title: Simulating Liquids with Graph Networks

abstract:
  Simulating complex dynamics like fluids with traditional simulators is
computationally challenging. Deep learning models have been proposed as an
efficient alternative, extending or replacing parts of traditional simulators.
We investigate graph neural networks (GNNs) for learning fluid dynamics and
find that their generalization capability is more limited than previous works
would suggest. We also challenge the current practice of adding random noise to
the network inputs in order to improve its generalization capability and
simulation stability. We find that inserting the real data distribution, e.g.
by unrolling multiple simulation steps, improves accuracy and that hiding all
domain-specific features from the learning model improves generalization. Our
results indicate that learning models, such as GNNs, fail to learn the exact
underlying dynamics unless the training set is devoid of any other
problem-specific correlations that could be used as shortcuts.


title: Two short closed geodesics on a sphere of odd dimension

abstract:
  We show that for an open and dense set non-reversible Finsler metrics on a
sphere of odd dimension $n=2m-1 \ge 3$ there is a second closed geodesic with
Morse index $\le 4(m+2)(m-1)+2.$


title: Magnetic Field Prediction Using Generative Adversarial Networks

abstract:
  Plenty of scientific and real-world applications are built on magnetic fields
and their characteristics. To retrieve the valuable magnetic field information
in high resolution, extensive field measurements are required, which are either
time-consuming to conduct or even not feasible due to physical constraints. To
alleviate this problem, we predict magnetic field values at a random point in
space from a few point measurements by using a generative adversarial network
(GAN) structure. The deep learning (DL) architecture consists of two neural
networks: a generator, which predicts missing field values of a given magnetic
field, and a critic, which is trained to calculate the statistical distance
between real and generated magnetic field distributions. By minimizing this
statistical distance, a reconstruction loss as well as physical losses, our
trained generator has learned to predict the missing field values with a median
reconstruction test error of 5.14%, when a single coherent region of field
points is missing, and 5.86%, when only a few point measurements in space are
available and the field measurements around are predicted. We verify the
results on an experimentally validated field.


title:
Dynamic Time Warping Under Translation: Approximation Guided by
  Space-Filling Curves

abstract:
  The Dynamic Time Warping (DTW) distance is a popular measure of similarity
for a variety of sequence data. For comparing polygonal curves $\pi, \sigma$ in
$\mathbb{R}^d$, it provides a robust, outlier-insensitive alternative to the
Fr\'echet distance. However, like the Fr\'echet distance, the DTW distance is
not invariant under translations. Can we efficiently optimize the DTW distance
of $\pi$ and $\sigma$ under arbitrary translations, to compare the curves'
shape irrespective of their absolute location?
  There are surprisingly few works in this direction, which may be due to its
computational intricacy: For the Euclidean norm, this problem contains as a
special case the geometric median problem, which provably admits no exact
algebraic algorithm (that is, no algorithm using only addition, multiplication,
and $k$-th roots). We thus investigate exact algorithms for non-Euclidean norms
as well as approximation algorithms for the Euclidean norm:
  - For the $L_1$ norm in $\mathbb{R}^d$, we provide an
$\mathcal{O}(n^{2(d+1)})$-time algorithm, i.e., an exact polynomial-time
algorithm for constant $d$. Here and below, $n$ bounds the curves'
complexities.
  - For the Euclidean norm in $\mathbb{R}^2$, we show that a simple
problem-specific insight leads to a $(1+\varepsilon)$-approximation in time
$\mathcal{O}(n^3/\varepsilon^2)$. We then show how to obtain a subcubic
$\widetilde{\mathcal{O}}(n^{2.5}/\varepsilon^2)$ time algorithm with
significant new ideas; this time comes close to the well-known quadratic time
barrier for computing DTW for fixed translations. Technically, the algorithm is
obtained by speeding up repeated DTW distance estimations using a dynamic data
structure for maintaining shortest paths in weighted planar digraphs.
Crucially, we show how to traverse a candidate set of translations using
space-filling curves in a way that incurs only few updates to the data
structure.


title:
A planning tool for optimal three-dimensional formation flight maneuvers
  of satellites in VLEO using aerodynamic lift and drag via yaw angle
  deviations

abstract:
  Differential drag is a promising option to control the relative motion of
distributed satellites in the Very Low Earth Orbit regime which are not
equipped with dedicated thrusting devices. A major downside of the methodology,
however, is that its control authority is (mainly) limited to the in-plane
relative motion control. By additionally applying differential lift, however,
all three translational degrees-of-freedom become controllable. In this
article, we present a tool to flexibly plan optimal three-dimensional formation
flight maneuvers via differential lift and drag. In the planning process, the
most significant perturbing effects in this orbital regime, namely the J2
effect and atmospheric forces, are taken into account. Moreover, varying
atmospheric densities as well as the co-rotation of the atmosphere are
considered. Besides its flexible and high-fidelity nature, the major assets of
the proposed methodology are that the in-and out-of-plane relative motion are
controlled simultaneously via deviations in the yaw angles of the respective
satellites and that the planned trajectory is optimal in a sense that the
overall decay during the maneuver is minimized. Thereby, the remaining lifetime
of the satellites is maximized and the practicability and sustainability of the
methodology significantly increased. To the best of the authors knowledge, a
tool with the given capabilities has not yet been presented in literature. The
resulting trajectories for three fundamentally different relevant formation
flight maneuvers are presented and discussed in detail in order to indicate the
vast range of applicability of the tool.


title:
Constraining modified gravity with gravitational wave distance
  measurements

abstract:
  It has been shown in the literature that detections of gravitational waves
(GWs) emitted by binary sources can provide measurements of luminosity
distance. The events followed by electromagnetic counterparts are, then,
suitable for probing the distance-redshift relation and doing cosmological
parameter estimation, as well as investigating modified gravity (MG) models. In
the context of effective approaches to MG equivalent to Horndeski, this GW
distance differs from the standard electromagnetic luminosity distance due to
the presence of a modified friction in the wave propagation. Here, we
investigate how precisely the future-planned interferometer Einstein Telescope
will probe such deviations from General Relativity, considering
phenomenological parametrizations for both the dark energy equation of state
and the GW friction. Despite being an independent test of gravity, for $f(R)$
in particular, we conclude that it may provide weaker constraints than the
current available ones.


title:
Introduction to a low-mass dark matter project, ALETHEIA: A Liquid
  hElium Time projection cHambEr In dArk matter

abstract:
  Although Weakly Interactive Massive Particles (WIMPs) is the most discussed
DM candidate, the null-WIMPs conclusion has been consistently addressed by most
convincing experiments in the field. However, the low-mass WIMPs region (10
MeV/c$^2$ - 10 GeV/c$^2$) has not been fully exploited compared to high-mass
WIMPs (10 GeV/c$^2$ - 10 TeV/c$^2$) experiments which implement liquid xenon or
argon TPCs (Time Projection Chambers). The ALETHEIA experiment aims to hunt for
low-mass WIMPs with liquid helium-filled TPCs. In this paper, we go through the
physics motivation of low-mass DM, the ALETHEIA detector's design, possible
analysis channels available for DM searches, and the latest progress we have
made recently.


title: Generalized Rectifier Wavelet Covariance Models For Texture Synthesis

abstract:
  State-of-the-art maximum entropy models for texture synthesis are built from
statistics relying on image representations defined by convolutional neural
networks (CNN). Such representations capture rich structures in texture images,
outperforming wavelet-based representations in this regard. However, conversely
to neural networks, wavelets offer meaningful representations, as they are
known to detect structures at multiple scales (e.g. edges) in images. In this
work, we propose a family of statistics built upon non-linear wavelet based
representations, that can be viewed as a particular instance of a one-layer
CNN, using a generalized rectifier non-linearity. These statistics
significantly improve the visual quality of previous classical wavelet-based
models, and allow one to produce syntheses of similar quality to
state-of-the-art models, on both gray-scale and color textures.


title:
UV and IR divergence-free calculation of the vertex function at
  arbitrary momentum transfer

abstract:
  The vertex function is analyzed using covariant spectral regularization
without encountering any divergence, either UV or IR. The mathematics of
covariant spectral regularization for covariant matrix valued measures with one
Lorentz index on open subsets of Minkowski space is described. This is then
applied to the case of the vertex function and expressions for the densities
associated with the vertex function in the t channel and the s channel with
respect to Lebesgue measure on Minkowski space are obtained. These densities
are well defined, non-divergent and analytic over their domains of definition
and can be used in QFT calculations. The limit of the expression for the vertex
function in the t channel at low energy and low momenta is computed resulting
in the classical result for the leading order (LO) contribution to the
anomalous magnetic moment of the electron. Also the density for the vertex
function in the s channel is used to compute the (LO) vertex correction
contribution to the high energy limit of the cross section for the process
$e^{+}e^{-}\rightarrow\mu^{+}\mu^{-}$ without divergence or the need to
consider final state radiation.


title: Unsupervised Learning Based Focal Stack Camera Depth Estimation

abstract:
  We propose an unsupervised deep learning based method to estimate depth from
focal stack camera images. On the NYU-v2 dataset, our method achieves much
better depth estimation accuracy compared to single-image based methods.


title:
Detectability of the gravitational-wave background produced by magnetar
  giant flares

abstract:
  We study the gravitational-wave background produced by f-mode oscillations of
neutron stars triggered by magnetar giant flares. For the gravitational-wave
energy, we use analytic formulae obtained via general relativistic
magnetohydrodynamic simulations of strongly magnetized neutron stars. Assuming
the magnetar giant flare rate is proportional to the star-formation rate, we
show the gravitational-wave signal is likely undetectable by third-generation
detectors such as the Einstein Telescope and Cosmic Explorer. We calculate the
minimum value of the magnetic field and the magnetar giant flare rate necessary
for such a signal to be detectable, and discuss these in the context of our
current understanding of magnetar flares throughout the Universe.


title: Microwave heating effect on diamond sample of NV centers

abstract:
  Diamond samples of defects with negative charged nitrogen-vacancy (NV)
centers are promising solid state spin sensors suitable for quantum information
processing, high sensitive measurements of magnetic, electric and thermal
fields in nanoscale. The diamond defect with a NV center is unique for its
robust temperature-dependent zero field splitting Dgs of the triplet ground
state. This property enables optical readout of electron spin states through
manipulation of the ground triplet state using microwave resonance with Dgs
from 100 K to about 600 K. Thus, prohibiting Dgs from unwanted external thermal
disturbances is crucial for an accurate measurement using diamond NV sensors.
Our observation demonstrates the existence of a prominent microwave heating
effect on the diamond samples of NV centers. The effect is inevitable to shift
Dgs and cause measurement errors. The temperature increment caused by the
effect monotonically depends on the power and the duration of microwave
irradiation. The effect is obvious with the microwave irradiation in the
continuous mode and some pulse sequence modes, but is neglectable for the
quantum lock-in XY8-N method.


title: Snowmass 2021 White Paper: Resummation for future colliders

abstract:
  Resummation techniques are essential for high-precision phenomenology at
current and future high-energy collider experiments. Perturbative computations
of cross sections often suffer from large logarithmic corrections, which must
be resummed to all orders to restore the reliability of predictions from first
principles. The precise understanding of the all-order structure of field
theories allows for fundamental tests of the Standard Model and new physics
searches. In this white paper, we review recent progress in modern resummation
techniques and outline future directions. In particular, we focus on the
resummation beyond leading power, the joint resummation of different classes of
logarithms relevant for jets and their substructure, small-$x$ resummation in
the high-energy regime and the QCD fragmentation process in the small-$z_h$
limit.


title: Panoptic SwiftNet: Pyramidal Fusion for Real-time Panoptic Segmentation

abstract:
  Dense panoptic prediction is a key ingredient in many existing applications
such as autonomous driving, automated warehouses or agri-robotics. However,
most of these applications leverage the recovered dense semantics as an input
to visual closed-loop control. Hence, practical deployments require real-time
inference over large input resolutions on embedded hardware. These requirements
call for computationally efficient approaches which deliver high accuracy with
limited computational resources. We propose to achieve this goal by trading-off
backbone capacity for multi-scale feature extraction. In comparison with
contemporaneous approaches to panoptic segmentation, the main novelties of our
method are scale-equivariant feature extraction and cross-scale upsampling
through pyramidal fusion. Our best model achieves 55.9% PQ on Cityscapes val at
60 FPS on full resolution 2MPx images and RTX3090 with FP16 Tensor RT
optimization.


title: An Epistemic Analysis of Time Phenomenon

abstract:
  In this work we present an epistemic analysis of time phenomenon using the
mathematical machinery of information theory and modular theory. By adopting
limited commitment to the ontology of time evolution, and instead by mainly
relying on the information that is in principle accessible to the observer, we
find that the most primary aspect of the temporal experience, the perceived
distinctiveness across the states of the world, emerges as a purely epistemic
function. By analyzing the mathematical properties of this epistemic function,
we interpret it to be in principle insensitive to any ontic state of the world,
which leads to the conclusion that the observer is subject to temporal
experience irrespective of whether the underlying state of the world is
dynamical or invariant. On the ground of the presented analysis, we also
provide a solution to the conceptual challenge of non-equilibrium phenomena
that faces the thermal time hypothesis.


title:
Deep Transfer Learning with Graph Neural Network for Sensor-Based Human
  Activity Recognition

abstract:
  The sensor-based human activity recognition (HAR) in mobile application
scenarios is often confronted with sensor modalities variation and annotated
data deficiency. Given this observation, we devised a graph-inspired deep
learning approach toward the sensor-based HAR tasks, which was further used to
build a deep transfer learning model toward giving a tentative solution for
these two challenging problems. Specifically, we present a multi-layer residual
structure involved graph convolutional neural network (ResGCNN) toward the
sensor-based HAR tasks, namely the HAR-ResGCNN approach. Experimental results
on the PAMAP2 and mHealth data sets demonstrate that our ResGCNN is effective
at capturing the characteristics of actions with comparable results compared to
other sensor-based HAR models (with an average accuracy of 98.18% and 99.07%,
respectively). More importantly, the deep transfer learning experiments using
the ResGCNN model show excellent transferability and few-shot learning
performance. The graph-based framework shows good meta-learning ability and is
supposed to be a promising solution in sensor-based HAR tasks.


title:
Signal in Noise: Exploring Meaning Encoded in Random Character Sequences
  with Character-Aware Language Models

abstract:
  Natural language processing models learn word representations based on the
distributional hypothesis, which asserts that word context (e.g.,
co-occurrence) correlates with meaning. We propose that $n$-grams composed of
random character sequences, or $garble$, provide a novel context for studying
word meaning both within and beyond extant language. In particular, randomly
generated character $n$-grams lack meaning but contain primitive information
based on the distribution of characters they contain. By studying the
embeddings of a large corpus of garble, extant language, and pseudowords using
CharacterBERT, we identify an axis in the model's high-dimensional embedding
space that separates these classes of $n$-grams. Furthermore, we show that this
axis relates to structure within extant language, including word
part-of-speech, morphology, and concept concreteness. Thus, in contrast to
studies that are mainly limited to extant language, our work reveals that
meaning and primitive information are intrinsically linked.


title:
Scalable Bigraphical Lasso: Two-way Sparse Network Inference for Count
  Data

abstract:
  Classically, statistical datasets have a larger number of data points than
features ($n > p$). The standard model of classical statistics caters for the
case where data points are considered conditionally independent given the
parameters. However, for $n\approx p$ or $p > n$ such models are poorly
determined. Kalaitzis et al. (2013) introduced the Bigraphical Lasso, an
estimator for sparse precision matrices based on the Cartesian product of
graphs. Unfortunately, the original Bigraphical Lasso algorithm is not
applicable in case of large p and n due to memory requirements. We exploit
eigenvalue decomposition of the Cartesian product graph to present a more
efficient version of the algorithm which reduces memory requirements from
$O(n^2p^2)$ to $O(n^2 + p^2)$. Many datasets in different application fields,
such as biology, medicine and social science, come with count data, for which
Gaussian based models are not applicable. Our multi-way network inference
approach can be used for discrete data. Our methodology accounts for the
dependencies across both instances and features, reduces the computational
complexity for high dimensional data and enables to deal with both discrete and
continuous data. Numerical studies on both synthetic and real datasets are
presented to showcase the performance of our method.


title:
New Physics with missing energy at future lepton colliders -- Snowmass
  White Paper

abstract:
  Two models that extend the particle content of the SM and provide dark matter
candidates, namely the Inert Doublet Model and the Two-Higgs Doublet model with
additional pseudoscalar, are confronted with current experimental and
theoretical constraints and predictions for production cross sections for
various standard pair-production modes within these models at future lepton
colliders are presented.


title: Scrambling time for analogue black holes embedded in AdS space

abstract:
  We propose a gedanken experiment on realizing thermofield double state (TFD)
by using analog black holes and provide an approach to test the scrambling
time. Through this approach, we demonstrate clearly how shock wave changes the
TFD state as time evolves. As the whole system evolves forward in time, the
perturbation of space-time geometry will increase exponentially. Finally, it
will destroy the entanglement between the two states of the thermal field, and
the mutual information between them is reduced to zero in the time scale of
scrambling. The results show that for perturbations of analogue black holes
embedded in AdS space, the scale of the scrambling time is closely related to
the logarithm of entropy of the black hole. The results provide further
theoretical argument for the scrambling time, which can be further falsified in
experiments.


title:
On the Application of Total Traction Equilibrium in Topology
  Optimization of Fluid-Structure Interactions

abstract:
  This work investigates the different techniques of enforcing traction
equilibrium in topology optimization of fluid-structure interaction problems.
In literature, force coupling between the solid and the fluid domains is
usually achieved using internal volume integrals of hydrostatic stresses. In
this work, we extend the traction equilibrium condition to include the viscous
stress components as well as the hydrostatic components in what is termed the
total stress formulation. We also investigate the different force calculation
techniques, and how the mixed finite element formulation results in two
conflicting approaches; external surface vs. internal volume integrals. Lastly,
a numerical experiment reveals the effects of the different force coupling
techniques on the optimized designs with commentary on complexity of
implementation and convergence behaviour.


title: Spherical convex hull of random points on a wedge

abstract:
  Consider two half-spaces $H_1^+$ and $H_2^+$ in $\mathbb{R}^{d+1}$ whose
bounding hyperplanes $H_1$ and $H_2$ are orthogonal and pass through the
origin. The intersection $\mathbb{S}_{2,+}^d:=\mathbb{S}^d\cap H_1^+\cap H_2^+$
is a spherical convex subset of the $d$-dimensional unit sphere $\mathbb{S}^d$,
which contains a great subsphere of dimension $d-2$ and is called a spherical
wedge. Choose $n$ independent random points uniformly at random on
$\mathbb{S}_{2,+}^d$ and consider the expected facet number of the spherical
convex hull of these points. It is shown that, up to terms of lower order, this
expectation grows like a constant multiple of $\log n$. A similar behaviour is
obtained for the expected facet number of a homogeneous Poisson point process
on $\mathbb{S}_{2,+}^d$. The result is compared to the corresponding behaviour
of classical Euclidean random polytopes and of spherical random polytopes on a
half-sphere.


title: Hodge structure of O'Grady's singular moduli spaces

abstract:
  We investigate the Hodge structure of the singular O'Grady's six and ten
dimensional examples of irreducible symplectic varieties. In particular, we
compute some of their Betti numbers and their Euler characteristic. As
consequence, we deduce that these varieties do not have finite quotient
singularities answering a question of Bakker and Lehn.


title:
GPV-Pose: Category-level Object Pose Estimation via Geometry-guided
  Point-wise Voting

abstract:
  While 6D object pose estimation has recently made a huge leap forward, most
methods can still only handle a single or a handful of different objects, which
limits their applications. To circumvent this problem, category-level object
pose estimation has recently been revamped, which aims at predicting the 6D
pose as well as the 3D metric size for previously unseen instances from a given
set of object classes. This is, however, a much more challenging task due to
severe intra-class shape variations. To address this issue, we propose
GPV-Pose, a novel framework for robust category-level pose estimation,
harnessing geometric insights to enhance the learning of category-level
pose-sensitive features. First, we introduce a decoupled confidence-driven
rotation representation, which allows geometry-aware recovery of the associated
rotation matrix. Second, we propose a novel geometry-guided point-wise voting
paradigm for robust retrieval of the 3D object bounding box. Finally,
leveraging these different output streams, we can enforce several geometric
consistency terms, further increasing performance, especially for non-symmetric
categories. GPV-Pose produces superior results to state-of-the-art competitors
on common public benchmarks, whilst almost achieving real-time inference speed
at 20 FPS.


title: Report of the Snowmass'21 Workshop on High-Power Cyclotrons and FFAs

abstract:
  This whitepaper summarizes and the state of the field of high-power
cyclotrons and FFAs as discussed by international experts during a three-day
workshop of the same name. The workshop was held online from Sep 7 to Sep 9,
2021 as part of the US Snowmass'21 Community Exercise, specifically the
Accelerator Frontier (AF) and the subpanel Accelerators for Neutrinos (AF02).
Thus, we put emphasis on the application of high-power cyclotrons in particle
physics, specifically neutrino physics, and as drivers for muon production. In
the introduction, we discuss the role of cyclotrons for particle physics, and
later we highlight existing and planned experiments in the corresponding
sections. However, as these same accelerators have important applications in
the fields of isotope production - both for research and medicine - and
possibly even in energy research, by providing beam to demonstrator experiments
in the areas of Accelerator Driven Systems (ADS), we include these far-reaching
topics to provide a full picture of the status and applications of high-power
cyclotrons. Furthermore, Fixed Field Alternating Gradient accelerators (FFAs)
have recently seen renewed interest. They are in many respects (basic operating
principles) similar to cyclotrons and have thus been included in this workshop
and whitepaper as well. We are discussing current projects and whether FFAs
have the prospect of becoming high-intensity machines.


title: Cost-effective BlackWater Raft on Highly Unreliable Nodes at Scale Out

abstract:
  The Raft algorithm maintains strong consistency across data replicas in
Cloud. This algorithm divides nodes into leaders and followers, to satisfy
read/write requests spanning geo-diverse sites. With the increase of workload,
Raft shall provide scale-out performance in proportion. However, traditional
scale-out techniques encounter bottlenecks in Raft, and when the provisioned
sites exhaust local resources, the performance loss will grow exponentially. To
provide scalability in Raft, this paper proposes a cost-effective mechanism for
elastic auto-scaling in Raft, called BlackWater-Raft or BW-Raft. BW-Raft
extends the original Raft with the following abstractions: (1) secretary nodes
that take over expensive log synchronization operations from the leader,
relaxing the performance constraints on locks. (2) massive low cost observer
nodes that handle reads only, improving throughput for typical data intensive
services. These abstractions are stateless, allowing elastic scale-out on
unreliable yet cheap spot instances. In theory, we demonstrate that BW-Raft can
maintain Raft's strong consistency guarantees when scaling out, processing a
50X increase in the number of nodes compared to the original Raft. We have
prototyped the BW-Raft on key-value services and evaluated it with many
state-of-the-arts on Amazon EC2 and Alibaba Cloud. Our results show that within
the same budget, BW-Raft's resource footprint increments are 5-7X smaller than
Multi-Raft, and 2X better than original Raft. Using spot instances, BW-Raft can
reduces costs by 84.5\% compared to Multi-Raft. In the real world experiments,
BW-Raft improves goodput of the 95th-percentile SLO by 9.4X, thus serving as an
alternative for services scaling out with strong consistency.


title: Unsupervised Extractive Opinion Summarization Using Sparse Coding

abstract:
  Opinion summarization is the task of automatically generating summaries that
encapsulate information from multiple user reviews. We present Semantic
Autoencoder (SemAE) to perform extractive opinion summarization in an
unsupervised manner. SemAE uses dictionary learning to implicitly capture
semantic information from the review and learns a latent representation of each
sentence over semantic units. A semantic unit is supposed to capture an
abstract semantic concept. Our extractive summarization algorithm leverages the
representations to identify representative opinions among hundreds of reviews.
SemAE is also able to perform controllable summarization to generate
aspect-specific summaries. We report strong performance on SPACE and AMAZON
datasets, and perform experiments to investigate the functioning of our model.
Our code is publicly available at https://github.com/brcsomnath/SemAE.


title:
How informative is the Order Book Beyond the Best Levels? Machine
  Learning Perspective

abstract:
  Research on limit order book markets has been rapidly growing and nowadays
high-frequency full order book data is widely available for researchers and
practitioners. However, it is common that research papers use the best level
data only, which motivates us to ask whether the exclusion of the quotes deeper
in the book over multiple price levels causes performance degradation. In this
paper, we address this question by using modern Machine Learning (ML)
techniques to predict mid-price movements without assuming that limit order
book markets represent a linear system. We provide a number of results that are
robust across ML prediction models, feature selection algorithms, data sets,
and prediction horizons. We find that the best bid and ask levels are
systematically identified not only as the most informative levels in the order
books, but also to carry most of the information needed for good prediction
performance. On the other hand, even if the top-of-the-book levels contain most
of the relevant information, to maximize models' performance one should use all
data across all the levels. Additionally, the informativeness of the order book
levels clearly decreases from the first to the fourth level while the rest of
the levels are approximately equally important.


title:
$L^p-L^q$ local smoothing estimates for the wave equation via $k$-broad
  Fourier restriction

abstract:
  We explore the connection between $k$-broad Fourier restriction estimates and
sharp regularity $L^p-L^q$ local smoothing estimates for the solutions of the
wave equation in $\mathbb{R}^{n}\times \mathbb{R}$ for all $n \geq 3$ via a
Bourgain--Guth broad-narrow analysis. An interesting feature is that local
smoothing estimates for $e^{i t \sqrt{-\Delta}}$ are not invariant under
Lorentz rescaling.


title:
Fast, slow convergence, and concentration in the house of cards
  replicator-mutator model

abstract:
  We propose a fine analysis of the various possible long time behaviours of
the solutions of the replicator-mutator equation with so-called Kingman's house
of cards mutations. In particular, we give what is to our knowledge the first
concentration result for this model.


title: Transverse Electron Beam Shaping with Light

abstract:
  Interfacing electrons and light enables ultrafast electron microscopy,
quantum control of electrons, as well as new optical elements for high
sensitivity imaging. Here we demonstrate for the first time programmable
transverse electron beam shaping in free space based on ponderomotive
potentials from short intense laser pulses. We can realize both convex and
concave electron lenses with a focal length of a few millimeters, comparable to
those in state-of-the-art electron microscopes. We further show that we can
realize almost arbitrary deflection patterns by shaping the ponderomotive
potentials using a spatial light modulator. Our modulator is lossless,
programmable, has unity fill factor, and could pave the way to electron
wavefront shaping with hundreds of individually addressable pixels.


title: On the intrinsically flat cosmological models in a lattice

abstract:
  In this manuscript we investigate the intrinsically flat (space-flat)
spacetimes as viable cosmological models. We show that they have a natural
geometric structure which is suitable to describe inhomogeneous matter
distributions forming a periodic pattern throughout the space. We prove
theorems for their local representation and for existence and uniqueness of the
Einstein's equations with these periodic boundary conditions. We also find an
interesting class of exact solutions, which illustrates the applicability of
such spacetimes in cosmology, with an early time behavior close to homogeneity
and isotropy and a late time aspect with peaks and voids in the matter
distribution.


title:
Conditional entropic approach to nonequilibrium systems with weak
  fluctuation correlation

abstract:
  A conditional entropic approach is discussed for nonequilibrium complex
systems with a weak correlation between spatiotemporally fluctuating quantities
on a large time scale. The weak correlation is found to constitute the
fluctuation distribution that maximizes the entropy associated with the
conditional fluctuations. The approach is illustrated in diffusion phenomenon
of proteins inside bacteria.


title:
Threshold resummation at N$^{3}$LL accuracy and approximate N$^{3}$LO
  corrections to semi-inclusive DIS

abstract:
  We advance the threshold resummation formalism for semi-inclusive
deep-inelastic scattering (SIDIS) to next-to-next-to-next-to-leading
logarithmic (N$^{3}$LL) order, including the three-loop hard factor. We expand
the results in the strong coupling to obtain approximate
next-to-next-to-next-to-leading order (N$^{3}$LO) corrections for the SIDIS
cross section. In Mellin moment space, these corrections include all terms that
are logarithmically enhanced at threshold, or that are constant. We also
consider a set of corrections that are suppressed near threshold. Our numerical
estimates show modest changes of the cross section by the approximate N$^{3}$LO
terms, suggesting a very good perturbative stability of the SIDIS process.


title:
Dynamical Modeling and Control of Soft Robots with Non-constant
  Curvature Deformation

abstract:
  The Piecewise Constant Curvature (PCC) model is the most widely used soft
robotic modeling and control. However, the PCC fails to accurately describe the
deformation of the soft robots when executing dynamic tasks or interacting with
the environment. This paper presents a simple threedimensional (3D) modeling
method for a multi-segment soft robotic manipulator with non-constant curvature
deformation. We devise kinematic and dynamical models for soft manipulators by
modeling each segment of the manipulator as two stretchable links connected by
a universal joint. Based on that, we present two controllers for dynamic
trajectory tracking in confguration space and pose control in task space,
respectively. Model accuracy is demonstrated with simulations and experimental
data. The controllers are implemented on a four-segment soft robotic
manipulator and validated in dynamic motions and pose control with unknown
loads. The experimental results show that the dynamic controller enables a
stable reference trajectory tracking at speeds up to 7m/s.


title: Relative Pose from SIFT Features

abstract:
  This paper proposes the geometric relationship of epipolar geometry and
orientation- and scale-covariant, e.g., SIFT, features. We derive a new linear
constraint relating the unknown elements of the fundamental matrix and the
orientation and scale. This equation can be used together with the well-known
epipolar constraint to, e.g., estimate the fundamental matrix from four SIFT
correspondences, essential matrix from three, and to solve the semi-calibrated
case from three correspondences. Requiring fewer correspondences than the
well-known point-based approaches (e.g., 5PT, 6PT and 7PT solvers) for epipolar
geometry estimation makes RANSAC-like randomized robust estimation
significantly faster. The proposed constraint is tested on a number of problems
in a synthetic environment and on publicly available real-world datasets on
more than 80000 image pairs. It is superior to the state-of-the-art in terms of
processing time while often leading to more accurate results.


title:
DialogueNeRF: Towards Realistic Avatar Face-to-face Conversation Video
  Generation

abstract:
  Conversation is an essential component of virtual avatar activities in the
metaverse. With the development of natural language processing, textual and
vocal conversation generation has achieved a significant breakthrough.
Face-to-face conversations account for the vast majority of daily
conversations. However, this task has not acquired enough attention. In this
paper, we propose a novel task that aims to generate a realistic human avatar
face-to-face conversation process and present a new dataset to explore this
target. To tackle this novel task, we propose a new framework that utilizes a
series of conversation signals, e.g. audio, head pose, and expression, to
synthesize face-to-face conversation videos between human avatars, with all the
interlocutors modeled within the same network. Our method is evaluated by
quantitative and qualitative experiments in different aspects, e.g. image
quality, pose sequence trend, and naturalness of the rendering videos. All the
code, data, and models will be made publicly available.


title: Style Transformer for Image Inversion and Editing

abstract:
  Existing GAN inversion methods fail to provide latent codes for reliable
reconstruction and flexible editing simultaneously. This paper presents a
transformer-based image inversion and editing model for pretrained StyleGAN
which is not only with less distortions, but also of high quality and
flexibility for editing. The proposed model employs a CNN encoder to provide
multi-scale image features as keys and values. Meanwhile it regards the style
code to be determined for different layers of the generator as queries. It
first initializes query tokens as learnable parameters and maps them into W+
space. Then the multi-stage alternate self- and cross-attention are utilized,
updating queries with the purpose of inverting the input by the generator.
Moreover, based on the inverted code, we investigate the reference- and
label-based attribute editing through a pretrained latent classifier, and
achieve flexible image-to-image translation with high quality results.
Extensive experiments are carried out, showing better performances on both
inversion and editing tasks within StyleGAN.


title:
Threat Detection for General Social Engineering Attack Using Machine
  Learning Techniques

abstract:
  This paper explores the threat detection for general Social Engineering (SE)
attack using Machine Learning (ML) techniques, rather than focusing on or
limited to a specific SE attack type, e.g. email phishing. Firstly, this paper
processes and obtains more SE threat data from the previous Knowledge Graph
(KG), and then extracts different threat features and generates new datasets
corresponding with three different feature combinations. Finally, 9 types of ML
models are created and trained using the three datasets, respectively, and
their performance are compared and analyzed with 27 threat detectors and 270
times of experiments. The experimental results and analyses show that: 1) the
ML techniques are feasible in detecting general SE attacks and some ML models
are quite effective; ML-based SE threat detection is complementary with
KG-based approaches; 2) the generated datasets are usable and the SE domain
ontology proposed in previous work can dissect SE attacks and deliver the SE
threat features, allowing it to be used as a data model for future research.
Besides, more conclusions and analyses about the characteristics of different
ML detectors and the datasets are discussed.


title: Towards Distributed Coordination for Fog Platforms

abstract:
  Distributed fog and edge applications communicate over unreliable networks
and are subject to high communication delays. This makes using existing
distributed coordination technologies from cloud applications infeasible, as
they are built on the assumption of a highly reliable, low-latency datacenter
network to achieve strict consistency with low overheads. To help implement
configuration and state management for fog platforms and applications, we
propose a novel decentralized approach that lets systems specify coordination
strategies and membership for different sets of coordination data.


title: Fractal Interpolation over Nonlinear Partitions

abstract:
  This paper introduces the fractal interpolation problem defined over domains
with a nonlinear partition. This setting generalizes known methodologies
regarding fractal functions and provides a new holistic approach to fractal
interpolation. In this context, perturbations of nonlinear partition functions
are considered and sufficient conditions for the existence of a unique solution
of the underlying fractal interpolation problem for some classes of function
spaces are given.


title:
Investigation of the Perturbative Expansion of Moments of Heavy Quark
  Correlators for $N_f=0$

abstract:
  The QCD-coupling is a necessary input in the computation of many observables,
and the parametric error on input parameters can be a dominant source of
uncertainty. The coupling can be extracted by comparing high order perturbative
computations and lattice evaluated moments of mesonic two-point functions with
heavy quarks, which provide a high energy scale for perturbation theory. The
truncation of the perturbative series is an important systematic uncertainty.
We report on our attempt to study this issue by measuring pseudo-scalar
two-point functions in volumes of $L=2\, \text{fm}$ with twisted-mass Wilson
fermions in the quenched approximation. We use full twist, the non-perturbative
clover term and lattice spacings down to $a=0.015\,\text{fm}$ to tame the
sizable discretization effects. Our preliminary results indicate that either
higher order perturbative corrections or the continuum limit are not under
sufficient control despite our small lattice spacings and quark masses
extending beyond $2\,m_{\text{charm}}$.


title: Edge-based Local Push for Personalized PageRank

abstract:
  Personalized PageRank (PPR) is a popular node proximity metric in graph
mining and network research. Given a graph G=(V,E) and a source node $s \in V$,
a single-source PPR (SSPPR) query asks for the PPR value $\vpi(u)$ with respect
to s, which represents the relative importance of node u in the context of the
source node s. Among existing algorithms for SSPPR queries, LocalPush is a
fundamental method which serves as a cornerstone for subsequent algorithms. In
LocalPush, a push operation is a crucial primitive operation, which distributes
the probability at a node u to ALL u's neighbors via the corresponding edges.
Although this push operation works well on unweighted graphs, unfortunately, it
can be rather inefficient on weighted graphs. In particular, on unbalanced
weighted graphs where only a few of these edges take the majority of the total
weight among them, the push operation would have to distribute insignificant
probabilities along those edges which just take the minor weights, resulting in
expensive overhead.
  To resolve this issue, we propose the EdgePush algorithm, a novel method for
computing SSPPR queries on weighted graphs. EdgePush decomposes the
aforementioned push operations in edge-based push, allowing the algorithm to
operate at the edge level granularity. Hence, it can flexibly distribute the
probabilities according to edge weights. Furthermore, our EdgePush allows a
fine-grained termination threshold for each individual edge, leading to a
superior complexity over LocalPush. Notably, we prove that EdgePush improves
the theoretical query cost of LocalPush by an order of up to O(n) when the
graph's weights are unbalanced, both in terms of $\ell_1$-error and normalized
additive error. Our experimental results demonstrate that EdgePush
significantly outperforms state-of-the-art baselines in terms of query
efficiency on large motif-based and real-world weighted graphs.


title:
The population of hot subdwarf stars studied with Gaia -- IV. Catalogues
  of hot subluminous stars based on Gaia EDR3

abstract:
  In light of substantial new discoveries of hot subdwarfs by ongoing
spectroscopic surveys and the availability of the Gaia mission Early Data
Release 3 (EDR3), we compiled new releases of two catalogues of hot subluminous
stars: The data release 3 (DR3) catalogue of the known hot subdwarf stars
contains 6,616 unique sources and provides multi-band photometry, and
astrometry from Gaia EDR3 as well as classifications based on spectroscopy and
colours. This is an increase of 742 objects over the DR2 catalogue. This new
catalogue provides atmospheric parameters for 3,087 stars and radial velocities
for 2,791 stars from the literature. In addition, we have updated the Gaia Data
Release 2 (DR2) catalogue of hot subluminous stars using the improved accuracy
of the Gaia EDR3 data set together with updated quality and selection criteria
to produce the Gaia EDR3 catalogue of 61,585 hot subluminous stars,
representing an increase of 21,785 objects. The improvements in Gaia EDR3
astrometry and photometry compared to Gaia DR2 have enabled us to define more
sophisticated selection functions. In particular, we improved hot subluminous
star detection in the crowded regions of the Galactic plane as well as in the
direction of the Magellanic Clouds by including sources with close apparent
neighbours but with flux levels that dominate the neighbourhood.


title: Strategic Thinking for Sustainable Practice-Centered Computing

abstract:
  This book was a trigger for me to reflect on all the projects we have
conducted at the local, regional, national, and European levels around
healthcare, addressing the social isolation of elderlies, the social support
among caregivers, and the coordination issues of professionals willing to take
care for patients at their home. Since Ina Wagner interviewed me in Paris, the
situation evolved in France as a new healthcare law was voted that emphasizes
the importance of cooperation among healthcare practitioners, the need for IT
support, and the key role of local territories (which is not obvious in France,
that is a very centralized country). The reflection that started with this book
definitely helped me to make decisions in this evolving context, and I am
grateful to Claudia M{\"u}ller who, through an interview, facilitated me to
realize what are, from my experience, the main issues that have to be dealt
with when aiming at ensuring a sustainable practice-based computing approach.
In this epilogue, I briefly list and illustrate these main issues, hoping they
could support other sustainable experiences.


title: Quantum effects in an expanded Black-Scholes model

abstract:
  The limitations of the classical Black-Scholes model are examined by
comparing calculated and actual historical prices of European call options on
stocks from several sectors of the S&P 500. Persistent differences between the
two prices point to an expanded model proposed by Segal and Segal (1998) in
which information not simultaneously observable or actionable with public
information can be represented by an additional pseudo-Wiener process. A real
linear combination of the original and added processes leads to a commutation
relation analogous to that between a boson field and its canonical momentum in
quantum field theory. The resulting pricing formula for a European call option
replaces the classical volatility with the norm of a complex quantity, whose
imaginary part is shown to compensate for the disparity between prices obtained
from the classical Black-Scholes model and actual prices of the test call
options. This provides market evidence for the influence of a non-classical
process on the price of a security based on non-commuting operators.


title: Reachability In Simple Neural Networks

abstract:
  We investigate the complexity of the reachability problem for (deep) neural
networks: does it compute valid output given some valid input? It was recently
claimed that the problem is NP-complete for general neural networks and
specifications over the input/output dimension given by conjunctions of linear
inequalities. We recapitulate the proof and repair some flaws in the original
upper and lower bound proofs. Motivated by the general result, we show that
NP-hardness already holds for restricted classes of simple specifications and
neural networks. Allowing for a single hidden layer and an output dimension of
one as well as neural networks with just one negative, zero and one positive
weight or bias is sufficient to ensure NP-hardness. Additionally, we give a
thorough discussion and outlook of possible extensions for this direction of
research on neural network verification.


title:
Pre-threshold fractional susceptibility function: holomorphy and
  response formula

abstract:
  For certain smooth unimodal families with negative Schwarzian derivative, we
construct a set of Collet-Eckmann and subexponentially recurrent parameters
$\Omega$, whose complement set has sufficiently fast decaying density, on which
exponential mixing with uniform rates occurs. We use this construction to
establish holomorphy of the true fractional susceptibility function of the
logistic family, in a disk of radius larger than one, for differentiation index
$0\le\eta<1/2$, as recently conjectured by Baladi and Smania. We also obtain a
fractional response formula.


title: The Physics of Light Relics

abstract:
  Many well-motivated extensions of the Standard Model predict the existence of
new light species that may have been produced in the early universe. Prominent
examples include axions, sterile neutrinos, gravitinos, dark photons, and more.
The gravitational influence of light relics leaves imprints in the cosmic
microwave background fluctuations, the large-scale structure of the universe
and the primordial element abundances. In this paper, we detail the physics of
cosmological light relics, and describe how measurements of their relic density
and mass serve as probes of physics beyond the Standard Model. A measurement of
the light relic density at the precision of upcoming cosmological surveys will
point the way toward new physics or severely constrain the range of viable
extensions to the Standard Model.


title: Measurement of $A_{LR}$ using radiative return at ILC 250

abstract:
  For the precision study at the ILC 250, measurement of $A_{LR}$ is important
as it can constrain SMEFT parameters. The current best measured $A_{LR}$ value
is $A_{LR} = 0.1514 \pm 0.0019\,(stat) \pm 0.0011\,(syst)$ which was measured
at the SLC, and a more precise value is required for the global fit for the new
physics search in TeV-scale. At the ILC, we can use the $e^+ e^- \to \gamma Z$
process to evaluate the $A_{LR}$. We performed a full simulation study of the
$e^+ e^- \to \gamma Z$ process at the center-of-mass energy of 250 GeV and
evaluated how much we can improve the precision of this observable. The
statistical error on $A_{LR}$ at the ILC 250 turned out to be $1.8 \times
10^{-4}$. Major source of the systematic error was error from the beam
polarization. As other sources of the systematic error, the uncorrelated parts
of error on the product of luminosity and selection efficiency for each
polarization combination contribute. Including those systematic errors, total
absolute error on $A_{LR}$ was estimated to be 0.00025, 8.8 times better
precision than that from the SLC (0.00219).


title:
The nuclear-spin-forbidden rovibrational transitions of water from first
  principles

abstract:
  The water molecule occurs in two nuclear-spin isomers that differ by the
value of the total nuclear spin of the hydrogen atoms, i.e., $I=0$ for
para-H$_2$O and $I=1$ for ortho-H$_2$O. Spectroscopic transitions between
rovibrational states of ortho and para water are extremely weak due to the tiny
hyperfine nuclear-spin-rotation interaction of only $\sim30$ kHz and so far
were not observed. We report the first comprehensive theoretical investigation
of the hyperfine effects and ortho-para transitions in H$_2$$^{16}$O due to
nuclear-spin-rotation and spin-spin interactions. We also present the details
of our newly developed general variational approach to the simulation of
hyperfine effects in polyatomic molecules. Our results for water suggest that
the strongest ortho-para transitions with room-temperature intensities on the
order of $10^{-31}$ cm/molecule are about an order of magnitude larger than
previously predicted values and should be detectable in the mid-infrared
$\nu_2$ and near-infrared $2\nu_1+\nu_2$ and $\nu_1+\nu_2+\nu_3$ bands by
current spectroscopy experiments.


title: Snowmass2021 Theory Frontier White Paper: Data-Driven Cosmology

abstract:
  Over the past few decades, astronomical and cosmological data sets firmly
established the existence of physics beyond the Standard Model of particle
physics by providing strong evidence for the existence of dark matter, dark
energy, and non-zero neutrino mass. In addition, the generation of primordial
perturbations most likely also relies on physics beyond the Standard Model of
particle physics. Theory work, ranging from models of the early universe in
string theory that that led to novel phenomenological predictions to the
development of effective field theories to large-scale cosmological simulations
that include the physics of galaxy evolution, has played a key role in
analyzing and interpreting these data sets and in suggesting novel paths
forward to isolate the origins of this new physics. Over the next decade, even
more sensitive surveys are beginning to take data and are being planned. In
this white paper, we describe key areas of the theory program that will be
needed to optimize the physics return on investment from these new
observational opportunities.


title: NINNs: Nudging Induced Neural Networks

abstract:
  New algorithms called nudging induced neural networks (NINNs), to control and
improve the accuracy of deep neural networks (DNNs), are introduced. The NINNs
framework can be applied to almost all pre-existing DNNs, with forward
propagation, with costs comparable to existing DNNs. NINNs work by adding a
feedback control term to the forward propagation of the network. The feedback
term nudges the neural network towards a desired quantity of interest. NINNs
offer multiple advantages, for instance, they lead to higher accuracy when
compared with existing data assimilation algorithms such as nudging. Rigorous
convergence analysis is established for NINNs. The algorithmic and theoretical
findings are illustrated on examples from data assimilation and chemically
reacting flows.


title:
An Ultra-Compact Single FeFET Binary and Multi-Bit Associative Search
  Engine

abstract:
  Content addressable memory (CAM) is widely used in associative search tasks
for its highly parallel pattern matching capability. To accommodate the
increasingly complex and data-intensive pattern matching tasks, it is critical
to keep improving the CAM density to enhance the performance and area
efficiency. In this work, we demonstrate: i) a novel ultra-compact 1FeFET CAM
design that enables parallel associative search and in-memory hamming distance
calculation; ii) a multi-bit CAM for exact search using the same CAM cell; iii)
compact device designs that integrate the series resistor current limiter into
the intrinsic FeFET structure to turn the 1FeFET1R into an effective 1FeFET
cell; iv) a successful 2-step search operation and a sufficient sensing margin
of the proposed binary and multi-bit 1FeFET1R CAM array with sizes of practical
interests in both experiments and simulations, given the existing unoptimized
FeFET device variation; v) 89.9x speedup and 66.5x energy efficiency
improvement over the state-of-the art alignment tools on GPU in accelerating
genome pattern matching applications through the hyperdimensional computing
paradigm.


title: Generating Privacy-Preserving Process Data with Deep Generative Models

abstract:
  Process data with confidential information cannot be shared directly in
public, which hinders the research in process data mining and analytics. Data
encryption methods have been studied to protect the data, but they still may be
decrypted, which leads to individual identification. We experimented with
different models of representation learning and used the learned model to
generate synthetic process data. We introduced an adversarial generative
network for process data generation (ProcessGAN) with two Transformer networks
for the generator and the discriminator. We evaluated ProcessGAN and
traditional models on six real-world datasets, of which two are public and four
are collected in medical domains. We used statistical metrics and supervised
learning scores to evaluate the synthetic data. We also used process mining to
discover workflows for the authentic and synthetic datasets and had medical
experts evaluate the clinical applicability of the synthetic workflows. We
found that ProcessGAN outperformed traditional sequential models when trained
on small authentic datasets of complex processes. ProcessGAN better represented
the long-range dependencies between the activities, which is important for
complicated processes such as the medical processes. Traditional sequential
models performed better when trained on large data of simple processes. We
conclude that ProcessGAN can generate a large amount of sharable synthetic
process data indistinguishable from authentic data.


title:
On some properties of the curl operator and their consequences for the
  Navier-Stokes system

abstract:
  We investigate some geometric properties of the $\operatorname{curl}$
operator, based on its diagonalizationand its expression as a non-local
symmetry of the pseudo-derivative $(-\Delta)^{1/2}$ among divergence-free
vector fieldswith finite energy. In this context, we introduce the notion of
spin-definite fields, i.e. eigenvectorsof
$(-\Delta)^{-1/2}\operatorname{curl}$.The two spin-definite components of a
general 3D incompressible flow untangle the right-handed motion from the
left-handed one. Having observed that the non-linearity of Navier-Stokes has
the structure of a cross-productand its weak (distributional) form is a
determinant that involves the vorticity, the velocity and a test function,we
revisit the conservation of energy and the balance of helicity in a geometrical
fashion. We show that in the caseof a finite-time blow-up, both spin-definite
components of the flow will explose simultaneously and with equal rates,i.e.
singularities in 3D are the result of a conflict of spin, which is impossible
in the poorer geometry of 2D flows.We investigate the role of the local and
non-local determinants $$\int_0^T\int_{\mathbb{R}^3}\det(\operatorname{curl} u,
u, (-\Delta)^{\theta} u)$$ and their spin-definite counterparts, which drive
the enstrophy and, more generally, are responsible forthe regularity of the
flow and the emergence of singularities or quasi-singularities.As such, they
are at the core of turbulence phenomena.


title:
Eruptivity Criteria for Two-dimensional Magnetic Flux Ropes in the Solar
  Corona

abstract:
  We apply the magneto-frictional approach to investigate which quantity or
quantities can best predict the loss of equilibrium of a
translationally-invariant magnetic flux rope. The flux rope is produced
self-consistently by flux cancellation combined with gradual footpoint shearing
of a coronal arcade which is open at the outer boundary. This models the
magnetic field in decaying active regions on the Sun. Such a model permits two
types of eruption: episodic small events caused by shearing and relaxation of
the overlying arcade, and major eruptions of the main low-lying coronal flux
rope. Through a parameter study, we find that the major eruptions are best
predicted not by individual quantities but by thresholds in the ratios of
squared rope current to either magnetic energy or relative magnetic helicity.
We show how to appropriately define the latter quantity for
translationally-invariant magnetic fields, along with a related eruptivity
index that has recently been introduced for three-dimensional magnetic fields.
In contrast to previous configurations studied, we find that the eruptivity
index has only a weak predictive skill, and in fact is lower prior to eruption,
rather than higher. This is because the overlying background magnetic field has
the same direction as the arcade itself. Thus we propose that there are a whole
class of solar eruptions that cannot be predicted by a high eruptivity index.


title: Causal and self-dual morphisms in four complex dimensions

abstract:
  We define a class of maps between holomorphically embedded null curves which
generalize conformal transformations, and can be defined in any complex
dimension. In four dimensions, we can also define a similar map between
self-dual surfaces, which generalize flat $\alpha$-planes. These maps are
respectively called causal and self-dual morphisms. It is shown that there
exist an infinite class of non-trivial examples for both types of maps in four
dimensions.


title: Reproducibility and Performance: Why Choose?

abstract:
  Research processes often rely on high-performance computing (HPC), but HPC is
often seen as antithetical to "reproducibility": one would have to choose
between software that achieves high performance, and software that can be
deployed in a reproducible fashion. However, by giving up on reproducibility we
would give up on verifiability, a foundation of the scientific process. How can
we conciliate performance and reproducibility? This article looks at two
performance-critical aspects in HPC: message passing (MPI) and CPU
micro-architecture tuning. Engineering work that has gone into performance
portability has already proved fruitful, but some areas remain unaddressed when
it comes to CPU tuning. We propose package multi-versioning, a technique
developed for GNU Guix, a tool for reproducible software deployment, and show
that it allows us to implement CPU tuning without compromising on
reproducibility and provenance tracking.


title:
Sub-40nm Nanogratings Self-Organized in PVP-based Polymer Composite Film
  by Photoexcitation and Two Sequent Splitting under Femtosecond Laser
  Irradiation

abstract:
  Laser-induced periodic surface structures (LIPSSs) on various materials have
been extensively investigated because of their wide applications. The
combination of different materials allows for greater freedom in tailoring
their functions and achieving responses not possible in a homogeneous material.
By utilizing a femtosecond (fs) laser to irradiate the Fe-doped Polyvinyl
Pyrrolidone (PVP) composite film, highly regular ultrafine nanogratings
(U-nanogratings) with a period as small as 35.0 ($\pm$ 2.0) nm can be
self-organized on the surface with extremely high efficiency. The period of the
U-nanogratings can be controlled by varying the scanning speed of the laser
beam (deposited energy) and the thickness of the composite film. Based on the
experimental, theoretical, and simulation results, we propose a two-step
formation mechanism: composite film excitation and two sequent
grating-splitting. The high photosensitivity and low glass transition
temperature of the composite film facilitate the fabrication of the ultrafine
nanostructures. The proposed design method for the composite material and
fabrication process could not only provide a strategy for obtaining highly
regular U-nanogratings, but also offer a platform to explore the interaction
physics between ultra-short pulses and matter under extreme conditions.


title: Anharmonic Lattice Dynamics in Sodium Ion Conductors

abstract:
  We employ THz-range temperature-dependent Raman spectroscopy and
first-principles lattice-dynamical calculations to show that the undoped sodium
ion conductors Na$_3$PS$_4$ and isostructural Na$_3$PSe$_4$ both exhibit
anharmonic lattice dynamics. The anharmonic effects in the compounds involve
coupled host lattice -- Na$^+$ ion dynamics that drive the tetragonal-to-cubic
phase transition in both cases, but with a qualitative difference in the
anharmonic character of the transition. Na$_3$PSe$_4$ shows almost purely
displacive character with the soft modes disappearing in the cubic phase as the
change of symmetry shifts these modes to the Raman-inactive Brillouin zone
boundary. Na$_3$PS$_4$ instead shows order-disorder character in the cubic
phase, with the soft modes persisting through the phase transition and
remaining active in Raman in the cubic phase, violating Raman selection rules
for that phase. Our findings highlight the important role of coupled host
lattice -- mobile ion dynamics in vibrational instabilities that are coincident
with the exceptional conductivity in these Na$^+$ ion conductors.


title:
ExHaLe-jet: An extended hadro-leptonic jet model for blazars. I. Code
  description and initial results

abstract:
  The processes operating in blazar jets are still an open question. Modeling
the radiation emanating from an extended part of the jet allows one to capture
these processes on all scales. Kinetic codes solving the Fokker-Planck equation
along the jet flow are well suited to this task, as they can efficiently derive
the radiation and particle spectra without the need for computationally
demanding plasma-physical simulations. Here, we present a new extended
hadro-leptonic jet code -- ExHaLe-jet -- which considers simultaneously the
processes of relativistic protons and electrons. Within a pre-set geometry and
bulk flow, the particle evolution is derived self-consistently. Highly
relativistic secondary electrons (and positrons) are created through
$\gamma$-$\gamma$ pair production, Bethe-Heitler pair production, and pion/muon
decay. These secondaries are entrained in the jet flow decreasing the ratio of
protons to electrons with distance from the jet base. For particle-photon
interactions, we consider all internal and many external photon fields, such as
the accretion disk, broad-line region, and the dusty torus. The external fields
turn out to be the most important source for particle-photon interactions
governing the resulting photon and neutrino spectra. In this paper, we present
the code and an initial parameter study, while in follow-up works we present
extensions of the code and more specific applications.


title: Soft-Theorems for Scalar Particles: The Dilatons Story

abstract:
  We summarize recent results regarding single and double soft theorems of two
different particles named dilatons, the Nambu-Goldstone boson of the
spontaneously broken conformal field theories and the massless scalar particle
of the closed string theories. Similarities and differences between the soft
theorems of these two particles are discussed as well as their connections with
the symmetries of the theories.


title: Transition matrices binding Carter diagrams

abstract:
  In 1972, R.Carter introduced the so-called admissible diagrams representing
semi-Coxeter elements, which, together with Coxeter elements, give a complete
list of conjugacy classes in the Weyl group. Carter diagrams differ from the
admissible ones in that they take into account the sign of the Tits bilinear
form on the pair of roots; for this, the language of ($solid~and~dotted$) edges
is used. These diagrams are characterized by the presence of subdiagrams, which
are 4-cycles. The Carter diagrams of the same $ADE~$ type and the same size are
called $homogeneous$. Let $\widetilde\Gamma$ and $\Gamma$ be homogeneous Carter
diagrams, where $\Gamma$ is a Dynkin diagram. The transition matrix $M_I$
between $\widetilde\Gamma$ and $\Gamma$ in composition with the Weyl group
binds arbitrary root subsets associated with $\widetilde\Gamma$ and $\Gamma$.
The homogeneous Carter diagrams form a chain of diagrams in which root subsets
for each successive diagram is obtained (using $M_I$) from the root subsets for
previous one in some canonical way, affecting exactly one root, so that this
root is mapped to the minimal element in some root subsystem. It is shown that
all root subsets associated with the given Carter diagram are conjugate under
Weyl group. It is observed a numerical relation between enhanced Dynkin
diagrams (introduced by Dynkin-Minchenko in [DM10]) and Carter diagrams. In
this regard, a number of facts and conjectures are discussed in the last
sections.


title: Coorbit spaces and dual molecules: The quasi-Banach case

abstract:
  This paper provides a self-contained exposition of coorbit spaces associated
with integrable group representations and quasi-Banach function spaces. It
extends the theory in [Studia Math., 180(3):237-253, 2007] to locally compact
groups that do not necessarily possess a compact, conjugation-invariant unit
neighborhood. Furthermore, the present paper establishes the existence of dual
molecules of frames and Riesz sequences as in [J. Funct. Anal., 280(10):56,
2021] for the setting of quasi-Banach spaces. To ensure the direct
applicability to various well-studied examples, the theory is developed for
possibly projective and reducible unitary representations.


title:
Investigating self-supervised learning for speech enhancement and
  separation

abstract:
  Speech enhancement and separation are two fundamental tasks for robust speech
processing. Speech enhancement suppresses background noise while speech
separation extracts target speech from interfering speakers. Despite a great
number of supervised learning-based enhancement and separation methods having
been proposed and achieving good performance, studies on applying
self-supervised learning (SSL) to enhancement and separation are limited. In
this paper, we evaluate 13 SSL upstream methods on speech enhancement and
separation downstream tasks. Our experimental results on Voicebank-DEMAND and
Libri2Mix show that some SSL representations consistently outperform baseline
features including the short-time Fourier transform (STFT) magnitude and log
Mel filterbank (FBANK). Furthermore, we analyze the factors that make existing
SSL frameworks difficult to apply to speech enhancement and separation and
discuss the representation properties desired for both tasks. Our study is
included as the official speech enhancement and separation downstreams for
SUPERB.


title:
Amortised inference of fractional Brownian motion with linear
  computational complexity

abstract:
  We introduce a simulation-based, amortised Bayesian inference scheme to infer
the parameters of random walks. Our approach learns the posterior distribution
of the walks' parameters with a likelihood-free method. In the first step a
graph neural network is trained on simulated data to learn optimized
low-dimensional summary statistics of the random walk. In the second step an
invertible neural network generates the posterior distribution of the
parameters from the learnt summary statistics using variational inference. We
apply our method to infer the parameters of the fractional Brownian motion
model from single trajectories. The computational complexity of the amortized
inference procedure scales linearly with trajectory length, and its precision
scales similarly to the Cram{\'e}r-Rao bound over a wide range of lengths. The
approach is robust to positional noise, and generalizes well to trajectories
longer than those seen during training. Finally, we adapt this scheme to show
that a finite decorrelation time in the environment can furthermore be inferred
from individual trajectories.

categories: cs.LG physics.bio-ph physics.data-an q-bio.QM


title: Automated Design Approximation to Overcome Circuit Aging

abstract:
  Transistor aging phenomena manifest themselves as degradations in the main
electrical characteristics of transistors. Over time, they result in a
significant increase of cell propagation delay, leading to errors due to timing
violations, since the operating frequency becomes unsustainable as the circuit
ages. Conventional techniques employ timing guardbands to mitigate
aging-induced delay increase, which leads to considerable performance losses
from the beginning of the circuit's lifetime. Leveraging the inherent error
resilience of a vast number of application domains, approximate computing was
recently introduced as an aging mitigation mechanism. In this work, we present
the first automated framework for generating aging-aware approximate circuits.
Our framework, by applying directed gate-level netlist approximation, induces a
small functional error and recovers the delay degradation due to aging. As a
result, our optimized circuits eliminate aging-induced timing errors.
Experimental evaluation over a variety of arithmetic circuits and image
processing benchmarks demonstrates that for an average error of merely
$5\times10^{-3}$, our framework completely eliminates aging-induced timing
guardbands. Compared to the respective baseline circuits without timing
guardbands (i.e., iso-performance evaluation), the error of the circuits
generated by our framework is $1208$x smaller.


title: On the spherical clothoid

abstract:
  We revisit a nonlinear spline primitive for 3-space first studied by Even
Mehlum. It is the spherical clothoid, the spherical curve with geodesic
curvature a linear function of arc length. We present its Cartesian coordinate
functions using confluent hypergeometric functions (the Kummer functions) and
its stereographic projection onto the complex plane. New Humbert series results
are also presented along with generating function formulas related to the
associated Meixner-Pollaczek polynomials.


title: Simulated Detector Performance at the Muon Collider

abstract:
  In this paper we report on the current status of studies on the expected
performance for a detector designed to operate in a muon collider environment.
Beam-induced backgrounds (BIB) represent the main challenge in the design of
the detector and the event reconstruction algorithms. The current detector
design aims to show that satisfactory performance can be achieved, while
further optimizations are expected to significantly improve the overall
performance. We present the characterization of the expected beam-induced
background, describe the detector design and software used for detailed event
simulations taking into account BIB effects. The expected performance of
charged-particle reconstruction, jets, electrons, photons and muons is
discussed, including an initial study on heavy-flavor jet tagging. A simple
method to measure the delivered luminosity is also described. Overall, the
proposed design and reconstruction algorithms can successfully reconstruct the
high transverse-momentum objects needed to carry out a broad physics program.


title:
Supporting multiple entanglement flows through a continuous-variable
  quantum repeater

abstract:
  Quantum repeaters are critical to the development of quantum networks,
enabling rates of entanglement distribution beyond those attainable by direct
transmission. We consider multiple continuous-variable, squeezed light-based
entanglement flows through a repeater involving noiseless linear amplification
and dual homodyne detection. By analyzing a single-repeater-enhanced channel
model with asymmetric losses across the repeater, we determine optimal
placements of the central repeater hub in a 4-user hub-and-spoke network such
that the rate of each entanglement flow through the hub is enhanced.


title: Learning Expanding Graphs for Signal Interpolation

abstract:
  Performing signal processing over graphs requires knowledge of the underlying
fixed topology. However, graphs often grow in size with new nodes appearing
over time, whose connectivity is typically unknown; hence, making more
challenging the downstream tasks in applications like cold start
recommendation. We address such a challenge for signal interpolation at the
incoming nodes blind to the topological connectivity of the specific node.
Specifically, we propose a stochastic attachment model for incoming nodes
parameterized by the attachment probabilities and edge weights. We estimate
these parameters in a data-driven fashion by relying only on the attachment
behaviour of earlier incoming nodes with the goal of interpolating the signal
value. We study the non-convexity of the problem at hand, derive conditions
when it can be marginally convexified, and propose an alternating projected
descent approach between estimating the attachment probabilities and the edge
weights. Numerical experiments with synthetic and real data dealing in cold
start collaborative filtering corroborate our findings.


title: Intrinsic Neural Fields: Learning Functions on Manifolds

abstract:
  Neural fields have gained significant attention in the computer vision
community due to their excellent performance in novel view synthesis, geometry
reconstruction, and generative modeling. Some of their advantages are a sound
theoretic foundation and an easy implementation in current deep learning
frameworks. While neural fields have been applied to signals on manifolds,
e.g., for texture reconstruction, their representation has been limited to
extrinsically embedding the shape into Euclidean space. The extrinsic embedding
ignores known intrinsic manifold properties and is inflexible wrt. transfer of
the learned function. To overcome these limitations, this work introduces
intrinsic neural fields, a novel and versatile representation for neural fields
on manifolds. Intrinsic neural fields combine the advantages of neural fields
with the spectral properties of the Laplace-Beltrami operator. We show
theoretically that intrinsic neural fields inherit many desirable properties of
the extrinsic neural field framework but exhibit additional intrinsic
qualities, like isometry invariance. In experiments, we show intrinsic neural
fields can reconstruct high-fidelity textures from images with state-of-the-art
quality and are robust to the discretization of the underlying manifold. We
demonstrate the versatility of intrinsic neural fields by tackling various
applications: texture transfer between deformed shapes & different shapes,
texture reconstruction from real-world images with view dependence, and
discretization-agnostic learning on meshes and point clouds.


title:
Pseudo standard entanglement structure cannot be distinguished from
  standard entanglement structure

abstract:
  An experimental verification of the maximally entangled state ensures that
the constructed state is close to the maximally entangled state, but it does
not guarantee that the state is exactly the same as the maximally entangled
state. Further, the entanglement structure is not uniquely determined in
general probabilistic theories even if we impose reasonable postulate about
local systems. Therefore, the existence of the maximally entangled state
depends on whether the standard entanglement structure is valid. To examine
this issue, we introduce pseudo standard entanglement structure as a structure
of quantum composite system under natural assumptions based on the
repeatability of measurement processing and local unitary symmetry.
Surprisingly, there exist infinitely many pseudo standard entanglement
structures different from the standard entanglement structure. In addition, we
show that any maximally entangled state can be arbitrarily approximated by an
entangled state that belongs to our obtained pseudo standard entanglement
structure. That is, experimental verification does not exclude the possibility
of our obtained pseudo standard entanglement structure that is different from
the standard entanglement structure.


title:
PDNS-Net: A Large Heterogeneous Graph Benchmark Dataset of Network
  Resolutions for Graph Learning

abstract:
  In order to advance the state of the art in graph learning algorithms, it is
necessary to construct large real-world datasets. While there are many
benchmark datasets for homogeneous graphs, only a few of them are available for
heterogeneous graphs. Furthermore, the latter graphs are small in size
rendering them insufficient to understand how graph learning algorithms perform
in terms of classification metrics and computational resource utilization. We
introduce, PDNS-Net, the largest public heterogeneous graph dataset containing
447K nodes and 897K edges for the malicious domain classification task.
Compared to the popular heterogeneous datasets IMDB and DBLP, PDNS-Net is 38
and 17 times bigger respectively. We provide a detailed analysis of PDNS-Net
including the data collection methodology, heterogeneous graph construction,
descriptive statistics and preliminary graph classification performance. The
dataset is publicly available at https://github.com/qcri/PDNS-Net. Our
preliminary evaluation of both popular homogeneous and heterogeneous graph
neural networks on PDNS-Net reveals that further research is required to
improve the performance of these models on large heterogeneous graphs.


title: Spin Cross-Correlation Experiments in an Electron Entangler

abstract:
  Correlations are fundamental in describing many body systems - not only in
natural sciences. However, in experiments, correlations are notoriously
difficult to assess on the microscopic scale, especially for electron spins.
Here, we demonstrate a direct measurement of the spin cross-correlations
between the currents of a Cooper pair splitter, an electronic device that emits
electrons originating from Cooper pairs in a superconductor. While it is firmly
established theoretically that these electron pairs form maximally
spin-entangled singlet states with opposite spin projections, no spin
correlation experiments have been demonstrated so far. We use ferromagnetic
sidegates, compatible with superconducting electronic structures, to
individually spin polarize the transmissions of two quantum dots fabricated in
the two electronic paths, which act as tunable spin filters. The signals are
detected in standard transport and in highly sensitive transconductance
experiments. We find that the spin-cross correlation is negative, compatible
with spin singlet emission, and deviates from the ideal value mostly due to a
finite overlap of the Zeeman split quantum dot states. Our results demonstrate
a new route to perform spin auto- and cross correlation experiments in
nanometer scaled electronic devices, especially suitable for those relying on
magnetic field sensitive superconducting elements, like unconventional, triplet
or topologically non-trivial superconductors, or to perform Bell tests with
massive particles, like electrons.


title: Experimental observation of ABCB stacked tetralayer graphene

abstract:
  In tetralayer graphene, three inequivalent layer stackings should exist,
however, only rhombohedral (ABCA) and Bernal (ABAB) stacking have so far been
observed. The three stacking sequences differ in their electronic structure,
with the elusive third stacking (ABCB) being unique as it is predicted to
exhibit an intrinsic bandgap as well as locally flat bands around the K points.
Within our random phase approximation, these flat bands are predicted to
promote correlated phenomena such as magnetic and unconventional
superconducting order. Here, we use scattering-type scanning near-field optical
microscopy and confocal Raman microscopy to identify and characterize domains
of ABCB stacked tetralayer graphene. We reveal unique optical fingerprints of
all three stacking sequences by addressing their infrared conductivities
between 0.28 and 0.56 eV using amplitude and phase-resolved near-field
nano-spectroscopy. Comparison with results from tight-binding calculations are
in good quantitative agreement and allow for unambiguous assignments.


title:
Detection of Early-Universe Gravitational Wave Signatures and
  Fundamental Physics

abstract:
  Detection of a gravitational-wave signal of non-astrophysical origin would be
a landmark discovery, potentially providing a significant clue to some of our
most basic, big-picture scientific questions about the Universe. In this white
paper, we survey the leading early-Universe mechanisms that may produce a
detectable signal -- including inflation, phase transitions, topological
defects, as well as primordial black holes -- and highlight the connections to
fundamental physics. We review the complementarity with collider searches for
new physics, and multimessenger probes of the large-scale structure of the
Universe.


title: MOBDrone: a Drone Video Dataset for Man OverBoard Rescue

abstract:
  Modern Unmanned Aerial Vehicles (UAV) equipped with cameras can play an
essential role in speeding up the identification and rescue of people who have
fallen overboard, i.e., man overboard (MOB). To this end, Artificial
Intelligence techniques can be leveraged for the automatic understanding of
visual data acquired from drones. However, detecting people at sea in aerial
imagery is challenging primarily due to the lack of specialized annotated
datasets for training and testing detectors for this task. To fill this gap, we
introduce and publicly release the MOBDrone benchmark, a collection of more
than 125K drone-view images in a marine environment under several conditions,
such as different altitudes, camera shooting angles, and illumination. We
manually annotated more than 180K objects, of which about 113K man overboard,
precisely localizing them with bounding boxes. Moreover, we conduct a thorough
performance analysis of several state-of-the-art object detectors on the
MOBDrone data, serving as baselines for further research.


title:
Plancherel Measures of Reductive Adelic Groups and Von Neumann
  Dimensions

abstract:
  We first point out a connection between the Plancherel measure of a general
unimodular group of type I and the von Neumann dimensions of its lattices.
Given a reductive algebraic group $G$ over a number field $F$, we describe the
Plancherel measure $\nu_{G(\mathbb{A}_F)}$ on the unitary dual
$\widehat{G(\mathbb{A}_F)}$ of the adelic group $G(\mathbb{A}_F)$. For any
subset $X$ of $\widehat{G(\mathbb{A}_F)}$ with a finite measure, there is
naturally a representation $H_X$ of $G(\mathbb{A}_F)$ given by the direct
integral of irreducible ones in $X$. Here we consider the dimension of this
module over the von Neumann algebra $\mathcal{L}(G(F))$ of the discrete group
$G(F)$. If $G$ is semi-simple and simply-connected, we prove
$\dim_{\mathcal{L}(G(F))}(H_X)=\nu_{G(\mathbb{A}_F)}(X)$, once we take the
canonical Tamagawa measure on $G(\mathbb{A}_F)$.


title:
Categorical Representation Learning and RG flow operators for
  algorithmic classifiers

abstract:
  Following the earlier formalism of the categorical representation learning
(arXiv:2103.14770) by the first two authors, we discuss the construction of the
"RG-flow based categorifier". Borrowing ideas from theory of renormalization
group flows (RG) in quantum field theory, holographic duality, and hyperbolic
geometry, and mixing them with neural ODE's, we construct a new algorithmic
natural language processing (NLP) architecture, called the RG-flow categorifier
or for short the RG categorifier, which is capable of data classification and
generation in all layers. We apply our algorithmic platform to biomedical data
sets and show its performance in the field of sequence-to-function mapping. In
particular we apply the RG categorifier to particular genomic sequences of flu
viruses and show how our technology is capable of extracting the information
from given genomic sequences, find their hidden symmetries and dominant
features, classify them and use the trained data to make stochastic prediction
of new plausible generated sequences associated with new set of viruses which
could avoid the human immune system. The content of the current article is part
of the recent US patent application submitted by first two authors (U.S. Patent
Application No.: 63/313.504).

categories: cs.LG cond-mat.dis-nn cs.AI math.AG math.CT math.DG


title:
On the Pitfalls of Batch Normalization for End-to-End Video Learning: A
  Study on Surgical Workflow Analysis

abstract:
  Batch Normalization's (BN) unique property of depending on other samples in a
batch is known to cause problems in several tasks, including sequential
modeling, and has led to the use of alternatives in these fields. In video
learning, however, these problems are less studied, despite the ubiquitous use
of BN in CNNs for visual feature extraction. We argue that BN's properties
create major obstacles for training CNNs and temporal models end to end in
video tasks. Yet, end-to-end learning seems preferable in specialized domains
such as surgical workflow analysis, which lack well-pretrained feature
extractors. While previous work in surgical workflow analysis has avoided
BN-related issues through complex, multi-stage learning procedures, we show
that even simple, end-to-end CNN-LSTMs can outperform the state of the art when
CNNs without BN are used. Moreover, we analyze in detail when BN-related issues
occur, including a "cheating" phenomenon in surgical anticipation tasks. We
hope that a deeper understanding of BN's limitations and a reconsideration of
end-to-end approaches can be beneficial for future research in surgical
workflow analysis and general video learning.


title:
OcclusionFusion: Occlusion-aware Motion Estimation for Real-time Dynamic
  3D Reconstruction

abstract:
  RGBD-based real-time dynamic 3D reconstruction suffers from inaccurate
inter-frame motion estimation as errors may accumulate with online tracking.
This problem is even more severe for single-view-based systems due to strong
occlusions. Based on these observations, we propose OcclusionFusion, a novel
method to calculate occlusion-aware 3D motion to guide the reconstruction. In
our technique, the motion of visible regions is first estimated and combined
with temporal information to infer the motion of the occluded regions through
an LSTM-involved graph neural network. Furthermore, our method computes the
confidence of the estimated motion by modeling the network output with a
probabilistic model, which alleviates untrustworthy motions and enables robust
tracking. Experimental results on public datasets and our own recorded data
show that our technique outperforms existing single-view-based real-time
methods by a large margin. With the reduction of the motion errors, the
proposed technique can handle long and challenging motion sequences. Please
check out the project page for sequence results:
https://wenbin-lin.github.io/OcclusionFusion.


title: Control Barrier Functions for Systems with Multiple Control Inputs

abstract:
  Control Barrier Functions (CBFs) are becoming popular tools in guaranteeing
safety for nonlinear systems and constraints, and they can reduce a constrained
optimal control problem into a sequence of Quadratic Programs (QPs) for affine
control systems. The recently proposed High Order Control Barrier Functions
(HOCBFs) work for arbitrary relative degree constraints. One of the challenges
in a HOCBF is to address the relative degree problem when a system has multiple
control inputs, i.e., the relative degree could be defined with respect to
different components of the control vector. This paper proposes two methods for
HOCBFs to deal with systems with multiple control inputs: a general integral
control method and a method which is simpler but limited to specific classes of
physical systems. When control bounds are involved, the feasibility of the
above mentioned QPs can also be significantly improved with the proposed
methods. We illustrate our approaches on a unicyle model with two control
inputs, and compare the two proposed methods to demonstrate their effectiveness
and performance.


title: Loss-tolerant all-photonic quantum repeater with generalized Shor code

abstract:
  The all-photonic quantum repeater (APQR) is a promising repeater scheme to
realize long-distance quantum communication. For a practical APQR, an
indispensable requirement is the robustness of the repeater graph state (RGS)
against photon loss. We propose a new loss-tolerant scheme by applying the
generalized Shor code to RGS, which can be experimentally demonstrated with
current technology. Experimentally, we first prepare and verify the nine-qubit
Shor code. Then, by applying the generalized Shor code to APQR and preparing a
simplified encoded RGS with the structure of $1\times2$ based on the Shor code
state, the effectiveness of our loss-tolerant scheme and the loss tolerance of
the encoded RGS are respectively verified. Our results make an essential step
toward a practical APQR and enrich the research of quantum error correction
code.


title: Object Detection as Probabilistic Set Prediction

abstract:
  Accurate uncertainty estimates are essential for deploying deep object
detectors in safety-critical systems. The development and evaluation of
probabilistic object detectors have been hindered by shortcomings in existing
performance measures, which tend to involve arbitrary thresholds or limit the
detector's choice of distributions. In this work, we propose to view object
detection as a set prediction task where detectors predict the distribution
over the set of objects. Using the negative log-likelihood for random finite
sets, we present a proper scoring rule for evaluating and training
probabilistic object detectors. The proposed method can be applied to existing
probabilistic detectors, is free from thresholds, and enables fair comparison
between architectures. Three different types of detectors are evaluated on the
COCO dataset. Our results indicate that the training of existing detectors is
optimized toward non-probabilistic metrics. We hope to encourage the
development of new object detectors that can accurately estimate their own
uncertainty. Code will be released.


title:
Discovery of Quasi Periodic Oscillations in the persistent X-ray
  emission of Accreting Binary X-ray Pulsar LMC X-4

abstract:
  We report the discovery of quasi-periodic oscillations (QPOs) in the High
Mass X-ray Binary (HMXB) pulsar LMC X-4 in its non-flaring (persistent) state
using observations with XMM-Newton. In addition to the 74 mHz coherent
pulsations, the persistent emission light curve shows a QPO feature in the
frequency range of 20-30 mHz. Quasi-periodic flares have been previously
observed from LMC X-4 in observations made with Rossi X-ray Timing Explorer
(RXTE). However, this is the first time QPOs have been observed in the
persistent emission observations of LMC X-4. QPOs in X-ray binaries are
generally thought to be related to the rotation of the inhomogeneous matter
distribution in the inner accretion disk. In High Mass X-ray Binaries (HMXBs)
such as LMC X-4 where the compact object is a neutron star with a high magnetic
field, the radius of the inner accretion disk is determined by the mass
accretion rate and the magnetic moment of the neutron star. In such systems,
the QPO feature, along with the pulse period and X-ray luminosity measurement
helps us to constrain the magnetic field strength of the neutron star. We use
considerations of magnetospheric accretion to have an approximate value of the
magnetic field strength of the neutron star in LMC X-4.


title: Linear-Time Verification of Data-Aware Dynamic Systems with Arithmetic

abstract:
  Combined modeling and verification of dynamic systems and the data they
operate on has gained momentum in AI and in several application domains. We
investigate the expressive yet concise framework of data-aware dynamic systems
(DDS), extending it with linear arithmetic, and provide the following
contributions. First, we introduce a new, semantic property of "finite
summary", which guarantees the existence of a faithful finite-state
abstraction. We rely on this to show that checking whether a witness exists for
a linear-time, finite-trace property is decidable for DDSs with finite summary.
Second, we demonstrate that several decidability conditions studied in formal
methods and database theory can be seen as concrete, checkable instances of
this property. This also gives rise to new decidability results. Third, we show
how the abstract, uniform property of finite summary leads to modularity
results: a system enjoys finite summary if it can be partitioned appropriately
into smaller systems that possess the property. Our results allow us to analyze
systems that were out of reach in earlier approaches. Finally, we demonstrate
the feasibility of our approach in a prototype implementation.


title:
Adversarial Robustness of Neural-Statistical Features in Detection of
  Generative Transformers

abstract:
  The detection of computer-generated text is an area of rapidly increasing
significance as nascent generative models allow for efficient creation of
compelling human-like text, which may be abused for the purposes of spam,
disinformation, phishing, or online influence campaigns. Past work has studied
detection of current state-of-the-art models, but despite a developing threat
landscape, there has been minimal analysis of the robustness of detection
methods to adversarial attacks. To this end, we evaluate neural and non-neural
approaches on their ability to detect computer-generated text, their robustness
against text adversarial attacks, and the impact that successful adversarial
attacks have on human judgement of text quality. We find that while statistical
features underperform neural features, statistical features provide additional
adversarial robustness that can be leveraged in ensemble detection models. In
the process, we find that previously effective complex phrasal features for
detection of computer-generated text hold little predictive power against
contemporary generative models, and identify promising statistical features to
use instead. Finally, we pioneer the usage of $\Delta$MAUVE as a proxy measure
for human judgement of adversarial text quality.