title: Stealing Malware Classifiers and AVs at Low False Positive Conditions

abstract:
  Model stealing attacks have been successfully used in many machine learning
domains, but there is little understanding of how these attacks work in the
malware detection domain. Malware detection and, in general, security domains
have very strong requirements of low false positive rates (FPR). However, these
requirements are not the primary focus of the existing model stealing
literature. Stealing attacks create surrogate models that perform similarly to
a target model using a limited amount of queries to the target. The first stage
of this study is the evaluation of active learning model stealing attacks
against publicly available stand-alone machine learning malware classifiers and
antivirus products (AVs). We propose a new neural network architecture for
surrogate models that outperforms the existing state of the art on low FPR
conditions. The surrogates were evaluated on their agreement with the targeted
models. Good surrogates of the stand-alone classifiers were created with up to
99% agreement with the target models, using less than 4% of the original
training dataset size. Good AV surrogates were also possible to train, but with
a lower agreement. The second stage used the best surrogates as well as the
target models to generate adversarial malware using the MAB framework to test
stand-alone models and AVs (offline and online). Results showed that surrogate
models could generate adversarial samples that evade the targets but are less
successful than the targets themselves. Using surrogates, however, is a
necessity for attackers, given that attacks against AVs are extremely
time-consuming and easily detected when the AVs are connected to the internet.


title:
Encoding Domain Knowledge in Multi-view Latent Variable Models: A
  Bayesian Approach with Structured Sparsity

abstract:
  Many real-world systems are described not only by data from a single source
but via multiple data views. For example, in genomic medicine, a patient can be
described by data from different molecular layers. This raises the need for
multi-view models that are able to disentangle variation within and across data
views in an interpretable manner. Latent variable models with structured
sparsity are a commonly used tool to address this modeling task but
interpretability is cumbersome since it requires a direct inspection and
interpretation of each factor via a specialized domain expert. Here, we propose
MuVI, a novel approach for domain-informed multi-view latent variable models,
facilitating the analysis of multi-view data in an inherently explainable
manner. We demonstrate that our model (i) is able to integrate noisy domain
expertise in form of feature sets, (ii) is robust to noise in the encoded
domain knowledge, (iii) results in identifiable factors and (iv) is able to
infer interpretable and biologically meaningful axes of variation in a
real-world multi-view dataset of cancer patients.


title:
Rapid model transfer for medical image segmentation via iterative
  human-in-the-loop update: from labelled public to unlabelled clinical
  datasets for multi-organ segmentation in CT

abstract:
  Despite the remarkable success on medical image analysis with deep learning,
it is still under exploration regarding how to rapidly transfer AI models from
one dataset to another for clinical applications. This paper presents a novel
and generic human-in-the-loop scheme for efficiently transferring a
segmentation model from a small-scale labelled dataset to a larger-scale
unlabelled dataset for multi-organ segmentation in CT. To achieve this, we
propose to use an igniter network which can learn from a small-scale labelled
dataset and generate coarse annotations to start the process of human-machine
interaction. Then, we use a sustainer network for our larger-scale dataset, and
iteratively updated it on the new annotated data. Moreover, we propose a
flexible labelling strategy for the annotator to reduce the initial annotation
workload. The model performance and the time cost of annotation in each subject
evaluated on our private dataset are reported and analysed. The results show
that our scheme can not only improve the performance by 19.7% on Dice, but also
expedite the cost time of manual labelling from 13.87 min to 1.51 min per CT
volume during the model transfer, demonstrating the clinical usefulness with
promising potentials.


title:
Basisness and completeness of Fucik eigenfunctions for the Neumann
  Laplacian

abstract:
  We investigate the basis properties of sequences of Fucik eigenfunctions of
the one-dimensional Neumann Laplacian. We show that any such sequence is
complete in $L^2(0,\pi)$ and a Riesz basis in the subspace of functions with
zero mean. Moreover, we provide sufficient assumptions on Fucik eigenvalues
which guarantee that the corresponding Fucik eigenfunctions form a Riesz basis
in $L^2(0,\pi)$ and we explicitly describe the corresponding biorthogonal
system.


title:
Entanglement of particles versus entanglement of fields: independent
  quantum resources

abstract:
  Nature allows one to explore a manifold of remarkable quantum effects. Most
prominently, quantum entanglement can be observed in many-particle systems,
between multiple quantized fields, and hybrid combinations thereof. This
diversity, however, also leads to contradicting conclusions about what truly
constitutes entanglement in any given physical scenario. By explicitly allowing
various perspectives, we rigorously define different notions of entanglement in
the context of first and second quantization. By providing instructive
examples, including scenarios of distinguishable and indistinguishable
particles, we show that particle entanglement and field entanglement are
actually distinct phenomena that can occur and be observed independently.
Furthermore, such types of different quantum correlations can be characterized
together with the exchange symmetry of bosonic and fermionic systems or
independently thereof. This observation not only affects our fundamental
understanding but has direct implications on quantum technology which can
harness those different forms of entanglement in practical scenarios.


title:
In situ gas cell for the analysis of adsorption behavior on surfaces
  using x-ray spectroscopy

abstract:
  A gas cell for in-situ measurements of Volatile Organic Compounds (VOCs) and
their adsorption behavior on different surfaces by means of X-Ray Fluorescence
(XRF) and X-ray Absorption Fine-Structure (XAFS) spectroscopy has been
developed. The cell is especially designed to allow for the efficient
excitation and detection of low-Z elements such as carbon, oxygen or nitrogen
as main components of VOCs. Two measurement modes are available. In the surface
mode, adsorption on a surface can be studied using XAFS by fluorescence
detection under shallow angles of incidence. The transmission mode enables the
simultaneous investigation of gaseous samples via XAFS in transmittance and
fluorescence detection modes. Proof-of-principle experiments were performed at
the PTB plane grating monochromator beamline for soft x-ray radiation at the
synchrotron radiation facility BESSY II. The flexible design and high
versatility of the cell are demonstrated with the investigation of ethanol
(EtOH) as one of the most abundant VOCs. The comparison of Near-Edge X-ray
Absorption Fine-Structure (NEXAFS) spectra under transmission and fluorescence
detection in the gas phase with measurements of adsorbed molecules on a
Si-wafer surface both at the C and O-K absorption edges proves the
applicability of the cell for the monitoring of adsorption processes.


title: xPACE and TASC Modeler: Tool support for data-driven context modeling

abstract:
  From a requirements engineering point of view, the elicitation of
context-aware functionalities calls for context modeling, an early step that
aims at understanding the application contexts and how it may influence user
tasks. In practice, however, context modeling activities have been overlooked
by practitioners due to their high complexity. To improve this situation, we
implemented xPACE and TASC Modeler, which are tools that support the automation
of context modeling based on existing contextual data. In this demonstration
paper, we present our implementation of a data-driven context modeling
approach, which is composed of a contextual data processor (xPACE) and a
context model generator (TASC Modeler). We successfully evaluated the results
provided by the tools in a software development project.


title: Distributed Coalgebraic Partition Refinement

abstract:
  Partition refinement is a method for minimizing automata and transition
systems of various types. Recently, a new partition refinement algorithm and
associated tool CoPaR were developed that are generic in the transition type of
the input system and match the theoretical run time of the best known
algorithms for many concrete system types. Genericity is achieved by modelling
transition types as functors on sets and systems as coalgebras. Experimentation
has shown that memory consumption is a bottleneck for handling systems with a
large state space, while running times are fast. We have therefore extended an
algorithm due to Blom and Orzan, which is suitable for a distributed
implementation to the coalgebraic level of genericity, and implemented it in
CoPaR. Experiments show that this allows to handle much larger state spaces.
Running times are low in most experiments, but there is a significant penalty
for some.


title: Decoherence-Suppressed Non-adiabatic Holonomic Quantum Computation

abstract:
  Nonadiabatic holonomic quantum computation~(NHQC) provides an essential way
to construct robust and high-fidelity quantum gates due to its geometric
features. However, NHQC is more sensitive to the decay and dephasing errors
than conventional dynamical gate since it requires an ancillary intermediate
state. Here, we utilize the Hamiltonian reverse engineering technique to study
the influence of the intermediate state-decoherence on the NHQC gate fidelity,
and propose the novel schemes to construct the arbitrary single-qubit holonomic
gate and nontrivial two-qubit holonomic gate with high fidelity and robustness
to the decoherence. Although the proposed method is generic and can be applied
to many experimental platforms, such as superconducting qubits, trapped ions,
quantum dots, here we take nitrogen-vacancy (NV) center as an example to show
that the gate fidelity can be significantly enhanced from 89\% to 99.6\% in
contrast to the recent experimental NHQC schemes [Phys. Rev. Lett. 119, 140503
(2017); Nat. Photonics 11, 309 (2017); Opt. Lett. 43, 2380 (2018)], and the
robustness against the decoherence can also be significantly improved. All in
all, our scheme provides a promising way for fault-tolerant geometric quantum
computation.


title:
Large-scale multi-objective influence maximisation with network
  downscaling

abstract:
  Finding the most influential nodes in a network is a computationally hard
problem with several possible applications in various kinds of network-based
problems. While several methods have been proposed for tackling the influence
maximisation (IM) problem, their runtime typically scales poorly when the
network size increases. Here, we propose an original method, based on network
downscaling, that allows a multi-objective evolutionary algorithm (MOEA) to
solve the IM problem on a reduced scale network, while preserving the relevant
properties of the original network. The downscaled solution is then upscaled to
the original network, using a mechanism based on centrality metrics such as
PageRank. Our results on eight large networks (including two with $\sim$50k
nodes) demonstrate the effectiveness of the proposed method with a more than
10-fold runtime gain compared to the time needed on the original network, and
an up to $82\%$ time reduction compared to CELF.


title:
Experimental Standards for Deep Learning Research: A Natural Language
  Processing Perspective

abstract:
  The field of Deep Learning (DL) has undergone explosive growth during the
last decade, with a substantial impact on Natural Language Processing (NLP) as
well. Yet, as with other fields employing DL techniques, there has been a lack
of common experimental standards compared to more established disciplines.
Starting from fundamental scientific principles, we distill ongoing discussions
on experimental standards in DL into a single, widely-applicable methodology.
Following these best practices is crucial to strengthening experimental
evidence, improve reproducibility and enable scientific progress. These
standards are further collected in a public repository to help them
transparently adapt to future needs.


title: What Matters in Language Conditioned Robotic Imitation Learning

abstract:
  A long-standing goal in robotics is to build robots that can perform a wide
range of daily tasks from perceptions obtained with their onboard sensors and
specified only via natural language. While recently substantial advances have
been achieved in language-driven robotics by leveraging end-to-end learning
from pixels, there is no clear and well-understood process for making various
design choices due to the underlying variation in setups. In this paper, we
conduct an extensive study of the most critical challenges in learning language
conditioned policies from offline free-form imitation datasets. We further
identify architectural and algorithmic techniques that improve performance,
such as a hierarchical decomposition of the robot control learning, a
multimodal transformer encoder, discrete latent plans and a self-supervised
contrastive loss that aligns video and language representations. By combining
the results of our investigation with our improved model components, we are
able to present a novel approach that significantly outperforms the state of
the art on the challenging language conditioned long-horizon robot manipulation
CALVIN benchmark. We have open-sourced our implementation to facilitate future
research in learning to perform many complex manipulation skills in a row
specified with natural language. Codebase and trained models available at
http://hulc.cs.uni-freiburg.de


title: The vertex connectivity of some classes of divisible design graphs

abstract:
  A $k$-regular graph is called a divisible design graph if its vertex set can
be partitioned into $m$ classes of size $n$, such that two distinct vertices
from the same class have exactly $\lambda_1$ common neighbours, and two
vertices from different classes have exactly $\lambda_2$ common neighbours. In
this paper, we find the vertex connectivity of some classes of divisible design
graphs, in particular, we present examples of divisible design graphs, whose
vertex connectivity is less than $k$, where $k$ is the degree of a vertex. We
also show that the vertex connectivity a divisible design graphs may be less
than $k$ by any power of 2.


title:
Deep Learning for Effective and Efficient Reduction of Large Adaptation
  Spaces in Self-Adaptive Systems

abstract:
  Many software systems today face uncertain operating conditions, such as
sudden changes in the availability of resources or unexpected user behavior.
Without proper mitigation these uncertainties can jeopardize the system goals.
Self-adaptation is a common approach to tackle such uncertainties. When the
system goals may be compromised, the self-adaptive system has to select the
best adaptation option to reconfigure by analyzing the possible adaptation
options, i.e., the adaptation space. Yet, analyzing large adaptation spaces
using rigorous methods can be resource- and time-consuming, or even be
infeasible. One approach to tackle this problem is by using online machine
learning to reduce adaptation spaces. However, existing approaches require
domain expertise to perform feature engineering to define the learner, and
support online adaptation space reduction only for specific goals. To tackle
these limitations, we present 'Deep Learning for Adaptation Space Reduction
Plus' -- DLASeR+ in short. DLASeR+ offers an extendable learning framework for
online adaptation space reduction that does not require feature engineering,
while supporting three common types of adaptation goals: threshold,
optimization, and set-point goals. We evaluate DLASeR+ on two instances of an
Internet-of-Things application with increasing sizes of adaptation spaces for
different combinations of adaptation goals. We compare DLASeR+ with a baseline
that applies exhaustive analysis and two state-of-the-art approaches for
adaptation space reduction that rely on learning. Results show that DLASeR+ is
effective with a negligible effect on the realization of the adaptation goals
compared to an exhaustive analysis approach, and supports three common types of
adaptation goals beyond the state-of-the-art approaches.


title:
Neural Operator with Regularity Structure for Modeling Dynamics Driven
  by SPDEs

abstract:
  Stochastic partial differential equations (SPDEs) are significant tools for
modeling dynamics in many areas including atmospheric sciences and physics.
Neural Operators, generations of neural networks with capability of learning
maps between infinite-dimensional spaces, are strong tools for solving
parametric PDEs. However, they lack the ability to modeling SPDEs which usually
have poor regularity due to the driving noise. As the theory of regularity
structure has achieved great successes in analyzing SPDEs and provides the
concept model feature vectors that well-approximate SPDEs' solutions, we
propose the Neural Operator with Regularity Structure (NORS) which incorporates
the feature vectors for modeling dynamics driven by SPDEs. We conduct
experiments on various of SPDEs including the dynamic Phi41 model and the 2d
stochastic Navier-Stokes equation, and the results demonstrate that the NORS is
resolution-invariant, efficient, and achieves one order of magnitude lower
error with a modest amount of data.


title: Fast Arbitrary Precision Floating Point on FPGA

abstract:
  Numerical codes that require arbitrary precision floating point (APFP)
numbers for their core computation are dominated by elementary arithmetic
operations due to the super-linear complexity of multiplication in the number
of mantissa bits. APFP computations on conventional software-based
architectures are made exceedingly expensive by the lack of native hardware
support, requiring elementary operations to be emulated using instructions
operating on machine-word-sized blocks. In this work, we show how APFP
multiplication on compile-time fixed-precision operands can be implemented as
deep FPGA pipelines with a recursively defined Karatsuba decomposition on top
of native DSP multiplication. When comparing our design implemented on an Alveo
U250 accelerator to a dual-socket 36-core Xeon node running the GNU Multiple
Precision Floating-Point Reliable (MPFR) library, we achieve a 9.8x speedup at
4.8 GOp/s for 512-bit multiplication, and a 5.3x speedup at 1.2 GOp/s for
1024-bit multiplication, corresponding to the throughput of more than 351x and
191x CPU cores, respectively. We apply this architecture to general
matrix-matrix multiplication, yielding a 10x speedup at 2.0 GOp/s over the Xeon
node, equivalent to more than 375x CPU cores, effectively allowing a single
FPGA to replace a small CPU cluster. Due to the significant dependence of some
numerical codes on APFP, such as semidefinite program solvers, we expect these
gains to translate into real-world speedups. Our configurable and flexible
HLS-based code provides as high-level software interface for plug-and-play
acceleration, published as an open source project.


title:
Physical layer security in large-scale random multiple access wireless
  sensor networks: a stochastic geometry approach

abstract:
  This paper investigates physical layer security for a large-scale WSN with
random multiple access, where each fusion center in the network randomly
schedules a number of sensors to upload their sensed data subject to the
overhearing of randomly distributed eavesdroppers. We propose an uncoordinated
random jamming scheme in which those unscheduled sensors send jamming signals
with a certain probability to defeat the eavesdroppers. With the aid of
stochastic geometry theory and order statistics, we derive analytical
expressions for the connection outage probability and secrecy outage
probability to characterize transmission reliability and secrecy, respectively.
Based on the obtained analytical results, we formulate an optimization problem
for maximizing the sum secrecy throughput subject to both reliability and
secrecy constraints, considering a joint design of the wiretap code rates for
each scheduled sensor and the jamming probability for the unscheduled sensors.
We provide both optimal and low-complexity sub-optimal algorithms to tackle the
above problem, and further reveal various properties on the optimal parameters
which are useful to guide practical designs. In particular, we demonstrate that
the proposed random jamming scheme is beneficial for improving the sum secrecy
throughput, and the optimal jamming probability is the result of trade-off
between secrecy and throughput. We also show that the throughput performance of
the sub-optimal scheme approaches that of the optimal one when facing a
stringent reliability constraint or a loose secrecy constraint.


title:
Highly efficient exponential scalar auxiliary variable approaches with
  relaxation (RE-SAV) for gradient flows

abstract:
  For the past few years, scalar auxiliary variable (SAV) and SAV-type
approaches became very hot and efficient methods to simulate various gradient
flows. Inspired by the new SAV approach in \cite{huang2020highly}, we propose a
novel technique to construct a new exponential scalar auxiliary variable
(E-SAV) approach to construct high-order numerical energy stable schemes for
gradient flows. To improve its accuracy and consistency noticeably, we propose
an E-SAV approach with relaxation, which we named the relaxed E-SAV (RE-SAV)
method for gradient flows. The RE-SAV approach preserves all the advantages of
the traditional SAV approach. In addition, we do not need any the
bounded-from-below assumptions for the free energy potential or nonlinear term.
Besides, the first-order, second-order and higher-order unconditionally energy
stable time-stepping schemes are easy to construct. Several numerical examples
are provided to demonstrate the improved efficiency and accuracy of the
proposed method.


title:
Simulator-in-the-loop state estimation for vehicle dynamics control:
  theory and experiments

abstract:
  In vehicle dynamics control, many variables of interest cannot be directly
measured, as sensors might be costly, fragile, or even not available.
Therefore, real-time estimation techniques need to be used. The previous
approach suffers from two main drawbacks: (i) the approximations due to model
mismatch might jeopardize the performance of the final estimation-based
control; (ii) each new estimator requires the calibration from scratch of a
dedicated model. In this paper, we propose a simulator-in-the-loop scheme,
where the ad-hoc model is replaced by an accurate multibody simulator of the
vehicle, typically available to vehicles manufacturers and suitable for the
estimation of any onboard variable, coupled with a compensator within a
closed-loop observer scheme. Given the black-box nature of the simulator, a
data-driven methodology for observer tuning is developed, based on Bayesian
optimization. The effectiveness of the proposed estimation method for the
estimation of vehicle states and forces, as compared to traditional model-based
Kalman filtering, is experimentally shown on a dataset collected with a sport
car.


title: Self-critical Sequence Training for Automatic Speech Recognition

abstract:
  Although automatic speech recognition (ASR) task has gained remarkable
success by sequence-to-sequence models, there are two main mismatches between
its training and testing that might lead to performance degradation: 1) The
typically used cross-entropy criterion aims to maximize log-likelihood of the
training data, while the performance is evaluated by word error rate (WER), not
log-likelihood; 2) The teacher-forcing method leads to the dependence on ground
truth during training, which means that model has never been exposed to its own
prediction before testing. In this paper, we propose an optimization method
called self-critical sequence training (SCST) to make the training procedure
much closer to the testing phase. As a reinforcement learning (RL) based
method, SCST utilizes a customized reward function to associate the training
criterion and WER. Furthermore, it removes the reliance on teacher-forcing and
harmonizes the model with respect to its inference procedure. We conducted
experiments on both clean and noisy speech datasets, and the results show that
the proposed SCST respectively achieves 8.7% and 7.8% relative improvements
over the baseline in terms of WER.


title:
On Signs of Fourier Coefficients of Hecke-Maass Cusp Forms on
  $\mathrm{GL}_3$

abstract:
  We consider sign changes of Fourier coefficients of Hecke-Maass cusp forms
for the group $\mathrm{SL}_3(\mathbb Z)$. When the underlying form is
self-dual, we show that there are $\gg_\varepsilon X^{5/6-\varepsilon}$ sign
changes among the coefficients $\{A(m,1)\}_{m\leq X}$ and that there is a
positive proportion of sign changes for many self-dual forms. Similar result
concerning the positive proportion of sign changes also hold for the
real-valued coefficients $A(m,m)$ for generic $\mathrm{GL}_3$ cusp forms, a
result which is based on a new effective Sato-Tate type theorem for a family of
$\mathrm{GL}_3$ cusp forms we establish. In addition, non-vanishing of the
Fourier coefficients is studied under the Ramanujan-Petersson conjecture.


title:
Analytical explanation of attosecond transient absorption spectroscopy
  in graphene

abstract:
  We investigate the attosecond transient absorption spectroscopy (ATAS) in
graphene numerically and analytically. We simplify our research from the
two-dimensional four-band model of graphene to a two-electron model in the
one-dimensional two-band structure. In particular, our analytical results based
on the two-electron model agree well with the numerical results. Inspired by
the analytical results, we split the ATAS into zero-order and first-order
fringes, and clearly define the opening angle of the V-shaped structure of the
first-order fringes. Our analytical results reveal the underlying mechanism for
the increase of the opening angles with increasing laser frequency. Our
analysis indicates that the brightness of the first-order fringes is closely
related to the effective mass of the electrons in Van Hove singularities. In
addition, other phenomena such as the periodic variation of the first-order
fringes at twice the laser frequency also can be understood by our analytical
results. These results may be generalized to other periodic materials and shed
light on the complex generation mechanism of the ATAS.


title: Two particles interacting via a contact interaction on $S^2$

abstract:
  We consider two particles interacting via a contact interaction that are
constrained to a sphere, or $S^2$. We determine their spectrum to arbitrary
precision and for arbitrary angular momentum. We show how the non-inertial
frame leads to non-trivial solutions for different angular momenta. Our results
represent an extension of the finite-volume L\"uscher formulas but now to a
non-trivial geometry. We apply our results to predict the spectrum of select
two-nucleon halo nuclei and compare with experimental results.


title: Generalization Error Bounds for Multiclass Sparse Linear Classifiers

abstract:
  We consider high-dimensional multiclass classification by sparse multinomial
logistic regression. Unlike binary classification, in the multiclass setup one
can think about an entire spectrum of possible notions of sparsity associated
with different structural assumptions on the regression coefficients matrix. We
propose a computationally feasible feature selection procedure based on
penalized maximum likelihood with convex penalties capturing a specific type of
sparsity at hand. In particular, we consider global sparsity, double row-wise
sparsity, and low-rank sparsity, and show that with the properly chosen tuning
parameters the derived plug-in classifiers attain the minimax generalization
error bounds (in terms of misclassification excess risk) within the
corresponding classes of multiclass sparse linear classifiers. The developed
approach is general and can be adapted to other types of sparsity as well.


title: Optimal Intermittent Particle Filter

abstract:
  The problem of the optimal allocation (in the expected mean square error
sense) of a measurement budget for particle filtering is addressed. We propose
three different optimal intermittent filters, whose optimality criteria depend
on the information available at the time of decision making. For the first, the
stochastic program filter, the measurement times are given by a policy that
determines whether a measurement should be taken based on the measurements
already acquired. The second, called the offline filter, determines all
measurement times at once by solving a combinatorial optimization program
before any measurement acquisition. For the third one, which we call online
filter, each time a new measurement is received, the next measurement time is
recomputed to take all the information that is then available into account. We
prove that in terms of expected mean square error, the stochastic program
filter outperforms the online filter, which itself outperforms the offline
filter. However, these filters are generally intractable. For this reason, the
filter estimate is approximated by a particle filter. Moreover, the mean square
error is approximated using a Monte-Carlo approach, and different optimization
algorithms are compared to approximately solve the combinatorial programs (a
random trial algorithm, greedy forward and backward algorithms, a simulated
annealing algorithm, and a genetic algorithm). Finally, the performance of the
proposed methods is illustrated on two examples: a tumor motion model and a
common benchmark for particle filtering.


title:
Power-law Spectrum of Energetic Particles in Classical Thermal
  Equilibrium by Pitch-angle Scattering Process

abstract:
  The Boltzmann-Gibbs thermodynamic equilibrium state of charged particles
pitch-angle scattered by weak plasma waves is discussed. Degrees of freedom of
these waves play a fundamental role in constructing the grand canonical
ensemble. Via the gyro-resonance condition, fast particles have an inverse
break power-law spectrum for $ \varepsilon -\mu \ll T $, where $ \varepsilon $
is the particle energy, $ \mu $ is the chemical potential, $ T $ is the
temperature. The break energies are the rest energy and $ -\mu $. For $
\varepsilon \ll -\mu \ll T $, the energy spectral index $ \alpha $ is $ \delta
/2+1 $ and $ \delta +1 $ for non- and ultra-relativistic particles,
respectively, with $ \delta $ an effective fractal dimension of background
magnetic field lines. The spectral index for $ -\mu \ll \varepsilon \ll T $ is
$ \alpha +1 $. This thermal equilibrium scenario, combined with the leaky-box
model and cosmic-ray observations, seems to suggest that the Galactic magnetic
field is super-diffusive with $ \delta \approx 1.4 $.


title: Hyperelliptic functions and motion in general relativity

abstract:
  Analysis of black hole spacetimes requires study of the motion of particles
and light in these spacetimes. Here exact solutions of the geodesic equations
are the means of choice. Numerous interesting black hole spacetimes have been
analyzed in terms of elliptic functions. However, the presence of a
cosmological constant, higher dimensions or alternative gravity theories often
necessitate an analysis in terms of hyperelliptic functions. Here we review the
method and current status for solving the geodesic equations for the general
hyperelliptic case, illustrating it with set a of examples of genus $g=2$.


title: Gravitational Response of Topological Quantum States of Matter

abstract:
  Identifying novel topological properties of topological quantum states of
matter, such as exemplified by the quantized Hall conductance, is a valuable
step towards realizing materials with attractive topological attributes that
guarantee their imperviousness to realistic imperfections, disorder and
environmental disturbances. Is the gravitational coupling coefficient of
topological quantum states of matter a promising candidate? Substantially
building on well established results for quantum Hall states, herein we report
that a large class of lattice topological states of matter exhibit
gravitational response, i.e., charge response to spatial curvature, which is
characterized by a topologically quantized coupling constant. Remarkably, the
charge-gravity relationship remains linear in the curvature, up to the maximum
curvature achievable on the lattice, demonstrating absence of higher order
nonlinear response. Our findings facilitate articulating the physical
principles underlying the topological quantization of the gravitational
coupling constant, in analogy with the insights offered by the Chern number
description of the quantized Hall conductance.


title:
Two-dimensional sliding charge density waves and their protected edge
  modes

abstract:
  We propose and study a two-dimensional (2D) charge density waves (CDW) phase,
which is constructed from an array of weakly coupled one-dimensional (1D)
sliding CDW wires whose phases shift from one wire to the next. We show that
the fully gapped bulk CDW has topological properties, characterized by a
nonzero Chern number, that imply protected edge modes within the bulk gap. The
edge modes exhibit spectral pseudo-flow as a function of \emph{position} along
the edge, and are thus dual to chiral edge modes of Chern insulators with their
spectral flow in \emph{momentum} space. Furthermore, we show that these sliding
CDW edge modes are stable against small inter-wire coupling. Our predictions
can be tested experimentally in quasi-1D CDW compounds such as Ta$_2$Se$_8$I.


title:
Utilizing variational autoencoders in the Bayesian inverse problem of
  photoacoustic tomography

abstract:
  There has been an increasing interest in utilizing machine learning methods
in inverse problems and imaging. Most of the work has, however, concentrated on
image reconstruction problems, and the number of studies regarding the full
solution of the inverse problem is limited. In this work, we study a machine
learning based approach for the Bayesian inverse problem of photoacoustic
tomography. We develop an approach for estimating the posterior distribution in
photoacoustic tomography using an approach based on the variational
autoencoder. The approach is evaluated with numerical simulations and compared
to the solution of the inverse problem using a Bayesian approach.


title: TangoBERT: Reducing Inference Cost by using Cascaded Architecture

abstract:
  The remarkable success of large transformer-based models such as BERT,
RoBERTa and XLNet in many NLP tasks comes with a large increase in monetary and
environmental cost due to their high computational load and energy consumption.
In order to reduce this computational load in inference time, we present
TangoBERT, a cascaded model architecture in which instances are first processed
by an efficient but less accurate first tier model, and only part of those
instances are additionally processed by a less efficient but more accurate
second tier model. The decision of whether to apply the second tier model is
based on a confidence score produced by the first tier model. Our simple method
has several appealing practical advantages compared to standard cascading
approaches based on multi-layered transformer models. First, it enables higher
speedup gains (average lower latency). Second, it takes advantage of batch size
optimization for cascading, which increases the relative inference cost
reductions. We report TangoBERT inference CPU speedup on four text
classification GLUE tasks and on one reading comprehension task. Experimental
results show that TangoBERT outperforms efficient early exit baseline models;
on the the SST-2 task, it achieves an accuracy of 93.9% with a CPU speedup of
8.2x.


title:
3D-SPS: Single-Stage 3D Visual Grounding via Referred Point Progressive
  Selection

abstract:
  3D visual grounding aims to locate the referred target object in 3D point
cloud scenes according to a free-form language description. Previous methods
mostly follow a two-stage paradigm, i.e., language-irrelevant detection and
cross-modal matching, which is limited by the isolated architecture. In such a
paradigm, the detector needs to sample keypoints from raw point clouds due to
the inherent properties of 3D point clouds (irregular and large-scale), to
generate the corresponding object proposal for each keypoint. However, sparse
proposals may leave out the target in detection, while dense proposals may
confuse the matching model. Moreover, the language-irrelevant detection stage
can only sample a small proportion of keypoints on the target, deteriorating
the target prediction. In this paper, we propose a 3D Single-Stage Referred
Point Progressive Selection (3D-SPS) method, which progressively selects
keypoints with the guidance of language and directly locates the target.
Specifically, we propose a Description-aware Keypoint Sampling (DKS) module to
coarsely focus on the points of language-relevant objects, which are
significant clues for grounding. Besides, we devise a Target-oriented
Progressive Mining (TPM) module to finely concentrate on the points of the
target, which is enabled by progressive intra-modal relation modeling and
inter-modal target mining. 3D-SPS bridges the gap between detection and
matching in the 3D visual grounding task, localizing the target at a single
stage. Experiments demonstrate that 3D-SPS achieves state-of-the-art
performance on both ScanRefer and Nr3D/Sr3D datasets.


title:
Towards A Critical Evaluation of Robustness for Deep Learning Backdoor
  Countermeasures

abstract:
  Since Deep Learning (DL) backdoor attacks have been revealed as one of the
most insidious adversarial attacks, a number of countermeasures have been
developed with certain assumptions defined in their respective threat models.
However, the robustness of these countermeasures is inadvertently ignored,
which can introduce severe consequences, e.g., a countermeasure can be misused
and result in a false implication of backdoor detection.
  For the first time, we critically examine the robustness of existing backdoor
countermeasures with an initial focus on three influential model-inspection
ones that are Neural Cleanse (S&P'19), ABS (CCS'19), and MNTD (S&P'21).
Although the three countermeasures claim that they work well under their
respective threat models, they have inherent unexplored non-robust cases
depending on factors such as given tasks, model architectures, datasets, and
defense hyper-parameter, which are \textit{not even rooted from delicate
adaptive attacks}. We demonstrate how to trivially bypass them aligned with
their respective threat models by simply varying aforementioned factors.
Particularly, for each defense, formal proofs or empirical studies are used to
reveal its two non-robust cases where it is not as robust as it claims or
expects, especially the recent MNTD. This work highlights the necessity of
thoroughly evaluating the robustness of backdoor countermeasures to avoid their
misleading security implications in unknown non-robust cases.


title: Overparameterized Linear Regression under Adversarial Attacks

abstract:
  As machine learning models start to be used in critical applications, their
vulnerabilities and brittleness become a pressing concern. Adversarial attacks
are a popular framework for studying these vulnerabilities. In this work, we
study the error of linear regression in the face of adversarial attacks. We
provide bounds of the error in terms of the traditional risk and the parameter
norm and show how these bounds can be leveraged and make it possible to use
analysis from non-adversarial setups to study the adversarial risk. The
usefulness of these results is illustrated by shedding light on whether or not
overparameterized linear models can be adversarially robust. We show that
adding features to linear models might be either a source of additional
robustness or brittleness. We show that these differences appear due to scaling
and how the $\ell_1$ and $\ell_2$ norms of random projections concentrate. We
also show how the reformulation we propose allows for solving adversarial
training as a convex optimization problem. This is then used as a tool to study
how adversarial training and other regularization methods might affect the
robustness of the estimated models.

categories: stat.ML cs.CR cs.LG eess.SP math.ST stat.TH


title: Assessing cloudiness in nonwovens

abstract:
  The homogeneity of filter media is important for material selection and
quality control, along with the specific weight (nominal grammage) and the
distribution of the local weight. Cloudiness or formation is a concept used to
describe deviations from homogeneity in filter media. We suggest to derive the
cloudiness index from the power spectrum of the relative local areal weight,
integrated over a selected frequency range. The power spectrum captures the
energy density in a broad spectral range. Moreover, under certain conditions,
the structure of a nonwoven is fully characterized by the areal weight, the
variance of the local areal weight, and the power spectrum. Consequently, the
power spectrum is the parameter that exclusively reflects the cloudiness. Here,
we address questions arising from practical application. The most prominent is
the choice of the spectral band. It certainly depends on the characteristic
"size of the clouds", but is limited by the size and lateral resolution of the
images. We show that the cloudiness index based on the power spectrum of the
relative local areal weight is theoretically well founded and can be robustly
measured from image data. Choosing the spectral band allows to capture the
cloudiness either visually perceived or found to be decisive for product
properties. It is thus well suited to build a technical standard on it.


title: Anomalous diffusion originated by two Markovian hopping-trap mechanisms

abstract:
  We show through intensive simulations that the paradigmatic features of
anomalous diffusion are indeed the features of a (continuous-time) random walk
driven by two different Markovian hopping-trap mechanisms. If $p \in (0,1/2)$
and $1-p$ are the probabilities of occurrence of each Markovian mechanism, then
the anomalousness parameter $\beta \in (0,1)$ results to be $\beta \simeq 1 -
1/\{1 + \log[(1-p)/p]\}$. Ensemble and single-particle observables of this
model have been studied and they match the main characteristics of anomalous
diffusion as they are typically measured in living systems. In particular, the
celebrated transition of the walker's distribution from exponential to
stretched-exponential and finally to Gaussian distribution is displayed by
including also the Brownian yet non-Gaussian interval.


title:
Edge Labelled Graphs and Property Graphs; a comparison from the user
  perspective

abstract:
  This study compares participant acceptance of the property graph and
edge-labelled graph paradigms, as represented by Cypher and the proposed
extensions to the W3C standards, RDF* and SPARQL*.
  In general, modelling preferences are consistent across the two paradigms.
When presented with location information, participants preferred to create
nodes to represent cities, rather than use metadata; although the preference
was less marked for Cypher. In Cypher, participants showed little difference in
preference between representing dates or population size as nodes. In RDF*,
this choice was not necessary since both could be represented as literals.
However, there was a significant preference for using the date as metadata to
describe a triple containing population size, rather than vice versa. There was
no significant difference overall in accuracy of interpretation of queries in
the two paradigms; although in one specific case, the use of a reverse arrow in
Cypher was interpreted significantly more accurately than the ^ symbol in
SPARQL. Based on our results and on the comments of participants, we make some
recommendations for modellers.
  Techniques for reifing RDF have attracted a great deal of research. Recently,
a hybrid approach, employing some of the features of property graphs, has
claimed to offer an improved technique for RDF reification. Query-time
reasoning is also a requirement which has prompted a number of proposed
extensions to SPARQL and which is only possible to a limited extent in the
property graph paradigm. Another recent development, the hypergraph paradigm
enables more powerful query-time reasoning. There is a need for more research
into the user acceptance of these various more powerful approaches to modelling
and querying. Such research should take account of complex modelling
situations.


title:
Full Statistics of Nonstationary Heat Transfer in the
  Kipnis-Marchioro-Presutti Model

abstract:
  We investigate non-stationary heat transfer in the Kipnis-Marchioro-Presutti
(KMP) lattice gas model at long times in one dimension when starting from a
localized heat distribution. At large scales this initial condition can be
described as a delta-function, $u(x,t=0)=W \delta(x)$. We characterize the
process by the heat, transferred to the right of a specified point $x=X$ by
time $T$, $$ J=\int_X^\infty u(x,t=T)\,dx\,, $$ and study the full probability
distribution $\mathcal{P}(J,X,T)$. The particular case of $X=0$ has been
recently solved [Bettelheim et al. Phys. Rev. Lett. 128, 130602 (2022)]. At
fixed $J$, the distribution $\mathcal{P}$ as a function of $X$ and $T$ has the
same long-time dynamical scaling properties as the position of a tracer in a
single-file diffusion. Here we evaluate $\mathcal{P}(J,X,T)$ by exploiting the
recently uncovered complete integrability of the equations of the macroscopic
fluctuation theory (MFT) for the KMP model and using the Zakharov-Shabat
inverse scattering method. We also discuss asymptotics of $\mathcal{P}(J,X,T)$
which we extract from the exact solution, and also obtain perturbatively
directly from the MFT equations.

categories: cond-mat.stat-mech math-ph math.MP math.PR nlin.SI


title:
Resolution of intramolecular dipoles and push-back effect of individual
  molecules on a metal surface

abstract:
  Molecules consisting of a donor and an acceptor moiety can exhibit large
intrinsic dipole moments. Upon deposition on a metal surface, the dipole may be
effectively screened and the charge distribution altered due to hybridization
with substrate electronic states. Here, we deposit Ethyl-Diaminodicyanoquinone
molecules, which exhibit a large dipole moment in gas phase, on a Au(111)
surface. Employing a combination of scanning tunneling microscopy and
non-contact atomic force microscopy, we find that a significant dipole moment
persists in the flat-lying molecules. Density-functional theory calculations
reveal that the dipole moment is even increased on the metal substrate as
compared to the gas phase. We also show that the local contact potential across
the molecular islands is decreased by several tens of meV with respect to the
bare metal. We explain this by the induced charge-density redistribution due to
the adsorbed molecules, which confine the substrate's wavefunction at the
interface. Our local measurements provide direct evidence of this so-called
push-back or cushion effect at the scale of individual molecules.


title: UNIONS: The impact of systematic errors on weak-lensing peak counts

abstract:
  UNIONS is an ongoing deep photometric multi-band survey of the Northern sky.
As part of UNIONS, CFIS provides r-band data which we use to study weak-lensing
peak counts for cosmological inference. We assess systematic effects for
weak-lensing peak counts and their impact on cosmological parameters for the
UNIONS survey. In particular, we present results on local calibration,
metacalibration shear bias, baryonic feedback, the source galaxy redshift
estimate, intrinsic alignment, and the cluster member dilution. For each
uncertainty and systematic effect, we describe our mitigation scheme and the
impact on cosmological parameter constraints. We obtain constraints on
cosmological parameters from MCMC using CFIS data and MassiveNuS N-body
simulations as a model for peak counts statistics. Depending on the calibration
(local versus global, and the inclusion of the residual multiplicative shear
bias), the mean matter density parameter $\Omega_m$ can shift up to $-0.024$
($-0.5\sigma$). We also see that including baryonic corrections can shift
$\Omega_m$ by $+0.027$ ($+0.5 \sigma$) with respect to the DM-only simulations.
Reducing the impact of the intrinsic alignment and cluster member dilution
through signal-to-noise cuts can lead to a shift in $\Omega_m$ of $+0.027$
($+0.5 \sigma$). Finally, with a mean redshift uncertainty of $\Delta \bar{z} =
0.03$, we see that the shift of $\Omega_m$ ($+0.001$ which corresponds to
$+0.02 \sigma$) is not significant. This paper investigates for the first time
with UNIONS weak-lensing data and peak counts the impact of systematic effects.
The value of $\Omega_m$ is the most impacted and can shift up to $\sim 0.03$
which corresponds to $0.5\sigma$ depending on the choices for each systematics.
We expect constraints to become more reliable with future (larger) data
catalogues, for which the current pipeline will provide a starting point.


title: On non-linear mappings preserving the semi-inner product

abstract:
  We say that a smooth normed space $X$ has a property (SL), if every mapping
$f:X \to X$ preserving the semi-inner product on $X$ is linear. It is well
known that every Hilbert space has the property (SL) and the same is true for
every finite-dimensional smooth normed space. In this paper, we establish
several new results concerning the property (SL). We give a simple example of a
smooth and strictly convex Banach space which is isomorphic to the space
$\ell_p$, but without the property (SL). Moreover, we provide a
characterization of the property (SL) in the class of reflexive smooth Banach
spaces in terms of subspaces of quotient spaces. As a consequence, we prove
that the space $\ell_p$ have the property (SL) for every $1 < p < \infty$.
Finally, using a variant of the Gowers-Maurey space, we construct an
infinite-dimensional uniformly smooth Banach space $X$ such that every smooth
Banach space isomorphic to $X$ has the property (SL).


title: Celestial: Virtual Software System Testbeds for the LEO Edge

abstract:
  As private space companies such as SpaceX and Telesat are building large LEO
satellite constellations to provide global broadband Internet access,
researchers have proposed to embed compute services within satellite
constellations to provide computing services on the LEO edge. While the LEO
edge is merely theoretical at the moment, providers are expected to rapidly
develop their satellite technologies to keep the upper hand in the new space
race.
  In this paper, we answer the question of how researchers can explore the
possibilities of LEO edge computing and evaluate arbitrary software systems in
an accurate runtime environment and with cost-efficient scalability. To that
end, we present Celestial, a virtual testbed for the LEO edge based on
microVMs. Celestial can efficiently emulate individual satellites and their
movement as well as ground station servers with realistic network conditions
and in an application-agnostic manner, which we show empirically. Additionally,
we explore opportunities and implications of deploying a real-time remote
sensing application on LEO edge infrastructure in a case study on Celestial.


title:
Curriculum: A Broad-Coverage Benchmark for Linguistic Phenomena in
  Natural Language Understanding

abstract:
  In the age of large transformer language models, linguistic evaluation play
an important role in diagnosing models' abilities and limitations on natural
language understanding. However, current evaluation methods show some
significant shortcomings. In particular, they do not provide insight into how
well a language model captures distinct linguistic skills essential for
language understanding and reasoning. Thus they fail to effectively map out the
aspects of language understanding that remain challenging to existing models,
which makes it hard to discover potential limitations in models and datasets.
In this paper, we introduce Curriculum as a new format of NLI benchmark for
evaluation of broad-coverage linguistic phenomena. Curriculum contains a
collection of datasets that covers 36 types of major linguistic phenomena and
an evaluation procedure for diagnosing how well a language model captures
reasoning skills for distinct types of linguistic phenomena. We show that this
linguistic-phenomena-driven benchmark can serve as an effective tool for
diagnosing model behavior and verifying model learning quality. In addition,
our experiments provide insight into the limitation of existing benchmark
datasets and state-of-the-art models that may encourage future research on
re-designing datasets, model architectures, and learning objectives.


title: On coloring of graphs of girth 2l + 1 without longer odd holes

abstract:
  A hole is an induced cycle of length at least 4. Let $\l\ge 2$ be a positive
integer, let ${\cal G}_l$ denote the family of graphs which have girth $2\l+1$
and have no holes of odd length at least $2\l+3$, and let $G\in {\cal G}_{\l}$.
For a vertex $u\in V(G)$ and a nonempty set $S\subseteq V(G)$, let $d(u,
S)=\min\{d(u, v):v\in S\}$, and let $L_i(S)=\{u\in V(G) \mbox{ and } d(u,
S)=i\}$ for any integer $i\ge 0$. We show that if $G[S]$ is connected and
$G[L_i(S)]$ is bipartite for each $i\in\{1, \ldots, \lfloor{\l\over
2}\rfloor\}$, then $G[L_i(S)]$ is bipartite for each $i>0$, and consequently
$\chi(G)\le 4$, where $G[S]$ denotes the subgraph induced by $S$. Let
$\theta^-$ be the graph obtained from the Petersen graph by deleting three
vertices which induce a path, let $\theta^+$ be the graph obtained from the
Petersen graph by deleting two adjacent vertices, and let $\theta$ be the graph
obtained from $\theta^+$ by removing an edge incident with two vertices of
degree 3. For a graph $G\in{\cal G}_2$, we show that if $G$ is 3-connected and
has no unstable 3-cutset then $G$ must induce either $\theta$ or $\theta^-$ but
does not induce $\theta^+$. As corollaries, $\chi(G)\le 3$ for every graph $G$
of ${\cal G}_2$ that induces neither $\theta$ nor $\theta^-$, and minimal
non-3-colorable graphs of ${\cal G}_2$ induce no $\theta^+$.


title:
GASKAP-HI Pilot Survey Science III: An unbiased view of cold gas in the
  Small Magellanic Cloud

abstract:
  We present the first unbiased survey of neutral hydrogen (HI) absorption in
the Small Magellanic Cloud (SMC). The survey utilises pilot HI observations
with the Australian Square Kilometre Array Pathfinder (ASKAP) telescope as part
of the Galactic ASKAP HI (GASKAP-HI) project whose dataset has been processed
with the GASKAP-HI absorption pipeline, also described here. This dataset
provides absorption spectra towards 229 continuum sources, a 275% increase in
the number of continuum sources previously published in the SMC region, as well
as an improvement in the quality of absorption spectra over previous surveys of
the SMC. Our unbiased view, combined with the closely matched beam size between
emission and absorption, reveals a lower cold gas faction (11%) than the 2019
ATCA survey of the SMC and is more representative of the SMC as a whole. We
also find that the optical depth varies greatly between the SMC's bar and wing
regions. In the bar we find that the optical depth is generally low (correction
factor to the optically thin column density assumption of $\mathcal{R}_{\rm HI}
\sim 1.04$) but increases linearly with column density. In the wing however,
there is a wide scatter in optical depth despite a tighter range of column
densities.


title: Electromagnetic Quasistatic Field Formulations of Darwin Type

abstract:
  Electromagnetic quasistatic (EMQS) fields, where radiation effects are
neglected, while Ohmic losses and electric and magnetic field energies are
considered, can be modeled using Darwin-type field models as an approximation
to the full Maxwell equations. Commonly formulated in terms of magnetic vector
and electric scalar potentials, these EMQS formulations are not gauge
invariant. Several EMQS formulations resulting from different gauge equations
are considered and analyzed in terms of their structural properties and their
modeling capabilities and limitations. Associated discrete field formulations
in the context of the Maxwell-grid equations of the Finite Integration
Technique are considered in frequency and time domain and are studied with
respect to their algebraic properties. A comparison of numerical simulation
results w.r.t. reference solutions obtained with established formulations for
the full Maxwell equations are presented.


title:
Liquid-activated quantum emission from native hBN defects for
  nanofluidic sensing

abstract:
  Nanostructures made of two-dimensional (2D) materials have become the
flagship of nanofluidic discoveries in recent years. By confining liquids down
to a few atomic layers, anomalies in molecular transport and structure have
been revealed. Currently, only indirect and ensemble averaged techniques have
been able to operate in such extreme confinements, as even the smallest
molecular fluorophores are too bulky to penetrate state-of-the-art single-digit
nanofluidic systems. This strong limitation calls for the development of novel
optical approaches allowing for the direct molecular imaging of liquids
confined at the nanoscale. Here, we show that native defects present at the
surface of hexagonal boron nitride (hBN) - a widely used 2D material - can
serve as probes for molecular sensing in liquid, without compromising the
atomic smoothness of their host material. We first demonstrate that native
surface defects are readily activated through interactions with organic
solvents and confirm their quantum emission properties. Vibrational spectra of
the emitters suggest that their activation occurs through the chemisorption of
carbon-bearing liquid molecules onto native hBN defects. The correlated
activation of neighboring defects reveals single-molecule dynamics at the
interface, while defect emission spectra offer a direct readout of the local
dielectric properties of the liquid medium. We then harvest these effects in
atomically smooth slit-shaped van der Waals channels, revealing molecular
dynamics and increasing dielectric order under nanometre-scale confinement.
Liquid-activated native defects in pristine hBN bridge the gap between
solid-state nanophotonics and nanofluidics and open up new avenues for
nanoscale sensing and optofluidics.


title:
Automated Atomic Silicon Quantum Dot Circuit Design via Deep
  Reinforcement Learning

abstract:
  Robust automated design tools are crucial for the proliferation of any
computing technology. We introduce the first automated design tool for the
silicon dangling bond quantum dot computing technology, which is an extremely
versatile and flexible single-atom computing circuitry framework. The automated
designer is capable of navigating the complex, hyperdimensional design spaces
of arbitrarily sized design areas and truth tables by employing a tabula rasa
double-deep Q-learning reinforcement learning algorithm. Robust policy
convergence is demonstrated for a wide range of two-input, one-output logic
circuits and a two-input, two-output half-adder, designed with an order of
magnitude fewer SiDBs in several orders of magnitude less time than the only
other half-adder demonstrated in the literature. We anticipate that
reinforcement learning-based automated design tools will accelerate the
development of the SiDB quantum dot computing technology, leading to its
eventual adoption in specialized computing hardware.


title: COCTEAU: an Empathy-Based Tool for Decision-Making

abstract:
  Traditional approaches to data-informed policymaking are often tailored to
specific contexts and lack strong citizen involvement and collaboration, which
are required to design sustainable policies. We argue the importance of
empathy-based methods in the policymaking domain given the successes in diverse
settings, such as healthcare and education. In this paper, we introduce COCTEAU
(Co-Creating The European Union), a novel framework built on the combination of
empathy and gamification to create a tool aimed at strengthening interactions
between citizens and policy-makers. We describe our design process and our
concrete implementation, which has already undergone preliminary assessments
with different stakeholders. Moreover, we briefly report pilot results from the
assessment. Finally, we describe the structure and goals of our demonstration
regarding the newfound formats and organizational aspects of academic
conferences.


title: Current status of the problem of thermal Casimir force

abstract:
  The problem of thermal Casimir force, which consists in disagreement of
theoretical predictions of the fundamental Lifshitz theory with the measurement
data of high precision experiments and some peculiar properties of the Casimir
entropy, is reviewed. We discuss different approaches to the resolution of this
problem proposed in the literature during the last twenty years. Particular
attention is given to the role of the effects of spatial dispersion. The
recently suggested nonlocal Drude-like permittivities which take proper account
of the dissipation of conduction electrons and bring the predictions of the
Lifshitz theory in agreement with experiment and requirements of thermodynamics
are considered. The prospects of this approach in the ultimate resolution of
the problem of thermal Casimir force are evaluated.


title:
Atomic Coherence Assisted Multipartite Entanglement Generation with DELC
  Four-Wave Mixing

abstract:
  Multipartite entanglement plays an important role in quantum information
processing and quantum metrology. Here, the dressing-energy-level-cascaded
(DELC) four-wave mixing (FWM) processes are proposed to generate all-optical
controlled multipartite entanglement within a single device. The entanglement
characteristics of the produced states of light are characterized by applying
the Duan criterion and the positivity under partial transposition criterion.
Moreover, by using an internal dressing field to modulate atomic coherence,
multiple quantum coherent channels of FWM are simultaneously constructed, which
result in a great extension of entanglement mode number and quantum information
capacity. We find that the violation of the entanglement criteria inequalities
is coherent-channel dependent, and the produced states can be directly
modulated via atomic coherence. Our system can integrate the generation and
modulation of the entangled states in one process. It may help provide a
compact method for realizing large scale quantum networks.


title: Reuse your features: unifying retrieval and feature-metric alignment

abstract:
  We propose a compact pipeline to unify all the steps of Visual Localization:
image retrieval, candidate re-ranking and initial pose estimation, and camera
pose refinement. Our key assumption is that the deep features used for these
individual tasks share common characteristics, so we should reuse them in all
the procedures of the pipeline. Our DRAN (Deep Retrieval and image Alignment
Network) is able to extract global descriptors for efficient image retrieval,
use intermediate hierarchical features to re-rank the retrieval list and
produce an intial pose guess, which is finally refined by means of a
feature-metric optimization based on learned deep multi-scale dense features.
DRAN is the first single network able to produce the features for the three
steps of visual localization. DRAN achieves a competitive performance in terms
of robustness and accuracy specially in extreme day-night changes.


title:
Conserved energies for the one dimensional Gross-Pitaevskii equation:
  low regularity case

abstract:
  We construct a family of conserved energies for the one dimensional
Gross-Pitaevskii equation, but in the low regularity case (in \cite{KL} we have
constructed conserved energies in the high regularity situation). This can be
done thanks to regularization procedures and a study of the topological
structure of the finite-energy space. The asymptotic (regularised conserved)
phase change on the real line with values in $ \R/2\pi \Z$ is studied. We also
construct a conserved quantity, the renormalized momentum $H_1$ (see Theorem
\ref{thm:E1}), on the universal covering space of the finite-energy space.


title: Pseudo-Riemannian Sasaki solvmanifolds

abstract:
  We study a class of left-invariant pseudo-Riemannian Sasaki metrics on
solvable Lie groups, which can be characterized by the property that the zero
level set of the moment map relative to the action of some one-parameter
subgroup $\{\exp tX\}$ is a normal nilpotent subgroup commuting with $\{\exp
tX\}$, and $X$ is not lightlike. We characterize this geometry in terms of the
Sasaki reduction and its pseudo-K\"ahler quotient under the action generated by
the Reeb vector field.
  We classify pseudo-Riemannian Sasaki solvmanifolds of this type in dimension
$5$ and those of dimension $7$ whose K\"ahler reduction in the above sense is
abelian.


title:
Matrix Product Operator Algebras II: Phases of Matter for 1D Mixed
  States

abstract:
  The classification of topological phases of matter is fundamental to
understand and characterize the properties of quantum materials. In this paper
we study phases of matter in one-dimensional open quantum systems. We define
two mixed states to be in the same phase if both states can be transformed into
the other by a shallow circuit of local quantum channels. We aim to understand
the phase diagram of matrix product density operators that are renormalization
fixed points. These states arise, for example, as boundaries of two-dimensional
topologically ordered states. We first construct families of such states based
on C*-weak Hopf algebras, the algebras whose representations form a fusion
category. More concretely, we provide explicit local fine-graining and local
coarse-graining quantum channels for the renormalization procedure of these
states. Finally, we prove that those arising from C*-Hopf algebras are in the
trivial phase.

categories: quant-ph cond-mat.str-el math-ph math.MP


title:
Z3 Charge Density Wave of Silicon Atomic Chains on a Vicinal Silicon
  Surface

abstract:
  An ideal one-dimensional electronic system is formed along atomic chains on
Au-decorated vicinal silicon surfaces but the nature of its low temperature
phases has been puzzled for last two decades. Here, we unambiguously identify
the low temperature structural distortion of this surface using high resolution
atomic force microscopy and scanning tunneling microscopy. The most important
structural ingredient of this surface, the step-edge Si chains are found to be
strongly buckled, every third atoms down, forming trimer unitcells. This
observation is consistent with the recent model of rehybridized dangling bonds
and rules out the antiferromagnetic spin ordering proposed earlier. The
spectroscopy and electronic structure calculation indicate a charge density
wave insulator with a Z3 topology making it possible to exploit topological
phases and excitations. Tunneling current was found to substantially lower the
energy barrier between three degenerate CDW states, which induces a dynamically
fluctuating CDW at very low temperature.


title: Enabling Synthetic Data adoption in regulated domains

abstract:
  The switch from a Model-Centric to a Data-Centric mindset is putting emphasis
on data and its quality rather than algorithms, bringing forward new
challenges. In particular, the sensitive nature of the information in highly
regulated scenarios needs to be accounted for. Specific approaches to address
the privacy issue have been developed, as Privacy Enhancing Technologies.
However, they frequently cause loss of information, putting forward a crucial
trade-off among data quality and privacy. A clever way to bypass such a
conundrum relies on Synthetic Data: data obtained from a generative process,
learning the real data properties. Both Academia and Industry realized the
importance of evaluating synthetic data quality: without all-round reliable
metrics, the innovative data generation task has no proper objective function
to maximize. Despite that, the topic remains under-explored. For this reason,
we systematically catalog the important traits of synthetic data quality and
privacy, and devise a specific methodology to test them. The result is DAISYnt
(aDoption of Artificial Intelligence SYnthesis): a comprehensive suite of
advanced tests, which sets a de facto standard for synthetic data evaluation.
As a practical use-case, a variety of generative algorithms have been trained
on real-world Credit Bureau Data. The best model has been assessed, using
DAISYnt on the different synthetic replicas. Further potential uses, among
others, entail auditing and fine-tuning of generative models or ensuring high
quality of a given synthetic dataset. From a prescriptive viewpoint,
eventually, DAISYnt may pave the way to synthetic data adoption in highly
regulated domains, ranging from Finance to Healthcare, through Insurance and
Education.


title:
Active Diffusion and VCA-Assisted Image Segmentation of Hyperspectral
  Images

abstract:
  Hyperspectral images encode rich structure that can be exploited for material
discrimination by machine learning algorithms. This article introduces the
Active Diffusion and VCA-Assisted Image Segmentation (ADVIS) for active
material discrimination. ADVIS selects high-purity, high-density pixels that
are far in diffusion distance (a data-dependent metric) from other high-purity,
high-density pixels in the hyperspectral image. The ground truth labels of
these pixels are queried and propagated to the rest of the image. The ADVIS
active learning algorithm is shown to strongly outperform its fully
unsupervised clustering algorithm counterpart, suggesting that the
incorporation of a very small number of carefully-selected ground truth labels
can result in substantially superior material discrimination in hyperspectral
images.


title:
TIB-VA at SemEval-2022 Task 5: A Multimodal Architecture for the
  Detection and Classification of Misogynous Memes

abstract:
  The detection of offensive, hateful content on social media is a challenging
problem that affects many online users on a daily basis. Hateful content is
often used to target a group of people based on ethnicity, gender, religion and
other factors. The hate or contempt toward women has been increasing on social
platforms. Misogynous content detection is especially challenging when textual
and visual modalities are combined to form a single context, e.g., an overlay
text embedded on top of an image, also known as meme. In this paper, we present
a multimodal architecture that combines textual and visual features in order to
detect misogynous meme content. The proposed architecture is evaluated in the
SemEval-2022 Task 5: MAMI - Multimedia Automatic Misogyny Identification
challenge under the team name TIB-VA. Our solution obtained the best result in
the Task-B where the challenge is to classify whether a given document is
misogynous and further identify the main sub-classes of shaming, stereotype,
objectification, and violence.


title: Linear Expand-Contract Plasticity of Ellipsoids Revisited

abstract:
  This work is aimed to describe linear expand-contract plastic ellipsoids
given via quadratic form of a bounded positively defined self-adjoint operator
in terms of its spectrum.


title:
Estimation of stellar atmospheric parameters from LAMOST DR8
  low-resolution spectra with 20$\leq$SNR$<$30

abstract:
  The accuracy of the estimated stellar atmospheric parameter decreases
evidently with the decreasing of spectral signal-to-noise ratio (SNR) and there
are a huge amount of this kind observations, especially in case of SNR$<$30.
Therefore, it is helpful to improve the parameter estimation performance for
these spectra and this work studied the ($T_\texttt{eff}, \log~g$, [Fe/H])
estimation problem for LAMOST DR8 low-resolution spectra with 20$\leq$SNR$<$30.
We proposed a data-driven method based on machine learning techniques. Firstly,
this scheme detected stellar atmospheric parameter-sensitive features from
spectra by the Least Absolute Shrinkage and Selection Operator (LASSO),
rejected ineffective data components and irrelevant data. Secondly, a
Multi-layer Perceptron (MLP) method was used to estimate stellar atmospheric
parameters from the LASSO features. Finally, the performance of the LASSO-MLP
was evaluated by computing and analyzing the consistency between its estimation
and the reference from the APOGEE (Apache Point Observatory Galactic Evolution
Experiment) high-resolution spectra. Experiments show that the Mean Absolute
Errors (MAE) of $T_\texttt{eff}, \log~g$, [Fe/H] are reduced from the LASP
(137.6 K, 0.195 dex, 0.091 dex) to LASSO-MLP (84.32 K, 0.137 dex, 0.063 dex),
which indicate evident improvements on stellar atmospheric parameter
estimation. In addition, this work estimated the stellar atmospheric parameters
for 1,162,760 low-resolution spectra with 20$\leq$SNR$<$30 from LAMOST DR8
using LASSO-MLP, and released the estimation catalog, learned model,
experimental code, trained model, training data and test data for scientific
exploration and algorithm study.


title:
Constraints on Primordial Magnetic Fields from their impact on the
  ionization history with Planck 2018

abstract:
  We update and extend our previous CMB anisotropy constraints on primordial
magnetic fields through their dissipation by ambipolar diffusion and MHD
decaying turbulence effects on the post-recombination ionization history. We
derive the constraints using the latest Planck 2018 data release which improves
on the E-mode polarization leading to overall tighter constraints with respect
to Planck 2015. We also use the low-multipole E-mode polarization likelihood
obtained by the SROLL2 map making algorithm and we note how it is compatible
with larger magnetic field amplitudes than the Planck 2018 baseline, especially
for positive spectral indices. The 95% CL constraints on the amplitude of the
magnetic fields from the combination of the effects is $\sqrt{\langle B^2
\rangle} <0.69 (<0.72)$ nG for Planck 2018 (SROLL2) by marginalizing on the
magnetic spectral index. We also investigate the impact of a damping scale
allowed to vary and the interplay between the magnetic field effects and the
lensing amplitude parameter.


title: Formal Laurent series rings and the Hermite ring conjecture

abstract:
  We study the question if projective modules over formal Laurent series rings
are extended. We relate this question to the Bass-Quillen conjecture for
commutative regular local rings and to the Hermite ring conjecture for all
commutative local rings. Using our result about projective modules over formal
Laurent series rings, we prove a reduction step for the Hermite ring
conjecture. We show that the Hermite ring conjecture holds for all commutative
local rings if and only if it holds for complete intersection rings which are
also unique factorization domains.


title:
Primordial Gravitational Waves Predictions for GW170817-compatible
  Einstein-Gauss-Bonnet Theory

abstract:
  In this work we shall calculate in detail the effect of an
GW170817-compatible Einstein-Gauss-Bonnet theory on the energy spectrum of the
primordial gravitational waves. The spectrum is affected by two
characteristics, the overall amplification/damping factor caused by the
GW170817-compatible Einstein-Gauss-Bonnet theory and by the tensor spectral
index and the tensor-to-scalar ratio. We shall present the formalism for
studying the inflationary dynamics and post-inflationary dynamics of
GW170817-compatible Einstein-Gauss-Bonnet theories for all redshifts starting
from the radiation era up to the dark energy era. We exemplify our formalism by
using two characteristic models, which produce viable inflationary and dark
energy eras. As we demonstrate, remarkably the overall damping/amplification
factor is of the order of unity, thus the GW170817-compatible
Einstein-Gauss-Bonnet models affect the primordial gravitational waves energy
spectrum only via their tensor spectral index and the tensor-to-scalar ratio.
Both models have a blue tilted tensor spectrum, and therefore the predicted
energy spectrum of the primordial gravity waves can be detectable by most of
the future gravitational waves experiments, for various reheating temperatures.


title:
Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot
  Classification

abstract:
  Prompt-based learning (i.e., prompting) is an emerging paradigm for
exploiting knowledge learned by a pretrained language model. In this paper, we
propose Automatic Multi-Label Prompting (AMuLaP), a simple yet effective method
to automatically select label mappings for few-shot text classification with
prompting. Our method exploits one-to-many label mappings and a
statistics-based algorithm to select label mappings given a prompt template.
Our experiments demonstrate that AMuLaP achieves competitive performance on the
GLUE benchmark without human effort or external resources.


title:
First Order Topological Phase Transitions and Disorder Induced Majorana
  Modes in Interacting Fermion Chains

abstract:
  Using a combination of the mean-field Bogoliubov deGennes (BdG) approach and
the Density Matrix Renormalization Group (DMRG) method, we discover first order
topological transitions between topological superconducting and trivial
insulating phases in a sawtooth lattice of inter-site attractive fermions.
Topological characterization of different phases is achieved in terms of
winding numbers, Majorana edge modes and entanglement spectra. By studying the
effect of disorder on the first order topological phase transitions, we
establish the disorder-induced topological phase coexistence as a mechanism for
generating a finite density of Majorana particles.


title:
Multi-View Consistent Generative Adversarial Networks for 3D-aware Image
  Synthesis

abstract:
  3D-aware image synthesis aims to generate images of objects from multiple
views by learning a 3D representation. However, one key challenge remains:
existing approaches lack geometry constraints, hence usually fail to generate
multi-view consistent images. To address this challenge, we propose Multi-View
Consistent Generative Adversarial Networks (MVCGAN) for high-quality 3D-aware
image synthesis with geometry constraints. By leveraging the underlying 3D
geometry information of generated images, i.e., depth and camera transformation
matrix, we explicitly establish stereo correspondence between views to perform
multi-view joint optimization. In particular, we enforce the photometric
consistency between pairs of views and integrate a stereo mixup mechanism into
the training process, encouraging the model to reason about the correct 3D
shape. Besides, we design a two-stage training strategy with feature-level
multi-view joint optimization to improve the image quality. Extensive
experiments on three datasets demonstrate that MVCGAN achieves the
state-of-the-art performance for 3D-aware image synthesis.


title:
Signal-to-noise ratio analysis of single-pixel detection multiplexing
  under photon-noise. Cases of Hadamard and Cosine positive modulation

abstract:
  In typical single-pixel detection multiplexing, an unknown object is
sequentially illuminated with intensity patterns: the total signal is summed
into a single-pixel detector and is then demultiplexed to retrieve the object.
Because of measurement noise, the retrieved object differs from the ground
truth by some error quantified by the signal-to-noise ratio (SNR). In
situations where the noise only arises from the photon counting process, it has
not been made clear if single-pixel detection multiplexing leads to a better
SNR than simply scanning the object with a focused intensity spot - a modality
known as raster scanning. This study theoretically assesses the SNR associated
with certain types of single-pixel detection multiplexing, and compares it with
raster scanning. In particular, we show that, under photon noise, when the
positive intensity modulation is based on Hadamard or Cosine patterns,
single-pixel detection multiplexing does not systematically improve the SNR as
compared to raster scanning. Instead, it only improves the SNR on object pixels
at least $k$ times brighter than the object mean signal $\bar{x}$, where $k$ is
a constant that depends on the modulation scheme.


title:
Call-sign recognition and understanding for noisy air-traffic
  transcripts using surveillance information

abstract:
  Air traffic control (ATC) relies on communication via speech between pilot
and air-traffic controller (ATCO). The call-sign, as unique identifier for each
flight, is used to address a specific pilot by the ATCO. Extracting the
call-sign from the communication is a challenge because of the noisy ATC voice
channel and the additional noise introduced by the receiver. A low
signal-to-noise ratio (SNR) in the speech leads to high word error rate (WER)
transcripts. We propose a new call-sign recognition and understanding (CRU)
system that addresses this issue. The recognizer is trained to identify
call-signs in noisy ATC transcripts and convert them into the standard
International Civil Aviation Organization (ICAO) format. By incorporating
surveillance information, we can multiply the call-sign accuracy (CSA) up to a
factor of four. The introduced data augmentation adds additional performance on
high WER transcripts and allows the adaptation of the model to unseen
airspaces.


title:
Deep Learning-based Framework for Automatic Cranial Defect
  Reconstruction and Implant Modeling

abstract:
  The goal of this work is to propose a robust, fast, and fully automatic
method for personalized cranial defect reconstruction and implant modeling.
  We propose a two-step deep learning-based method using a modified U-Net
architecture to perform the defect reconstruction, and a dedicated iterative
procedure to improve the implant geometry, followed by automatic generation of
models ready for 3-D printing. We propose a cross-case augmentation based on
imperfect image registration combining cases from different datasets. We
perform ablation studies regarding different augmentation strategies and
compare them to other state-of-the-art methods.
  We evaluate the method on three datasets introduced during the AutoImplant
2021 challenge, organized jointly with the MICCAI conference. We perform the
quantitative evaluation using the Dice and boundary Dice coefficients, and the
Hausdorff distance. The average Dice coefficient, boundary Dice coefficient,
and the 95th percentile of Hausdorff distance are 0.91, 0.94, and 1.53 mm
respectively. We perform an additional qualitative evaluation by 3-D printing
and visualization in mixed reality to confirm the implant's usefulness.
  We propose a complete pipeline that enables one to create the cranial implant
model ready for 3-D printing. The described method is a greatly extended
version of the method that scored 1st place in all AutoImplant 2021 challenge
tasks. We freely release the source code, that together with the open datasets,
makes the results fully reproducible. The automatic reconstruction of cranial
defects may enable manufacturing personalized implants in a significantly
shorter time, possibly allowing one to perform the 3-D printing process
directly during a given intervention. Moreover, we show the usability of the
defect reconstruction in mixed reality that may further reduce the surgery
time.


title:
Data-driven soliton solutions and model parameters of nonlinear wave
  models via the conservation-law constrained neural network method

abstract:
  In the process of the deep learning, we integrate more integrable information
of nonlinear wave models, such as the conservation law obtained from the
integrable theory, into the neural network structure, and propose a
conservation-law constrained neural network method with the flexible learning
rate to predict solutions and parameters of nonlinear wave models. As some
examples, we study real and complex typical nonlinear wave models, including
nonlinear Schr\"odinger equation, Korteweg-de Vries and modified Korteweg-de
Vries equations. Compared with the traditional physics-informed neural network
method, this new method can more accurately predict solutions and parameters of
some specific nonlinear wave models even when less information is needed, for
example, in the absence of the boundary conditions. This provides a reference
to further study solutions of nonlinear wave models by combining the deep
learning and the integrable theory.


title: A faithful solid-state spin-wave quantum memory for polarization qubits

abstract:
  Polarization-encoded qubits are particularly useful in quantum information
tasks due to the easy transportation in a single spatial and temporal mode, the
accurate qubit manipulation and the high robustness against decoherence.
Reliable storage of polarization-encoded qubits is essential for the
construction of large-scale quantum networks. Here we demonstrate a faithful
quantum memory for photonic polarization qubits using the noiseless photon echo
protocol implemented in a rare-earth-ion doped crystal (151Eu3+:Y2SiO5). Based
on a detailed spectroscopic investigation on the 151Eu3+ ions at the site 2 of
Y2SiO5 crystals, the qubit memory is implemented using a single piece of
crystal which provides a near-uniform absorption for two orthogonal
polarization states. A process fidelity of 0.919(24) is obtained for the
storage of qubits carried by single-photon-level coherent pulses, which is
beyond the maximal fidelity that can be achieved using the classical
measure-and-prepare strategy. This compact device shall provide a useful
solution for the construction of a long-lived transportable quantum memory and
the memory-based quantum networks.


title:
Investigating the efficiency of marginalising over discrete parameters
  in Bayesian computations

abstract:
  Bayesian analysis methods often use some form of iterative simulation such as
Monte Carlo computation. Models that involve discrete variables can sometime
pose a challenge, either because the methods used do not support such variables
(e.g. Hamiltonian Monte Carlo) or because the presence of such variables can
slow down the computation. A common workaround is to marginalise the discrete
variables out of the model. While it is reasonable to expect that such
marginalisation would also lead to more time-efficient computations, to our
knowledge this has not been demonstrated beyond a few specialised models.
  We explored the impact of marginalisation on the computational efficiency for
a few simple statistical models. Specifically, we considered two- and
three-component Gaussian mixture models, and also the Dawid-Skene model for
categorical ratings. We explored each with two software implementations of
Markov chain Monte Carlo techniques: JAGS and Stan. We directly compared
marginalised and non-marginalised versions of the same model using the samplers
on the same software.
  Our results show that marginalisation on its own does not necessarily boost
performance. Nevertheless, the best performance was usually achieved with Stan,
which requires marginalisation. We conclude that there is no simple answer to
whether or not marginalisation is helpful. It is not necessarily the case that,
when turned 'on', this technique can be assured to provide computational
benefit independent of other factors, nor is it likely to be the model
component that has the largest impact on computational efficiency.


title:
Observation of a Jump Like Transition from Fast to Normal Kinetics of
  Facets for Free Growing Helium Crystals

abstract:
  The growth kinetics is studied of free-growing helium crystals within the
temperature range 0.5-0.83 K and supersaturations to 10 mbar. For the pressure
supersaturations above 0.5 mbar, numerous drastic changes are observed in the
growth kinetics for all crystalline facets from the normal and slow kinetics to
the anomalously high or burst-like one. It is found for the first time that the
anomalous to normal transition in the facet growth kinetics occurs in a jump
like manner within a time 80 {\mu}s interval of the measurement record.


title:
LED-based photo-CIDNP hyperpolarization enables 19F MR imaging and 19F
  NMR spectroscopy of 3-fluoro-DL-tyrosine at 0.6 T

abstract:
  Although 19F has high potential to serve as a background-free molecular
marker in bioimaging, the molar amount of marker substance is often too small
to enable 19F MR imaging or 19F NMR spectroscopy with a sufficiently high
signal-to-noise ratio (SNR). Hyperpolarization methods such as
parahydrogen-based hyperpolarization or dynamic nuclear polarization (DNP) can
significantly improve the SNR, but require expensive and complex sample
preparation and the removal of toxic catalysts and solvents. Therefore, we used
the biologically compatible model of the fluorinated amino acid
3-fluoro-DL-tyrosine with riboflavin 5'-monophosphate (FMN) as a chromophore
dissolved in D2O with 3.4% H2Odest. allowing to transform light energy into
hyperpolarization of the 19F nucleus via photo-chemically induced dynamic
nuclear polarization (photo-CIDNP). We used a low-cost high-power blue LED to
illuminate the sample replacing traditionally used laser excitation, which is
both potentially harmful and costly. For the first time, we present results of
hyperpolarized 19F MRI and 19F NMR performed with a low-cost 0.6 T benchtop MRI
system. The device allowed simultaneous dual channel 1H/19F NMR. 19F imaging
was performed with a (0.94 mm)2 in-plane resolution. This enabled the spatial
resolution of different degrees of hyperpolarization within the sample. We
estimated the photo-CIDNP-based 19F signal enhancement at 0.6 T to be
approximately 465. FMN did not bleach out even after multiple excitations, so
that the signal-to-noise ratio could be further improved by averaging
hyperpolarized signals. The results show that the easy-to-use experimental
setup has a high potential to serve as an efficient preclinical tool for
hyperpolarization studies in bioimaging.


title: The Ceresa class and tropical curves of hyperelliptic type

abstract:
  We define a new algebraic invariant of a graph $G$ called the Ceresa-Zharkov
class and show that it is trivial if and only if $G$ is of hyperelliptic type,
equivalently, $G$ does not have as a minor the complete graph on 4 vertices or
the loop of 3 loops. After choosing edge-lengths, this class specializes to an
algebraic invariant of a tropical curve with underlying graph $G$ that is
closely related to the Ceresa cycle for an algebraic curve defined over
$\mathbb{C}(\!(t)\!)$.


title:
Several problems on reduced spherical polygons of thickness less than
  {\pi}/2

abstract:
  The present paper aims to solve some problems proposed by Lassak about the
reduced spherical polygons. The main result is to show that the regular
spherical n-gon has the minimal perimeter among all reduced spherical polygons
of fixed thickness less than {\pi}/2 and with at most n vertices. In addition,
we determine the maximal diameter of every reduced spherical polygons with a
fixed thickness less than {\pi}/2. We also find the smallest spherical radius
that contains every reduced spherical polygons with a fixed thickness less than
{\pi}/2.


title:
Exploring coherent elastic neutrino-nucleus scattering using reactor
  electron antineutrinos in the NEON experiment

abstract:
  Neutrino Elastic scattering Observation with NaI (NEON) is an experiment
designed to detect neutrino-nucleus coherent scattering using reactor electron
antineutrinos. NEON is based on an array of six NaI(Tl) crystals with a total
mass of 13.3 kg, located at the tendon gallery that is 24 m away from a reactor
core with a thermal power of 2.8 GW in the Hanbit nuclear power complex. The
installation of the NEON detector was completed in December 2020, and since May
2021, the detector has been acquiring data at full reactor power. This paper
describes the design of the NEON detector, including the shielding arrangement,
configuration of the NaI(Tl) crystals, and associated operation systems. The
initial performance and associated sensitivity of the experiment are presented.


title:
A "parallel universe" scheme for crack nucleation in the phase field
  approach to fracture

abstract:
  Crack nucleation is crucial in many industrial applications. The phase field
method for fracture transforms the crack nucleation problem into a minimization
problem of the sum of the elastic potential energy and the crack surface
energy. Due to the polyconvexity of the formulation, starting from a crackless
solid, a standard Newton iteration may lead to a solution with no crack, even
though a cracked solution has a lower total energy. As such, the critical load
for cracking is highly overestimated. Here, we propose an algorithm termed
"parallel universe" algorithm to capture the global minimum. This algorithm has
two key ingredients: (a) a necessary condition for cracking solely based on the
current crackless solution, and (b) beginning from when this condition is met,
Newton iteration with two initial guesses, a crackles one and a cracked one,
will both be performed and the converged candidate solution with lower energy
is accepted as the solution at that load step. Once the cracked candidate
solution is accepted, the crackless one is discarded, i.e., only one universe
is retained. This cracked initial guess is obtained only once for all load
steps by solving a series of similar minimization problems with a progressively
reduced critical crack energy release rate. Numerical examples with isotropic
and anisotropic critical crack energy release rates indicate that the proposed
algorithm is more reliable (as there is no need to retrace) and more efficient
than the standard Newton iteration and a well-known backtracking algorithm.


title: Eigenvector in Non-Commutative Algebra

abstract:
  $\newcommand{\Vector}[1]{\bar{#1}{}}$
$\newcommand{\Basis}[1]{\bar{\bar{#1}}{}}$ $\newcommand{\RC}{{}_*{}^*-}$ Let
$\Basis e$ be a basis of vector space $V$ over non-commutative $D$-algebra $A$.
Endomorhism $\Vector{\Basis eb}$ of vector space $V$ whose matrix with respect
to given basis $\Basis e$ has form $Eb$ where $E$ is identity matrix is called
similarity transformation with respect to the basis $\Basis e$. Let $V$ be a
left $A$-vector space and $\Basis e$ be basis of left $A$-vector space $V$. The
vector $v\in V$ is called eigenvector of the endomorphism \[\Vector
f:V\rightarrow V\] with respect to the basis $\Basis e$, if there exists $b\in
A$ such that \[ \Vector f\circ{v}= \Vector{\Basis eb} \circ{v} \] $A$-number
$b$ is called eigenvalue of the endomorphism $\Vector f$ with respect to the
basis $\Basis e$. There are two products of matrices: ${}_*{}^*$ (row column:
$(ab)^i_j=a^i_kb^k_j$) and ${}^*{}_*$ (column row: $(ab)^i_j=a^k_jb^i_k$).
$A$-number $b$ is called $\RC$ eigenvalue of the matrix $f$ if the matrix
$f-bE$ is $\RC$ singular matrix. The $A$-number $b$ is called right $\RC$
eigenvalue if there exists the column vector $u$ which satisfies to the
equality \[a{}_*{}^* u=ub\] The column vector $u$ is called eigencolumn for
right $\RC$ eigenvalue $b$. The $A$-number $b$ is called left $\RC$ eigenvalue
if there exists the row vector $u$ which satisfies to the equality \[u{}_*{}^*
a=bu\] The row vector $u$ is called eigenrow for right $\RC$ eigenvalue $b$.
The set $\RC$ $\mathrm{spec}(a)$ of all left and right $\RC$ eigenvalues is
called $\RC$ spectrum of the matrix a.


title: A Case Study on Identifying Bifurcation and Chaos with CROCKER Plots

abstract:
  The CROCKER plot is a coarsened but easy to visualize representation of the
data in a one-parameter varying family of persistence barcodes. In this paper,
we use the CROCKER plot to view changes in the persistence under a varying
bifurcation parameter. We perform experiments to support our methods using the
R\"ossler and Lorenz system and show the relationship with common methods for
bifurcation analysis such as the Lyapunov exponent.


title:
Production federated keyword spotting via distillation, filtering, and
  joint federated-centralized training

abstract:
  We trained a keyword spotting model using federated learning on real user
devices and observed significant improvements when the model was deployed for
inference on phones. To compensate for data domains that are missing from
on-device training caches, we employed joint federated-centralized training.
And to learn in the absence of curated labels on-device, we formulated a
confidence filtering strategy based on user-feedback signals for federated
distillation. These techniques created models that significantly improved
quality metrics in offline evaluations and user-experience metrics in live A/B
experiments.


title: First principles cosmology of the Standard Model epoch

abstract:
  This is a short summary of a project to construct a first principles
cosmology of the Standard Model epoch, the period starting shortly before the
electro-weak transition. The cosmology is derived from a simple initial state
entirely within the SM and General Relativity. The initial state is
semi-classical -- concentrated near a classical solution of the SM equations of
motion -- and is precisely specified by a few simple conditions. The dark
matter is a classical effect, a coherent state of the SU(2)-weak gauge field
and the Higgs field. The leading order, classical universe contains only the
dark matter. Ordinary matter is a correction due to the fluctuations of the SM
fields around the classical trajectory. The initial state produces a
homogeneous, isotropic, flat universe. There are no adjustable parameters. No
physics beyond the SM is invoked. Only the classical calculations have been
done so far. The time evolution of the fluctuations remains to be calculated.