
bits for every single bit in the original implementation. We then implement a
full Dilithium signature sequence on a 667 MHz ARM Cortex-A9 processor
integrated in a Xilinx Zynq SoC. We perform a detailed EM fault-injection
parameter search to optimize the location, intensity and timing of injected EM
pulses. We demonstrate that, under optimized fault injection parameters, about
10% of the injected faults become potentially exploitable. However, the
bit-sliced NTT design is able to catch the majority of these potentially
exploitable faults, even when the remainder of the Dilithium algorithm as well
as the control flow is left unprotected. To our knowledge, this is the first
demonstration of a bitslice-redundant design of Dilithium that offers
distributed fault detection throughout the execution of the algorithm.


title:
MGM: A meshfree geometric multilevel method for systems arising from
  elliptic equations on point cloud surfaces

abstract:
  We develop a new meshfree geometric multilevel (MGM) method for solving
linear systems that arise from discretizing elliptic PDEs on surfaces
represented by point clouds. The method uses a Poisson disk sampling-type
technique for coarsening the point clouds and new meshfree
restriction/interpolation operators based on polyharmonic splines for
transferring information between the coarsened point clouds. These are then
combined with standard smoothing and operator coarsening methods in a V-cycle
iteration. MGM is applicable to discretizations of elliptic PDEs based on
various localized meshfree methods, including RBF finite differences (RBF-FD)
and generalized finite differences (GFD). We test MGM both as a standalone
solver and preconditioner for Krylov subspace methods on several test problems
using RBF-FD and GFD, and numerically analyze convergence rates, efficiency,
and scaling with increasing point cloud sizes. We also perform a side-by-side
comparison to algebraic multigrid (AMG) methods for solving the same systems.
Finally, we further demonstrate the effectiveness of MGM by applying it to
three challenging applications on complicated surfaces: pattern formation,
surface harmonics, and geodesic distance.


title:
Countering detector manipulation attacks in quantum communication
  through detector self-testing

abstract:
  In practical quantum key distribution systems, imperfect physical devices
open security loopholes that challenge the core promise of this technology.
Apart from various side channels, a vulnerability of single-photon detectors to
blinding attacks has been one of the biggest concerns, and has been addressed
both by technical means as well as advanced protocols. In this work, we present
a countermeasure against such attacks based on self-testing of detectors to
confirm their intended operation without relying on specific aspects of their
inner working, and to reveal any manipulation attempts. We experimentally
demonstrate this countermeasure with a typical InGaAs avalanche photodetector,
but the scheme can be easily implemented with any single photon detector.