title:
Tiramisu: Layering Consensus Protocols for Scalable and Secure
  Blockchains

abstract:
  Cryptocurrencies are poised to revolutionize the modern economy by
democratizing commerce. These currencies operate on top of blockchain-based
distributed ledgers. Existing permissionless blockchain-based protocols offer
unparalleled benefits like decentralization, anonymity, and transparency.
However, these protocols suffer in performance which hinders their widespread
adoption. In particular, high time-to-finality and low transaction rates keep
them from replacing centralized payment systems such as the Visa network.
Permissioned blockchain protocols offer attractive performance guarantees, but
they are not considered suitable for deploying decentralized cryptocurrencies
due to their centralized nature. Researchers have developed several
multi-layered blockchain protocols that combine both permissioned and
permissionless blockchain protocols to achieve high performance along with
decentralization. The key idea with existing layered blockchain protocols in
literature is to divide blockchain operations into two layers and use different
types of blockchain protocols to manage each layer. However, many such works
come with the assumptions of honest majority which may not accurately reflect
the real world where the participants may be self-interested or rational. These
assumptions may render the protocols susceptible to security threats in the
real world, as highlighted by the literature focused on exploring
game-theoretic attacks on these protocols. We generalize the "layered" approach
taken by existing protocols in the literature and present a framework to
analyze the system in the BAR Model and provide a generalized game-theoretic
analysis of such protocols. Using our analysis, we identify the critical system
parameters required for a distributed ledger's secure operation in a more
realistic setting.


title:
An In-Depth Comparative Analysis of Cloud Block Storage Workloads:
  Findings and Implications

abstract:
  Cloud block storage systems support diverse types of applications in modern
cloud services. Characterizing their I/O activities is critical for guiding
better system designs and optimizations. In this paper, we present an in-depth
comparative analysis of production cloud block storage workloads through the
block-level I/O traces of billions of I/O requests collected from two
production systems, Alibaba Cloud and Tencent Cloud Block Storage. We study
their characteristics of load intensities, spatial patterns, and temporal
patterns. We also compare the cloud block storage workloads with the notable
public block-level I/O workloads from the enterprise data centers at Microsoft
Research Cambridge, and identify the commonalities and differences of the three
sources of traces. To this end, we provide six findings through the high-level
analysis and 16 findings through the detailed analysis on load intensity,
spatial patterns, and temporal patterns. We discuss the implications of our
findings on load balancing, cache efficiency, and storage cluster management in
cloud block storage systems.


title: Magnon squeezing enhanced ground-state cooling in cavity magnomechanics

abstract:
  Cavity magnomechanics has recently become a new platform for studying
macroscopic quantum phenomena. The magnetostriction induced vibration mode of a
large-size ferromagnet or ferrimagnet reaching its ground state represents a
genuine macroscopic quantum state. Here we study the ground-state cooling of
the mechanical vibration mode in a cavity magnomechanical system, and focus on
the role of magnon squeezing in improving the cooling efficiency. We find that
the magnon squeezing can significantly and even completely suppress the
magnomechanical Stokes scattering. It thus becomes particularly useful in
realizing ground-state cooling in the unresolved-sideband regime, where the
conventional sideband cooling protocols become inefficient. We also find that
the coupling to the microwave cavity plays only an adverse effect in mechanical
cooling. This makes essentially the two-mode magnomechanical system (without
involving the microwave cavity) a preferred system for cooling the mechanical
motion, in which the magnon mode is established by a uniform bias magnetic
field and a microwave drive field.


title: Upsampling Autoencoder for Self-Supervised Point Cloud Learning

abstract:
  In computer-aided design (CAD) community, the point cloud data is pervasively
applied in reverse engineering, where the point cloud analysis plays an
important role. While a large number of supervised learning methods have been
proposed to handle the unordered point clouds and demonstrated their remarkable
success, their performance and applicability are limited to the costly data
annotation. In this work, we propose a novel self-supervised pretraining model
for point cloud learning without human annotations, which relies solely on
upsampling operation to perform feature learning of point cloud in an effective
manner. The key premise of our approach is that upsampling operation encourages
the network to capture both high-level semantic information and low-level
geometric information of the point cloud, thus the downstream tasks such as
classification and segmentation will benefit from the pre-trained model.
Specifically, our method first conducts the random subsampling from the input
point cloud at a low proportion e.g., 12.5%. Then, we feed them into an
encoder-decoder architecture, where an encoder is devised to operate only on
the subsampled points, along with a upsampling decoder is adopted to
reconstruct the original point cloud based on the learned features. Finally, we
design a novel joint loss function which enforces the upsampled points to be
similar with the original point cloud and uniformly distributed on the
underlying shape surface. By adopting the pre-trained encoder weights as
initialisation of models for downstream tasks, we find that our UAE outperforms
previous state-of-the-art methods in shape classification, part segmentation
and point cloud upsampling tasks. Code will be made publicly available upon
acceptance.


title: ASE: Anomaly Scoring Based Ensemble Learning for Imbalanced Datasets

abstract:
  Nowadays, many industries have applied classification algorithms to help them
solve problems in their business, like finance, medicine, manufacturing
industry and so on. However, in real-life scenarios, positive examples only
make up a small part of all instances and our datasets suffer from high
imbalance ratio which leads to poor performance of existing classification
models. To solve this problem, we come up with a bagging ensemble learning
framework based on an anomaly detection scoring system. We test out that our
ensemble learning model can dramatically improve performance of base estimators
(e.g. Decision Tree, Multilayer perceptron, KNN) and is more efficient than
other existing methods under a wide range of imbalance ratio, data scale and
data dimension.


title:
Realization of superchiral surface lattice resonances in
  three-dimensional bipartite nanoparticle arrays

abstract:
  Optical chirality (OC) is a fundamental property of electromagnetic waves
that plays a key role in governing chiral light-matter interaction. Here, we
demonstrate how to obtain superchiral surface lattice resonances (SLRs), which
arise from the hybridization between localized surface plasmons (LSPs) and
diffractive Rayleigh anomalies (RAs), in nanoparticle arrays. We first study
the coupling constants between LSPs and RAs in 2D Au monopartite nanorod arrays
by angle-resolved reflectivity spectroscopy and finite-difference time-domain
(FDTD) simulations. The complex dispersion relations and the near-fields of
SLRs are then analyzed by temporal coupled-mode theory (CMT) for formulating
the dependence of the coupling constants on the dipole orientation of the
nanorod. The TE and TM coupling constants are found to depend strongly on the
orientation of the dipole lying in the plane perpendicular to the propagation
direction of RA. By using two orthogonally oriented nanorods, TE- and TM-SLRs
can be excited independently. We then extend the CMT approach to rationally
design superchiral SLRs based on 3D bipartite nanorods. The OC is shown to
depend on the relative displacement between two nanorods, the near-field
strength, and the interplay between the coupling constants and the Q-factor of
the SLR resonance. We have achieved an averaged OC of 27 times stronger than
that of the circularly polarized plane wave over the entire surface, featuring
large area chiral surface waves that are useful for chirality-based
applications.


title:
Intelligent control of a single-link flexible manipulator using sliding
  modes and artificial neural networks

abstract:
  This letter presents a new intelligent control scheme for the accurate
trajectory tracking of flexible link manipulators. The proposed approach is
mainly based on a sliding mode controller for underactuated systems with an
embedded artificial neural network to deal with modeling inaccuracies. The
adopted neural network only needs a single input and one hidden layer, which
drastically reduces the computational complexity of the control law and allows
its implementation in low-power microcontrollers. Online learning, rather than
supervised offline training, is chosen to allow the weights of the neural
network to be adjusted in real time during the tracking. Therefore, the
resulting controller is able to cope with the underactuating issues and to
adapt itself by learning from experience, which grants the capacity to deal
with plant dynamics properly. The boundedness and convergence properties of the
tracking error are proved by evoking Barbalat's lemma in a Lyapunov-like
stability analysis. Experimental results obtained with a small single-link
flexible manipulator show the efficacy of the proposed control scheme, even in
the presence of a high level of uncertainty and noisy signals.


title: How Expressive Are Friendly School Partitions?

abstract:
  A natural procedure for assigning students to classes in the beginning of the
school-year is to let each student write down a list of $d$ other students with
whom she/he wants to be in the same class (typically $d=3$). The teachers then
gather all the lists and try to assign the students to classes in a way that
each student is assigned to the same class with at least one student from
her/his list. We refer to such partitions as friendly. In realistic scenarios,
the teachers may also consider other constraints when picking the friendly
partition: e.g. there may be a group of students whom the teachers wish to
avoid assigning to the same class; alternatively, there may be two close
friends whom the teachers want to put together; etc.
  Inspired by such challenges, we explore questions concerning the
expressiveness of friendly partitions. For example: Does there always exist a
friendly partition? More generally, how many friendly partitions are there? Can
every student $u$ be separated from any other student $v$? Does there exist a
student $u$ that can be separated from any other student $v$?
  We show that when $d\geq 3$ there always exist at least $2$ friendly
partitions and when $d\geq 15$ there always exists a student $u$ which can be
separated from any other student $v$. The question regarding separability of
each pair of students is left open, but we give a positive answer under the
additional assumption that each student appears in at most roughly $\exp(d)$
lists. We further suggest several open questions and present some preliminary
findings towards resolving them.


title:
Slice Imputation: Intermediate Slice Interpolation for Anisotropic 3D
  Medical Image Segmentation

abstract:
  We introduce a novel frame-interpolation-based method for slice imputation to
improve segmentation accuracy for anisotropic 3D medical images, in which the
number of slices and their corresponding segmentation labels can be increased
between two consecutive slices in anisotropic 3D medical volumes. Unlike
previous inter-slice imputation methods, which only focus on the smoothness in
the axial direction, this study aims to improve the smoothness of the
interpolated 3D medical volumes in all three directions: axial, sagittal, and
coronal. The proposed multitask inter-slice imputation method, in particular,
incorporates a smoothness loss function to evaluate the smoothness of the
interpolated 3D medical volumes in the through-plane direction (sagittal and
coronal). It not only improves the resolution of the interpolated 3D medical
volumes in the through-plane direction but also transforms them into isotropic
representations, which leads to better segmentation performances. Experiments
on whole tumor segmentation in the brain, liver tumor segmentation, and
prostate segmentation indicate that our method outperforms the competing slice
imputation methods on both computed tomography and magnetic resonance images
volumes in most cases.


title: Fictitious Play with Maximin Initialization

abstract:
  Fictitious play has recently emerged as the most accurate scalable algorithm
for approximating Nash equilibrium strategies in multiplayer games. We show
that the degree of equilibrium approximation error of fictitious play can be
significantly reduced by carefully selecting the initial strategies. We present
several new procedures for strategy initialization and compare them to the
classic approach, which initializes all pure strategies to have equal
probability. The best-performing approach, called maximin, solves a nonconvex
quadratic program to compute initial strategies and results in a nearly 75%
reduction in approximation error compared to the classic approach when 5
initializations are used.


title: Modified Method of Moments for Generalized Laplace Distributions

abstract:
  In this short note, we demonstrate the failure of the classic method of
moments for parameter estimation of symmetric variance-gamma (generalized
Laplace) distributions. This disproves the claim found in the literature that
method of moments works well for generalized asymmetric Laplace distributions
and related models. We use both theoretical analysis (multivariate delta
method) and simulations to make our case. Finally, we modify the method of
moments by taking absolute moments to improve efficiency.


title:
K-space and Image Domain Collaborative Energy based Model for Parallel
  MRI Reconstruction

abstract:
  Decreasing magnetic resonance (MR) image acquisition times can potentially
make MR examinations more accessible. Prior arts including the deep learning
models have been devoted to solving the problem of long MRI imaging time.
Recently, deep generative models have exhibited great potentials in algorithm
robustness and usage flexibility. Nevertheless, no existing such schemes that
can be learned or employed directly to the k-space measurement. Furthermore,
how do the deep generative models work well in hybrid domain is also worth to
be investigated. In this work, by taking advantage of the deep en-ergy-based
models, we propose a k-space and image domain collaborative generative model to
comprehensively estimate the MR data from under-sampled measurement.
Experimental comparisons with the state-of-the-arts demonstrated that the
proposed hybrid method has less error in reconstruction and is more stable
under different acceleration factors.


title: Vulnerability-CoVaR: Investigating the Crypto-market

abstract:
  This paper proposes an important extension to Conditional Value-at-Risk
(CoVaR), the popular systemic risk measure, and investigates its properties on
the cryptocurrency market. The proposed Vulnerability-CoVaR (VCoVaR) is defined
as the Value-at-Risk (VaR) of a financial system or institution, given that at
least one other institution is equal or below its VaR. The VCoVaR relaxes
normality assumptions and is estimated via copula. While important theoretical
findings of the measure are detailed, the empirical study analyzes how
different distressing events of the cryptocurrencies impact the risk level of
each other. The results show that Litecoin displays the largest impact on
Bitcoin and that each cryptocurrency is significantly affected if an event of
joint distress among the remaining market participants occurs. The VCoVaR is
shown to capture domino effects better than other CoVaR extensions.


title: Delving into the Estimation Shift of Batch Normalization in a Network

abstract:
  Batch normalization (BN) is a milestone technique in deep learning. It
normalizes the activation using mini-batch statistics during training but the
estimated population statistics during inference. This paper focuses on
investigating the estimation of population statistics. We define the estimation
shift magnitude of BN to quantitatively measure the difference between its
estimated population statistics and expected ones. Our primary observation is
that the estimation shift can be accumulated due to the stack of BN in a
network, which has detriment effects for the test performance. We further find
a batch-free normalization (BFN) can block such an accumulation of estimation
shift. These observations motivate our design of XBNBlock that replace one BN
with BFN in the bottleneck block of residual-style networks. Experiments on the
ImageNet and COCO benchmarks show that XBNBlock consistently improves the
performance of different architectures, including ResNet and ResNeXt, by a
significant margin and seems to be more robust to distribution shift.


title: Adaptive and Cascaded Compressive Sensing

abstract:
  Scene-dependent adaptive compressive sensing (CS) has been a long pursuing
goal which has huge potential in significantly improving the performance of CS.
However, without accessing to the ground truth image, how to design the
scene-dependent adaptive strategy is still an open-problem and the improvement
in sampling efficiency is still quite limited. In this paper, a restricted
isometry property (RIP) condition based error clamping is proposed, which could
directly predict the reconstruction error, i.e. the difference between the
currently-stage reconstructed image and the ground truth image, and adaptively
allocate samples to different regions at the successive sampling stage.
Furthermore, we propose a cascaded feature fusion reconstruction network that
could efficiently utilize the information derived from different adaptive
sampling stages. The effectiveness of the proposed adaptive and cascaded CS
method is demonstrated with extensive quantitative and qualitative results,
compared with the state-of-the-art CS algorithms.


title: Tripartite entanglement and matrix inversion quantum algorithm

abstract:
  The role of entanglement is discussed in the Harrow-Hassidim-Lloyd (HHL)
algorithm. We compute all tripartite entanglement at every steps of the HHL
algorithm. The tripartite entanglement is generated in the first quantum phase
estimation (QPE) step. However, it turns out that amount of the generated
entanglement is not maximal except very rare cases. In the second rotation step
some tripartite entanglement is annihilated. Thus, the net tripartite
entanglement is diminished. At the final inverse-QPE step the matrix inversion
task is completed at the price of complete annihilation of the entanglement. An
implication of this result is discussed.


title:
Effect of Macroscopic Surface Heterogeneities on an Advancing Contact
  Line

abstract:
  The shape of a liquid-air interface advancing on a heterogeneous surface was
studied experimentally, together with the force induced by the pinning of the
contact line to surface defects. Different surfaces were considered with
circular defects introduced as arrays of cocoa butter patches or small circular
holes. These heterogeneous surfaces were submerged into aqueous ethanol
solutions, while measuring the additional force arising from the deformation of
the advancing contact line and characterising the interface shape and its
pinning on the defects. Initially, the submersion force is linear with
submerged depth, suggesting a constant defect-induced stiffness. This regime
ends when the contact line de pins from the defects. A simple scaling is
proposed to describe the depinning force and the depinning energy. We find
that, as the defect separation increases, the interface stiffness increases
too, with a weak dependency on the defect radius. This interaction between
defects cannot be captured by the simple scaling, but can be well predicted by
a theory considering the interface deformation in presence of periodic arrays
of holes. Creating a 4-phase contact line, by including solid defects (cocoa
butter) reduced pinning forces. The radius of the defect had a nonlinear effect
on the depinning depth. The 4-phase contact line resulted in depinning before
the defects are fully submerged. These experimental results and the associated
theory help understanding quantitatively the extent to which surface
heterogeneities can slow down wetting. This in turn paves the way to tailor the
design of heterogeneous surfaces, towards desired wetting performances.


title: Generalized integral means spectrum of SLE

abstract:
  We extend the validity domain of the conjecture about the average generalized
integral means spectrum of whole-plane SLE introduced in [7, 8]. Thence we
improve the results obtained in [7, 8] on the average generalized integral
means spectrum of the m-fold transform associated to the wholeplane SLE map. An
extension to a new interval of parameter for the usual average integral means
spectrum of the interior whole-plane SLE is also given.


title: Simple and Efficient LoRa Receiver Scheme for Multi-Path Channel

abstract:
  This paper presents a novel LoRa (Long Range) receiver operating in frequency
selective Multi-Path Channel (MPC). The dechirped received LoRa wave-forms
under MPC allows us to derive a simple and efficient LoRa receiver scheme by
using a MF (Matched Filter) approach that aims to maximize the SNR
(Signal-to-Noise Ratio) at the symbol index frequency of the DFT output. We
show that the MF receiver can be seen as RAKE structure where interference
peaks related to multipath, exhibited at DFT output, are recombined in a
constructive way. Detection performance is driven by channel energy and the
benefit of this novel MF/RAKE receiver over original coherent and non-coherent
receiver appears only for MPC that exhibits significant paths energy. These two
MF and RAKE receivers have however different implementation complexities that
are studied in details. We provide in that sense recommendations on which
receiver variant to use for real operations, depending on complexity
constraints. Finally, the proposed MF/RAKE receiver outperforms previous
results on TDEL (Time Delay Estimation LoRa) receiver over MPC, especially at
low SNR and higher LoRa Spreading Factor (SF) parameter, at the cost of higher
but reasonable complexity.


title:
Hybrid Quantum-Classical Boson Sampling Algorithm for Molecular
  Vibrationally Resolved Electronic Spectroscopy with Duschinsky Rotation and
  Anharmonicity

abstract:
  Using a photonic quantum computer for boson sampling has been demonstrated a
tremendous advantage over classical supercomputers. It is highly desirable to
develop boson sampling algorithms for realistic scientific problems. In this
work, we propose a hybrid quantum-classical sampling (HQCS) algorithm to
calculate the optical spectrum for complex molecules considering anharmonicity
and Duschinsky rotation (DR) effects. The classical sum-over-state method for
this problem has a computational complexity that exponentially increases with
system size. In the HQCS algorithm, an intermediate harmonic potential energy
surface (PES) is created, bridging the initial and final PESs. The magnitude
and sign (-1 or +1) of the overlap between the initial state and the
intermediate state are estimated by quantum boson sampling and by classical
algorithms respectively, achieving an exponential speed-up. Additionally, the
overlap between the intermediate state and the final state is efficiently
evaluated by classical algorithms. The feasibility of HQCS is demonstrated in
calculations of the emission spectrum of a Morse model as well as pyridine
molecule by comparison with the nearly exact time-dependent density matrix
renormalization group solutions. A near-term quantum advantage for realistic
molecular spectroscopy simulation is proposed.


title:
GroupTransNet: Group Transformer Network for RGB-D Salient Object
  Detection

abstract:
  Salient object detection on RGB-D images is an active topic in computer
vision. Although the existing methods have achieved appreciable performance,
there are still some challenges. The locality of convolutional neural network
requires that the model has a sufficiently deep global receptive field, which
always leads to the loss of local details. To address the challenge, we propose
a novel Group Transformer Network (GroupTransNet) for RGB-D salient object
detection. This method is good at learning the long-range dependencies of cross
layer features to promote more perfect feature expression. At the beginning,
the features of the slightly higher classes of the middle three levels and the
latter three levels are soft grouped to absorb the advantages of the high-level
features. The input features are repeatedly purified and enhanced by the
attention mechanism to purify the cross modal features of color modal and depth
modal. The features of the intermediate process are first fused by the features
of different layers, and then processed by several transformers in multiple
groups, which not only makes the size of the features of each scale unified and
interrelated, but also achieves the effect of sharing the weight of the
features within the group. The output features in different groups complete the
clustering staggered by two owing to the level difference, and combine with the
low-level features. Extensive experiments demonstrate that GroupTransNet
outperforms the comparison models and achieves the new state-of-the-art
performance.


title:
Classifications of Skull Fractures using CT Scan Images via CNN with
  Lazy Learning Approach

abstract:
  Classification of skull fracture is a challenging task for both radiologists
and researchers. Skull fractures result in broken pieces of bone, which can cut
into the brain and cause bleeding and other injury types. So it is vital to
detect and classify the fracture very early. In real world, often fractures
occur at multiple sites. This makes it harder to detect the fracture type where
many fracture types might summarize a skull fracture. Unfortunately, manual
detection of skull fracture and the classification process is time-consuming,
threatening a patient's life. Because of the emergence of deep learning, this
process could be automated. Convolutional Neural Networks (CNNs) are the most
widely used deep learning models for image categorization because they deliver
high accuracy and outstanding outcomes compared to other models. We propose a
new model called SkullNetV1 comprising a novel CNN by taking advantage of CNN
for feature extraction and lazy learning approach which acts as a classifier
for classification of skull fractures from brain CT images to classify five
fracture types. Our suggested model achieved a subset accuracy of 88%, an F1
score of 93%, the Area Under the Curve (AUC) of 0.89 to 0.98, a Hamming score
of 92% and a Hamming loss of 0.04 for this seven-class multi-labeled
classification.


title:
McKean-Vlasov Equations with Positive Feedback through Elastic Stopping
  Times

abstract:
  We prove existence and uniqueness of physical and minimal solutions to
McKean-Vlasov equations with positive feedback through elastic stopping times.
We do this by establishing a relationship between this problem and a problem
with absorbing stopping times. We show convergence of a particle system to the
McKean-Vlasov equation. Moreover, we establish convergence of the elastic
McKean-Vlasov problem to the problem with absorbing stopping times and to a
reflecting Brownian motion as the elastic parameter goes to infinity or zero
respectively.


title:
Computing the least action ground state of the nonlinear Schr\"odinger
  equation by a normalized gradient flow

abstract:
  In this paper, we generalize the normalized gradient flow method which is
first applied to computing the least energy ground state to compute the least
action ground state. A continuous normalized gradient flow (CNGF) will be
presented and the action decreasing property will be proved to provide a
mathematical justification of the gradient flow with discrete normalization
(GFDN). Then we use backward-forward Euler method to further discretize the
GFDN in time which leads to the GFDN-BF scheme. We compare it with other three
schemes which are modified from corresponding ones designed for the least
energy ground state and the numerical results show that the GFDN-BF scheme
performs much better than the others in accuracy, efficiency and robustness for
large time steps. Extensive numerical results of least action ground states for
several types of potentials are provided. We also use our numerical results to
verify some existing results and lead to some conjectures.


title:
Domain Generalization by Mutual-Information Regularization with
  Pre-trained Models

abstract:
  Domain generalization (DG) aims to learn a generalized model to an unseen
target domain using only limited source domains. Previous attempts to DG fail
to learn domain-invariant representations only from the source domains due to
the significant domain shifts between training and test domains. Instead, we
re-formulate the DG objective using mutual information with the oracle model, a
model generalized to any possible domain. We derive a tractable variational
lower bound via approximating the oracle model by a pre-trained model, called
Mutual Information Regularization with Oracle (MIRO). Our extensive experiments
show that MIRO significantly improves the out-of-distribution performance.
Furthermore, our scaling experiments show that the larger the scale of the
pre-trained model, the greater the performance improvement of MIRO. Source code
is available at https://github.com/kakaobrain/miro.


title:
ScalableViT: Rethinking the Context-oriented Generalization of Vision
  Transformer

abstract:
  The vanilla self-attention mechanism inherently relies on pre-defined and
steadfast computational dimensions. Such inflexibility restricts it from
possessing context-oriented generalization that can bring more contextual cues
and global representations. To mitigate this issue, we propose a Scalable
Self-Attention (SSA) mechanism that leverages two scaling factors to release
dimensions of query, key, and value matrix while unbinding them with the input.
This scalability fetches context-oriented generalization and enhances object
sensitivity, which pushes the whole network into a more effective trade-off
state between accuracy and cost. Furthermore, we propose an Interactive
Window-based Self-Attention (IWSA), which establishes interaction between
non-overlapping regions by re-merging independent value tokens and aggregating
spatial information from adjacent windows. By stacking the SSA and IWSA
alternately, the Scalable Vision Transformer (ScalableViT) achieves
state-of-the-art performance in general-purpose vision tasks. For example,
ScalableViT-S outperforms Twins-SVT-S by 1.4% and Swin-T by 1.8% on ImageNet-1K
classification.


title: IoT Data Discovery: Routing Table and Summarization Techniques

abstract:
  In this paper, we consider the IoT data discovery problem in very large and
growing scale networks. Through analysis, examples, and experimental studies,
we show the importance of peer-to-peer, unstructured routing for IoT data
discovery and point out the space efficiency issue that has been overlooked in
keyword-based routing algorithms in unstructured networks. Specifically, as the
first in the field, this paper investigates routing table designs and various
compression techniques to support effective and space-efficient IoT data
discovery routing. Novel summarization algorithms, including alphabetical,
hash, and meaning-based summarization and their corresponding coding schemes,
are proposed. We also consider routing table design to support summarization
without degrading lookup efficiency for discovery query routing. The issue of
potentially misleading routing due to summarization is also investigated.
Subsequently, we analyze the strategy of when to summarize to balance the
tradeoff between the routing table compression rate and the chance of causing
misleading routing. For the experimental study, we have collected 100K IoT data
streams from various IoT databases as the input dataset. Experimental results
show that our summarization solution can reduce the routing table size by 20 to
30 folds with a 2-5% increase in latency compared with similar peer-to-peer
discovery routing algorithms without summarization. Also, our approach
outperforms DHT-based approaches by 2 to 6 folds in terms of latency and
traffic.


title:
Nuclear polarizability in muonic atoms: Bayesian analysis of the
  $\eta$-expansion uncertainty

abstract:
  Over the last decade, spectroscopy of muonic atoms has been used to extract
nuclear charge radii with unprecedented precision. This extraction requires a
careful analysis of the interplay between theoretical and experimental results,
and the attainable precision is nowadays limited by the large uncertainties
associated with the theoretical evaluation of the nuclear polarizability
effects. To facilitate calculations, these polarizability corrections are
conventionally expressed as expansions in a dimensionless parameter $\eta$.
Based on the uncertainty principle, $\eta$ has been argued to hold a value of
approximately 0.33 in light-nuclear systems. In this work, we check this claim
by performing a Bayesian analysis of the nuclear-polarizability corrections to
the Lamb shift in $\mu{}^2$H and $\mu{}^3$H atoms and in $\mu{}^3$He$^+$ and
$\mu{}^4$He$^+$ ions at various orders in the $\eta$-expansion. We derive the
Bayesian posterior probability distributions for the values of $\eta$, check
for their sensitivity to several reasonable choices of the Bayesian priors, and
estimate the uncertainties due to truncation of the $\eta$-expansion. This
analysis supports the claim that $\eta\ll1$ for these systems. Overall, the
68\% credible intervals are in good agreement with the simpler truncation
uncertainty estimates previously reported in the literature, with the only
exception of the $\mu {}^3$He$^+$ ion, for which we find larger uncertainties.


title: Phase-Aware Spoof Speech Detection Based on Res2Net with Phase Network

abstract:
  The spoof speech detection (SSD) is the essential countermeasure for
automatic speaker verification systems. Although SSD with magnitude features in
the frequency domain has shown promising results, the phase information also
can be important to capture the artefacts of certain types of spoofing attacks.
Thus, both magnitude and phase features must be considered to ensure the
generalization ability to diverse types of spoofing attacks. In this paper, we
investigate the failure reason of feature-level fusion of the previous works
through the entropy analysis from which we found that the randomness difference
between magnitude and phase features is large, which can interrupt the
feature-level fusion via backend neural network; thus, we propose a phase
network to reduce that difference. Our SSD system: phase network equipped
Res2Net achieved significant performance improvement, specifically in the
spoofing attack for which the phase information is considered to be important.
Also, we demonstrate our SSD system in both known- and unknown-kind SSD
scenarios for practical applications.


title:
Human-Centric Artificial Intelligence Architecture for Industry 5.0
  Applications

abstract:
  Human-centricity is the core value behind the evolution of manufacturing
towards Industry 5.0. Nevertheless, there is a lack of architecture that
considers safety, trustworthiness, and human-centricity at its core. Therefore,
we propose an architecture that integrates Artificial Intelligence (Active
Learning, Forecasting, Explainable Artificial Intelligence), simulated reality,
decision-making, and users' feedback, focusing on synergies between humans and
machines. Furthermore, we align the proposed architecture with the Big Data
Value Association Reference Architecture Model. Finally, we validate it on two
use cases from real-world case studies.


title: Unitary vertex algebras and Wightman conformal field theories

abstract:
  We prove an equivalence between the following notions: (i) unitary M\"obius
vertex algebras, and (ii) Wightman conformal field theories on the circle (with
finite-dimensional conformal weight spaces) satisfying an additional condition
that we call uniformly bounded order. Reading this equivalence in one
direction, we obtain new analytic and operator-theoretic information about
vertex operators. In the other direction we characterize OPEs of Wightman
fields and show they satisfy the axioms of a vertex algebra. As an application
we establish new results linking unitary vertex operator algebras with
conformal nets.


title:
Effective Token Graph Modeling using a Novel Labeling Strategy for
  Structured Sentiment Analysis

abstract:
  The state-of-the-art model for structured sentiment analysis casts the task
as a dependency parsing problem, which has some limitations: (1) The label
proportions for span prediction and span relation prediction are imbalanced.
(2) The span lengths of sentiment tuple components may be very large in this
task, which will further exacerbate the imbalance problem. (3) Two nodes in a
dependency graph cannot have multiple arcs, therefore some overlapped sentiment
tuples cannot be recognized. In this work, we propose nichetargeting solutions
for these issues. First, we introduce a novel labeling strategy, which contains
two sets of token pair labels, namely essential label set and whole label set.
The essential label set consists of the basic labels for this task, which are
relatively balanced and applied in the prediction layer. The whole label set
includes rich labels to help our model capture various token relations, which
are applied in the hidden layer to softly influence our model. Moreover, we
also propose an effective model to well collaborate with our labeling strategy,
which is equipped with the graph attention networks to iteratively refine token
representations, and the adaptive multi-label classifier to dynamically predict
multiple relations between token pairs. We perform extensive experiments on 5
benchmark datasets in four languages. Experimental results show that our model
outperforms previous SOTA models by a large margin.


title:
Determining the beaming of Io decametric emissions : a remote diagnostic
  to probe the Io-Jupiter interaction

abstract:
  We investigate the beaming of 11 Io-Jupiter decametric (Io-DAM) emissions
observed by Juno/Waves, the Nan\c cay Decameter Array and NenuFAR. Using an
up-to-date magnetic field model and three methods to position the active Io
Flux Tube (IFT), we accurately locate the radiosources and determine their
emission angle $\theta$ from the local magnetic field vector. These methods use
(i) updated models of the IFT equatorial lead angle, (ii) ultraviolet (UV)
images of Jupiter's aurorae and (iii) multi-point radio measurements. The
kinetic energy $E_{e-}$ of source electrons is then inferred from $\theta$ in
the framework of the Cyclotron Maser Instability. The precise position of the
active IFT achieved from methods (ii,iii) can be used to test the effective
torus plasma density. Simultaneous radio/UV observations reveal that multiple
Io-DAM arcs are associated with multiple UV spots and provide the first direct
evidence of an Io-DAM arc associated with a trans-hemispheric beam UV spot.
Multi-point radio observations probe the Io-DAM sources at various altitudes,
times and hemispheres. Overall, $\theta$ varies a function of frequency
(altitude), by decreasing from $75^\circ-80^\circ$ to $70^\circ-75^\circ$ over
$10-40$ MHz with slightly larger values in the northern hemisphere, and
independently varies as a function of time (or longitude of Io). Its
uncertainty of a few degrees is dominated by the error on the longitude of the
active IFT. The inferred values of $E_{e-}$ also vary as a function of altitude
and time. For the 11 investigated cases, they range from 3 to 16 keV, with a
$6.6\pm2.7$ keV average.


title: Generating series of classes of exotic un-ordered configuration spaces

abstract:
  A notion of exotic (ordered) configuration spaces of points on a space $X$
was suggested by Yu.~Baryshnikov. He gave equations for the (exponential)
generating series of the Euler characteristics of these spaces. Here we
consider un-ordered analogues of these spaces. For $X$ being a complex
quasiprojective variety, we give equations for the generating series of classes
of these configuration spaces in the Grothendieck ring
$K_0({\rm{Var}_{\mathbb{C}}})$ of complex quasiprojective varieties. The answer
is formulated in terms of the (natural) power structure over the ring
$K_0({\rm{Var}_{\mathbb{C}}})$. This gives equations for the generating series
of additive invariants of the configuration spaces such as the Hodge--Deligne
polynomial and the Euler characteristic.


title:
A Stochastic Planning Method for Low-carbon Building-level Integrated
  Energy System Considering Electric-Heat-V2G Coupling

abstract:
  The concept of low-carbon building is proposed to ameliorate the climate
change caused by environmental problems and realize carbon neutrality at the
building level in urban areas. In addition, renewable energy curtailment in the
power distribution system, as well as low efficiency due to independent
operation of traditional energy systems, has been addressed by the application
of integrated energy system (IES) to some extent. In this paper, we propose a
planning method for low-carbon building-level IES, in which electric vehicles
(EV) and the mode of Vehicle to Grid (V2G) are considered and further increase
the flexibility of low-carbon buildings. The proposed planning model optimize
the investment, operation costs and CO2 emission for building-level IES, so as
to achieve the maximum benefit of the construction of the low-carbon building
and help the realization of carbon neutrality. Moreover, we consider the
uncertainty of distributed renewable energy, multi-energy load fluctuation and
the random behavior of EV users, then formulating a two-stage stochastic
programming model with chance constraints, in which heuristic moment matching
scenario generation (HMMSG) and sample average approximation (SAA) method are
applied. In case study, a real IES commercial building in Shanghai, where
photovoltaic (PV), energy storage system (ESS), fuel cell (FC), EV, etc. are
included as planning options, is used as numerical example to verify the
effectiveness of the proposed planning method, with functions of ESS and EV in
IES are analyzed in detail in different operation scenarios.


title:
Graph Neural Networks for Wireless Communications: From Theory to
  Practice

abstract:
  Deep learning-based approaches have been developed to solve challenging
problems in wireless communications, leading to promising results. Early
attempts adopted neural network architectures inherited from applications such
as computer vision. They often require huge amounts of training samples (i.e.,
poor generalization), and yield poor performance in large-scale networks (i.e.,
poor scalability). To resolve these issues, graph neural networks (GNNs) have
been recently adopted, as they can effectively exploit the domain knowledge,
i.e., the graph topology in wireless communication problems. GNN-based methods
can achieve near-optimal performance in large-scale networks and generalize
well under different system settings, but the theoretical underpinnings and
design guidelines remain elusive, which may hinder their practical
implementations. This paper endeavors to fill both the theoretical and
practical gaps. For theoretical guarantees, we prove that GNNs achieve
near-optimal performance in wireless networks with much fewer training samples
than traditional neural architectures. Specifically, to solve an optimization
problem on an $n$-node graph (where the nodes may represent users, base
stations, or antennas), GNNs' generalization error and required number of
training samples are $\mathcal{O}(n)$ and $\mathcal{O}(n^2)$ times lower than
the unstructured multi-layer perceptrons. For design guidelines, we propose a
unified framework that is applicable to general design problems in wireless
networks, which includes graph modeling, neural architecture design, and
theory-guided performance enhancement. Extensive simulations, which cover a
variety of important problems and network settings, verify our theory and
effectiveness of the proposed design framework.


title: Towards the standard Majorana representations of 3-transposition groups

abstract:
  In this paper, we discuss 3-transposition groups. In particular, we find
sizes of maximal symmetric subgroups of the groups, which are in Fischer list.
In addition, we build faithful representations of symmetric groups in
orthogonal, symplectic, and unitary spaces, which fully verify the main result.
Thus, we find candidate groups, for which one may be able to build standard
Majorana representations.


title:
High-efficiency grating couplers for pixel-level flat-top beam
  generation

abstract:
  We demonstrate a kind of grating coupler that generates high quality flat-top
beam with a small beamwidth from photonic integrated circuits into free-space.
The grating coupler is designed on a silicon-on-insulator wafer with a 220
nm-thick silicon layer and consists of a dual-etch grating (DG) and a
distributed Bragg reflector (DBR). By adjusting the structural parameters of DG
and DBR, a pixel-level (6.6 um) flat-top beam with the vertical radiation of
-0.5 dB and the mode match of 97% at 1550 nm is realized. Furthermore, a series
of high-efficiency grating couplers are designed to create flat-top beam with
different scales.


title: Inductive Synthesis of Finite-State Controllers for POMDPs

abstract:
  We present a novel learning framework to obtain finite-state controllers
(FSCs) for partially observable Markov decision processes and illustrate its
applicability for indefinite-horizon specifications. Our framework builds on
oracle-guided inductive synthesis to explore a design space compactly
representing available FSCs. The inductive synthesis approach consists of two
stages: The outer stage determines the design space, i.e., the set of FSC
candidates, while the inner stage efficiently explores the design space. This
framework is easily generalisable and shows promising results when compared to
existing approaches. Experiments indicate that our technique is (i) competitive
to state-of-the-art belief-based approaches for indefinite-horizon properties,
(ii) yields smaller FSCs than existing methods for several models, and (iii)
naturally treats multi-objective specifications.


title: Longitudinal Self-Supervision for COVID-19 Pathology Quantification

abstract:
  Quantifying COVID-19 infection over time is an important task to manage the
hospitalization of patients during a global pandemic. Recently, deep
learning-based approaches have been proposed to help radiologists automatically
quantify COVID-19 pathologies on longitudinal CT scans. However, the learning
process of deep learning methods demands extensive training data to learn the
complex characteristics of infected regions over longitudinal scans. It is
challenging to collect a large-scale dataset, especially for longitudinal
training. In this study, we want to address this problem by proposing a new
self-supervised learning method to effectively train longitudinal networks for
the quantification of COVID-19 infections. For this purpose, longitudinal
self-supervision schemes are explored on clinical longitudinal COVID-19 CT
scans. Experimental results show that the proposed method is effective, helping
the model better exploit the semantics of longitudinal data and improve two
COVID-19 quantification tasks.


title:
Study of magnetoelastic interaction in MnF$_2$ by the acoustoelectric
  transformation method

abstract:
  The mechanisms of magnetoelastic interaction in MnF$_2$ are studied using the
method of acoustoelectric transformation. The temperature dependence of the
piezomagnetic coupling coefficient and its anisotropy are determined in the
antiferromagnetic phase. We observe a new effect consisting in the appearance
of a non-diagonal component of the magnetic susceptibility tensor, which is
proportional to the square of the order parameter, under the action of the
shear wave. A phenomenological interpretation of the effect, which takes into
account a small-angle rotation of the crystal lattice, is presented. In the
paramagnetic state, the effect of acoustic deformation is reduced to the
modulation of the diagonal component of the susceptibility tensor.


title: Elongated Poisson-Voronoi cells in an empty half-plane

abstract:
  The Voronoi tessellation of a homogeneous Poisson point process in the lower
half-plane gives rise to a family of vertical elongated cells in the upper
half-plane. The set of edges of these cells is ruled by a Markovian branching
mechanism which is asymptotically described by two sequences of iid variables
which are respectively Beta and exponentially distributed. This leads to a
precise description of the scaling limit of a so-called typical cell. The limit
object is a random apeirogon that we name menhir in reference to the Gallic
huge stones. We also deduce from the aforementioned branching mechanism that
the number of vertices of a cell of height $\lambda$ is asymptotically equal to
$\frac45\log\lambda$.


title: ViM: Out-Of-Distribution with Virtual-logit Matching

abstract:
  Most of the existing Out-Of-Distribution (OOD) detection algorithms depend on
single input source: the feature, the logit, or the softmax probability.
However, the immense diversity of the OOD examples makes such methods fragile.
There are OOD samples that are easy to identify in the feature space while hard
to distinguish in the logit space and vice versa. Motivated by this
observation, we propose a novel OOD scoring method named Virtual-logit Matching
(ViM), which combines the class-agnostic score from feature space and the
In-Distribution (ID) class-dependent logits. Specifically, an additional logit
representing the virtual OOD class is generated from the residual of the
feature against the principal space, and then matched with the original logits
by a constant scaling. The probability of this virtual logit after softmax is
the indicator of OOD-ness. To facilitate the evaluation of large-scale OOD
detection in academia, we create a new OOD dataset for ImageNet-1K, which is
human-annotated and is 8.8x the size of existing datasets. We conducted
extensive experiments, including CNNs and vision transformers, to demonstrate
the effectiveness of the proposed ViM score. In particular, using the BiT-S
model, our method gets an average AUROC 90.91% on four difficult OOD
benchmarks, which is 4% ahead of the best baseline. Code and dataset are
available at https://github.com/haoqiwang/vim.


title:
AnoViT: Unsupervised Anomaly Detection and Localization with Vision
  Transformer-based Encoder-Decoder

abstract:
  Image anomaly detection problems aim to determine whether an image is
abnormal, and to detect anomalous areas. These methods are actively used in
various fields such as manufacturing, medical care, and intelligent
information. Encoder-decoder structures have been widely used in the field of
anomaly detection because they can easily learn normal patterns in an
unsupervised learning environment and calculate a score to identify
abnormalities through a reconstruction error indicating the difference between
input and reconstructed images. Therefore, current image anomaly detection
methods have commonly used convolutional encoder-decoders to extract normal
information through the local features of images. However, they are limited in
that only local features of the image can be utilized when constructing a
normal representation owing to the characteristics of convolution operations
using a filter of fixed size. Therefore, we propose a vision transformer-based
encoder-decoder model, named AnoViT, designed to reflect normal information by
additionally learning the global relationship between image patches, which is
capable of both image anomaly detection and localization. The proposed approach
constructs a feature map that maintains the existing location information of
individual patches by using the embeddings of all patches passed through
multiple self-attention layers. The proposed AnoViT model performed better than
the convolution-based model on three benchmark datasets. In MVTecAD, which is a
representative benchmark dataset for anomaly localization, it showed improved
results on 10 out of 15 classes compared with the baseline. Furthermore, the
proposed method showed good performance regardless of the class and type of the
anomalous area when localization results were evaluated qualitatively.


title:
Well posedness and stochastic derivation of a
  diffusion-growth-fragmentation equation in a chemostat

abstract:
  We study the existence and uniqueness of the solution of a non-linear coupled
system constituted of a degenerate diffusion-growth-fragmentation equation and
a differential equation, resulting from the modeling of bacterial growth in a
chemostat. This system is derived, in a large population approximation, from a
stochastic individual-based model where each individual is characterized by a
non-negative real valued trait described by a diffusion. Two uniqueness results
are highlighted. They differ in their hypotheses related to the influence of
the resource on individual trait dynamics, the main difficulty being the
non-linearity due to this dependence and the degeneracy of the diffusion
coefficient. Further we show that the semi-group of the stochastic trait
dynamics admits a density by probabilistic arguments, that allows the measure
solution of the diffusiongrowth-fragmentation equation to be a function with a
certain Besov regularity.


title:
Information-theoretic analyses of neural data to minimize the effect of
  researchers' assumptions in predictive coding studies

abstract:
  Studies investigating neural information processing often implicitly ask
both, which processing strategy out of several alternatives is used and how
this strategy is implemented in neural dynamics. A prime example are studies on
predictive coding. These often ask if confirmed predictions about inputs or
predictions errors between internal predictions and inputs are passed on in a
hierarchical neural system-while at the same time looking for the neural
correlates of coding for errors and predictions. If we do not know exactly what
a neural system predicts at any given moment, this results in a circular
analysis-as has been criticized correctly. To circumvent such circular
analysis, we propose to express information processing strategies (such as
predictive coding) by local information-theoretic quantities, such that they
can be estimated directly from neural data. We demonstrate our approach by
investigating two opposing accounts of predictive coding-like processing
strategies, where we quantify the building blocks of predictive coding, namely
predictability of inputs and transfer of information, by local active
information storage and local transfer entropy. We define testable hypotheses
on the relationship of both quantities to identify which of the assumed
strategies was used. We demonstrate our approach on spiking data from the
retinogeniculate synapse of the cat. Applying our local information dynamics
framework, we are able to show that the synapse codes for predictable rather
than surprising input. To support our findings, we apply measures from partial
information decomposition, which allow to differentiate if the transferred
information is primarily bottom-up sensory input or information transferred
conditionally on the current state of the synapse. Supporting our local
information-theoretic results, we find that the synapse preferentially
transfers bottom-up information.


title:
Snowmass White Paper: Beyond the Standard Model effects on Neutrino
  Flavor

abstract:
  Neutrinos are one of the most promising messengers for signals of new physics
Beyond the Standard Model (BSM). On the theoretical side, their elusive nature,
combined with their unknown mass mechanism, seems to indicate that the neutrino
sector is indeed opening a window to new physics. On the experimental side,
several long-standing anomalies have been reported in the past decades,
providing a strong motivation to thoroughly test the standard three-neutrino
oscillation paradigm. In this Snowmass21 white paper, we explore the potential
of current and future neutrino experiments to explore BSM effects on neutrino
flavor during the next decade.


title: ARM: Any-Time Super-Resolution Method

abstract:
  This paper proposes an Any-time super-Resolution Method (ARM) to tackle the
over-parameterized single image super-resolution (SISR) models. Our ARM is
motivated by three observations: (1) The performance of different image patches
varies with SISR networks of different sizes. (2) There is a tradeoff between
computation overhead and performance of the reconstructed image. (3) Given an
input image, its edge information can be an effective option to estimate its
PSNR. Subsequently, we train an ARM supernet containing SISR subnets of
different sizes to deal with image patches of various complexity. To that
effect, we construct an Edge-to-PSNR lookup table that maps the edge score of
an image patch to the PSNR performance for each subnet, together with a set of
computation costs for the subnets. In the inference, the image patches are
individually distributed to different subnets for a better
computation-performance tradeoff. Moreover, each SISR subnet shares weights of
the ARM supernet, thus no extra parameters are introduced. The setting of
multiple subnets can well adapt the computational cost of SISR model to the
dynamically available hardware resources, allowing the SISR task to be in
service at any time. Extensive experiments on resolution datasets of different
sizes with popular SISR networks as backbones verify the effectiveness and the
versatility of our ARM. The source code is available at
\url{https://github.com/chenbong/ARM-Net}.


title: Dispersion Analysis of CIP-FEM for Helmholtz Equation

abstract:
  When solving the Helmholtz equation numerically, the accuracy of numerical
solution deteriorates as the wave number $k$ increases, known as `pollution
effect' which is directly related to the phase difference between the exact and
numerical solutions, caused by the numerical dispersion. In this paper, we
propose a dispersion analysis for the continuous interior penalty finite
element method (CIP-FEM) and derive an explicit formula of the penalty
parameter for the $p^{\rm th}$ order CIP-FEM on tensor product (Cartesian)
meshes, with which the phase difference is reduced from
$\mathcal{O}\big(k(kh)^{2p}\big)$ to $\mathcal{O}\big(k(kh)^{2p+2}\big)$.
Extensive numerical tests show that the pollution error of the CIP-FE solution
is also reduced by two orders in $kh$ with the same penalty parameter.


title:
Bracket words: a generalisation of Sturmian words arising from
  generalised polynomials

abstract:
  Generalised polynomials are maps constructed by applying the floor function,
addition, and multiplication to polynomials. Despite superficial similarity,
generalised polynomials exhibit many phenomena which are impossible for
polynomials. In particular, there exist generalised polynomial sequences which
take only finitely many values without being periodic; examples of such
sequences include the Sturmian words, as well as more complicated sequences
like $[ 2\{ \pi n^2 + \sqrt{2}n[\sqrt{3}n] \}]$. The purpose of this paper is
to investigate letter-to-letter codings of finitely-valued generalised
polynomial sequences, which we call \emph{bracket words}, from the point of
view of combinatorics on words. We survey existing results on generalised
polynomials and their corollaries in terms of bracket words, and also prove
several new results. Our main contribution is a polynomial bound on the subword
complexity of bracket words.


title:
Improving the stability of frequency dependent squeezing with
  bichromatic control of filter cavity length, alignment and incident beam
  pointing

abstract:
  Frequency dependent squeezing is the main upgrade for achieving broadband
quantum noise reduction in upcoming observation runs of gravitational wave
detectors. The proper frequency dependence of the squeezed quadrature is
obtained by reflecting squeezed vacuum from a Fabry-Perot filter cavity detuned
by half of its linewidth. However, since the squeezed vacuum contains no
classical amplitude, co-propagating auxiliary control beams are required to
achieve the filter cavity's length, alignment, and incident beam pointing
stability. In our frequency dependent squeezing experiment at the National
Astronomical Observatory of Japan, we used a control beam at a harmonic of
squeezed vacuum wavelength and found visible detuning variation related to the
suspended mirrors angular drift. These variations can degrade interferometer
quantum noise reduction. We investigated various mechanisms that can cause the
filter cavity detuning variation. The detuning drift is found to be mitigated
sufficiently by fixing the incident beam pointing and applying filter cavity
automatic alignment. It was also found that there is an optimal position of the
beam on the filter cavity mirror that helps to reduce the detuning
fluctuations. Here we report a stabilized filter cavity detuning variation of
less than 10$\,$Hz compared to the 113$\,$Hz cavity linewidth. Compared to
previously published results [Phys. Rev. Lett. 124, 171101 (2020)], such
detuning stability would be sufficient to make filter cavity detuning drift
induced gravitational wave detector detection range fluctuation reduce from
$11\%$ to within $2\%$.


title:
Moduli space of irregular rank two parabolic bundles over the Riemann
  sphere and its compactification

abstract:
  In this paper, we study rank 2 (quasi) parabolic bundles over the Riemann
sphere with an effective divisor and these moduli spaces. First we consider a
criterium when a parabolic bundle admits a unramified irregular singular
parabolic connection. Second, to give a good compactification of the moduli
space of semistable parabolic bundles, we introduce a generalization of
parabolic bundles, which is called refined parabolic bundles. Third, we discuss
a stability condition of refined parabolic bundles and define elementary
transformations of the refined parabolic bundles. Finally, we describe the
moduli spaces of refined parabolic bundles when the dimensions of the moduli
spaces are two. These are related to geometry of some weak del Pezzo surfaces.


title:
Distributed hybrid observer with prescribed convergence rate for a
  linear plant subject to disturbances using multi-hop decomposition

abstract:
  With a continuous-time formulation of the multihop decomposition, we propose
a distributed hybrid observer for a sensor network where the plant and local
observers run in continuous time and the information exchange among the sensing
nodes is sampled-data. Process disturbances, measurement noise and
communication noise are considered, and we prove that under some necessary
detectability assumptions the observer gains can be tuned to guarantee
exponential ISS with a prescribeda convergence rate. Simulations illustrate the
performance of the proposed observer.


title: Holomorphic bundles on complex manifolds with boundary

abstract:
  Let $\Omega$ be a complex manifold, and let $X\subset \Omega$ be an open
submanifold whose closure $\bar X$ is a (not necessarily compact) submanifold
with smooth boundary. Let $G$ be a complex Lie group, $\Pi$ be a differentiable
principal $G$-bundle on $\Omega$ and $J$ a formally integrable bundle almost
complex structure on the restriction $\bar P:= \Pi|_{\bar X}$. We prove that,
if the boundary of $\bar X$ is strictly pseudoconvex, $J$ extends to a
holomorphic structure on the restriction of $\Pi$ to a neighborhood of $\bar X$
in $\Omega$. This answers positively and generalizes a problem stated in the
article "Boundary value problems for Yang-Mills fields" by S. Donaldson. We
obtain a gauge theoretical interpretation of the quotient
$\mathcal{C}^\infty(\partial \bar X,G)/\mathcal{O}^\infty(\bar X,G)$ associated
with any compact Stein manifold with boundary $\bar X$ endowed with a Hermitian
metric. For a fixed differentiable $G$-bundle $\bar P$ on a complex manifold
$\bar X$ with non-pseudoconvex boundary, we study the set of formally
integrable almost complex structures on $\bar P$ which admit formally
holomorphic local trivializations at boundary points. We give an example where
a "generic" formally integrable almost complex on $\bar P$ admits formally
holomorphic local trivializations at no boundary point, whereas the set of
formally integrable almost complex structures which admit formally holomorphic
local trivializations at all boundary points is dense.


title:
Proximal Policy Optimization-based Transmit Beamforming and Phase-shift
  Design in an IRS-aided ISAC System for the THz Band

abstract:
  In this paper, an IRS-aided integrated sensing and communications (ISAC)
system operating in the terahertz (THz) band is proposed to maximize the system
capacity. Transmit beamforming and phase-shift design are transformed into a
universal optimization problem with ergodic constraints. Then the joint
optimization of transmit beamforming and phase-shift design is achieved by
gradient-based, primal-dual proximal policy optimization (PPO) in the
multi-user multiple-input single-output (MISO) scenario. Specifically, the
actor part generates continuous transmit beamforming and the critic part takes
charge of discrete phase shift design. Based on the MISO scenario, we
investigate a distributed PPO (DPPO) framework with the concept of
multi-threading learning in the multi-user multiple-input multiple-output
(MIMO) scenario. Simulation results demonstrate the effectiveness of the
primal-dual PPO algorithm and its multi-threading version in terms of transmit
beamforming and phase-shift design.


title:
Spatial Concept-based Topometric Semantic Mapping for Hierarchical
  Path-planning from Speech Instructions

abstract:
  Navigating to destinations using human speech instructions is an important
task for autonomous mobile robots that operate in the real world. Spatial
representations include a semantic level that represents an abstracted location
category, a topological level that represents their connectivity, and a metric
level that depends on the structure of the environment. The purpose of this
study is to realize a hierarchical spatial representation using a topometric
semantic map and planning efficient paths through human-robot interactions. We
propose a novel probabilistic generative model, SpCoTMHP, that forms a
topometric semantic map that adapts to the environment and leads to
hierarchical path planning. We also developed approximate inference methods for
path planning, where the levels of the hierarchy can influence each other. The
proposed path planning method is theoretically supported by deriving a
formulation based on control as probabilistic inference. The navigation
experiment using human speech instruction shows that the proposed spatial
concept-based hierarchical path planning improves the performance and reduces
the calculation cost compared with conventional methods. Hierarchical spatial
representation provides a mutually understandable form for humans and robots to
render language-based navigation tasks feasible.


title:
Sem2NeRF: Converting Single-View Semantic Masks to Neural Radiance
  Fields

abstract:
  Image translation and manipulation have gain increasing attention along with
the rapid development of deep generative models. Although existing approaches
have brought impressive results, they mainly operated in 2D space. In light of
recent advances in NeRF-based 3D-aware generative models, we introduce a new
task, Semantic-to-NeRF translation, that aims to reconstruct a 3D scene
modelled by NeRF, conditioned on one single-view semantic mask as input. To
kick-off this novel task, we propose the Sem2NeRF framework. In particular,
Sem2NeRF addresses the highly challenging task by encoding the semantic mask
into the latent code that controls the 3D scene representation of a pretrained
decoder. To further improve the accuracy of the mapping, we integrate a new
region-aware learning strategy into the design of both the encoder and the
decoder. We verify the efficacy of the proposed Sem2NeRF and demonstrate that
it outperforms several strong baselines on two benchmark datasets.


title: Two-particle superposition effects in two-slit interference

abstract:
  Entanglement can modify the interference patterns of multi-particle systems.
We analyse, using the path integral formalism, a novel example of
multi-particle interference and some unexplored aspects of this phenomenon by
considering the two-slit arrangement with two distinguishable particles in a
superposition state. The two components of entanglement, multiplicity of
multi-particle terms and their coherence, can be studied separately by
comparing the patterns of product, mixed and superposition states. This
suggests a scheme to detect multi-particle superposition in some favourable
cases. Moreover, the dependence of the effect on the entanglement degree,
measured by the Schmidt number, is not a monotone.


title: Long Short-Term Memory for Spatial Encoding in Multi-Agent Path Planning

abstract:
  Reinforcement learning-based path planning for multi-agent systems of varying
size constitutes a research topic with increasing significance as progress in
domains such as urban air mobility and autonomous aerial vehicles continues.
Reinforcement learning with continuous state and action spaces is used to train
a policy network that accommodates desirable path planning behaviors and can be
used for time-critical applications. A Long Short-Term Memory module is
proposed to encode an unspecified number of states for a varying, indefinite
number of agents. The described training strategies and policy architecture
lead to a guidance that scales to an infinite number of agents and unlimited
physical dimensions, although training takes place at a smaller scale. The
guidance is implemented on a low-cost, off-the-shelf onboard computer. The
feasibility of the proposed approach is validated by presenting flight test
results of up to four drones, autonomously navigating collision-free in a
real-world environment.


title: Spectral theory of the non-backtracking Laplacian for graphs

abstract:
  We introduce a non-backtracking Laplace operator for graphs and we
investigate its spectral properties. With the use of both theoretical and
computational techniques, we show that the spectrum of this operator captures
several structural properties of the graph in a more precise way than the
classical operators that have been studied so far in the literature, including
the non-backtracking matrix.


title:
Using lower-redshift, non-CMB, data to constrain the Hubble constant and
  other cosmological parameters

abstract:
  We use updated Hubble parameter and baryon acoustic oscillation data, as well
as other lower-redshift Type Ia supernova, Mg II reverberation-measured quasar,
quasar angular size, H II starburst galaxy, and Amati-correlated gamma-ray
burst data, to jointly constrain cosmological parameters in six cosmological
models. The joint analysis provides model-independent determinations of the
Hubble constant, $H_0=69.7\pm1.2$ $\rm{km \ s^{-1} \ Mpc^{-1}}$, and the
current non-relativistic matter density parameter, $\Omega_{m0}=0.295\pm0.017$.
These error bars are factors of 2.2 and 2.3 larger than the corresponding error
bars in the flat $\Lambda$CDM model from Planck TT,TE,EE+lowE+lensing cosmic
microwave background anisotropy data. Based on the deviance information
criterion (DIC), the flat $\Lambda$CDM model is most favored but mild dark
energy dynamics and a little spatial curvature are not ruled out.


title: Joint Probabilities within Random Permutations

abstract:
  A celebrated analogy between prime factorizations of integers and cycle
decompositions of permutations is explored here. Asymptotic formulas
characterizing semismooth numbers (possessing at most several large factors)
carry over to random permutations. We offer a survey of practical methods for
computing relevant probabilities of a bivariate or trivariate flavor.


title:
Separating Content from Speaker Identity in Speech for the Assessment of
  Cognitive Impairments

abstract:
  Deep speaker embeddings have been shown effective for assessing cognitive
impairments aside from their original purpose of speaker verification. However,
the research found that speaker embeddings encode speaker identity and an array
of information, including speaker demographics, such as sex and age, and speech
contents to an extent, which are known confounders in the assessment of
cognitive impairments. In this paper, we hypothesize that content information
separated from speaker identity using a framework for voice conversion is more
effective for assessing cognitive impairments and train simple classifiers for
the comparative analysis on the DementiaBank Pitt Corpus. Our results show that
while content embeddings have an advantage over speaker embeddings for the
defined problem, further experiments show their effectiveness depends on
information encoded in speaker embeddings due to the inherent design of the
architecture used for extracting contents.


title:
Distributed non-disclosive validation of predictive models by a modified
  ROC-GLM

abstract:
  Distributed statistical analyses provide a promising approach for privacy
protection when analysing data distributed over several databases. It brings
the analysis to the data and not the data to the analysis. The analyst receives
anonymous summary statistics which are combined to a aggregated result. We are
interested to calculate the AUC of a prediction score based on a distributed
approach without getting to know the data of involved individual subjects
distributed over different databases. We use DataSHIELD as the technology to
carry out distributed analyses and use a newly developed algorithms to perform
the validation of the prediction score. Calibration can easily be implemented
in the distributed setting. But, discrimination represented by a respective ROC
curve and its AUC is challenging. We base our approach on the ROC-GLM algorithm
as well as on ideas of differential privacy. The proposed algorithms are
evaluated in a simulation study. A real-word application is described: The
audit use case of DIFUTURE (Medical Informatics Initiative) with the goal to
validate a treatment prediction rule of patients with newly diagnosed multiple
sclerosis.


title:
Existence and Uniqueness of the Solution to the Anisotropic
  Quasi-Geostrophic Equations in the Sobolev Space

abstract:
  In this paper, we focus on the two-dimensional surface quasi-geostrophic
equation with fractional horizontal dissipation and vertical thermal diffusion
which represents a general case of the classical surface quasi-geostrophic
equation. On the one hand, we will show the local existence and uniqueness of
the solution in Sobolev space $H^{2-2\alpha}(\mathbb{R}^2)\cap
H^{2-2\beta}(\mathbb{R}^2)$, which is the critical space in the classical case.
Furthermore, we will demonstrate that the solution is global even when the
initial data is very small. Finally, we will study the asymptotic
representation of our global solution in infinity.


title:
Perceptual Features as Markers of Parkinson's Disease: The Issue of
  Clinical Interpretability

abstract:
  Up to 90% of patients with Parkinson's disease (PD) suffer from hypokinetic
dysathria (HD) which is also manifested in the field of phonation. Clinical
signs of HD like monoloudness, monopitch or hoarse voice are usually quantified
by conventional clinical interpretable features (jitter, shimmer,
harmonic-to-noise ratio, etc.). This paper provides large and robust insight
into perceptual analysis of 5 Czech vowels of 84 PD patients and proves that
despite the clinical inexplicability the perceptual features outperform the
conventional ones, especially in terms of discrimination power (classification
accuracy ACC = 92 %, sensitivity SEN = 93 %, specificity SPE = 92 %) and
partial correlation with clinical scores like UPDRS (Unified Parkinson's
disease rating scale), MMSE (Mini-mental state examination) or FOG (Freezing of
gait questionnaire), where p < 0.0001.


title: On a conjecture of spectral extremal problems

abstract:
  For a simple graph $F$, let $\mathrm{Ex}(n, F)$ and $\mathrm{Ex_{sp}}(n,F)$
denote the set of graphs with the maximum number of edges and the set of graphs
with the maximum spectral radius in an $n$-vertex graph without any copy of the
graph $F$, respectively. The Tur\'an graph $T_{n,r}$ is the complete
$r$-partite graph on $n$ vertices where its part sizes are as equal as
possible. Cioab\u{a}, Desai and Tait [The spectral radius of graphs with no odd
wheels, European J. Combin., 99 (2022) 103420] posed the following conjecture:
Let $F$ be any graph such that the graphs in $\mathrm{Ex}(n,F)$ are Tur\'{a}n
graphs plus $O(1)$ edges. Then $\mathrm{Ex_{sp}}(n,F)\subset \mathrm{Ex}(n,F)$
for sufficiently large $n$. In this paper we consider the graph $F$ such that
the graphs in $\mathrm{Ex}(n, F)$ are obtained from $T_{n,r}$ by adding $O(1)$
edges, and prove that if $G$ has the maximum spectral radius among all
$n$-vertex graphs not containing $F$, then $G$ is a member of $\mathrm{Ex}(n,
F)$ for $n$ large enough. Then Cioab\u{a}, Desai and Tait's conjecture is
completely solved.


title:
Optimal Selective Orientation of Chiral Molecules Using Femtosecond
  Laser Pulses

abstract:
  We present a comprehensive study of enantioselective orientation of chiral
molecules excited by a pair of delayed cross-polarized femtosecond laser
pulses. We show that by optimizing the pulses' parameters, a significant (~
10%) degree of enantioselective orientation can be achieved at zero and at five
kelvin rotational temperatures. This study suggests a set of reasonable
experimental conditions for inducing and measuring strong enantioselective
orientation. The strong enantioselective orientation and the wide availability
of the femtosecond laser systems required for the proposed experiments may open
new avenues for discriminating and separating molecular enantiomers.


title:
Hyperbolic Vision Transformers: Combining Improvements in Metric
  Learning

abstract:
  Metric learning aims to learn a highly discriminative model encouraging the
embeddings of similar classes to be close in the chosen metrics and pushed
apart for dissimilar ones. The common recipe is to use an encoder to extract
embeddings and a distance-based loss function to match the representations --
usually, the Euclidean distance is utilized. An emerging interest in learning
hyperbolic data embeddings suggests that hyperbolic geometry can be beneficial
for natural data. Following this line of work, we propose a new
hyperbolic-based model for metric learning. At the core of our method is a
vision transformer with output embeddings mapped to hyperbolic space. These
embeddings are directly optimized using modified pairwise cross-entropy loss.
We evaluate the proposed model with six different formulations on four datasets
achieving the new state-of-the-art performance. The source code is available at
https://github.com/htdt/hyp_metric.


title: Molecular state in a spin-orbital-angular-momentum coupled Fermi gas

abstract:
  We study the two-body bound states in a spin-orbital-angular-momentum (SOAM)
coupled quantum gas of fermions. Two different configurations are considered:
an attractive $s$-wave interaction exists between two spin species that are
SOAM coupled; and an atom with SOAM coupled internal spins interacts
state-selectively with another atom. For both cases, we identify the condition
for the emergence of molecular states with finite total angular momenta.These
molecular states with quantized total angular momenta correspond to the
SOAM-coupling-induced vortices in the corresponding Fermi superfluid. We
propose to detect the molecules through Raman spectroscopy with
Laguerre-Gaussian lasers. As the molecular states can form above the superfluid
temperature, they offer an experimentally more accessible route toward the
study of the underlying pairing mechanism under SOAM coupling.


title: Index estimates for free boundary $f$-minimal hypersurfaces

abstract:
  We prove that the index is bounded from below by a linear function of its
first Betti number for any compact free boundary $f$-minimal hypersurface in
certain positively curved weighted manifolds.


title: A Model and Survey of Distributed Data-Intensive Systems

abstract:
  Data is a precious resource in today's society, and is generated at an
unprecedented and constantly growing pace. The need to store, analyze, and make
data promptly available to a multitude of users introduces formidable
challenges in modern software platforms. These challenges radically transformed
all research fields that gravitate around data management and processing, with
the introduction of distributed data-intensive systems that offer new
programming models and implementation strategies to handle data characteristics
such as volume, velocity, heterogeneity, and distribution. Each data-intensive
system brings its specific choices in terms of data model, usage assumptions,
synchronization, processing strategy, deployment, guarantees in terms of
consistency, fault tolerance, ordering. Yet, the problems data-intensive
systems face and the solutions they propose are frequently overlapping. This
paper proposes a unifying model that dissects the core functionalities of
data-intensive systems, and precisely discusses alternative design and
implementation strategies, pointing out their assumptions and implications. The
model offers a common ground to understand and compare highly heterogeneous
solutions, with the potential of fostering cross-fertilization across research
communities and advancing the field. We apply our model by classifying tens of
systems and this exercise guides interesting observations on the current state
of things and on open research directions.


title:
Multi-class versus One-class classifier in spontaneous speech analysis
  oriented to Alzheimer Disease diagnosis

abstract:
  Most of medical developments require the ability to identify samples that are
anomalous with respect to a target group or control group, in the sense they
could belong to a new, previously unseen class or are not class data. In this
case when there are not enough data to train two-class One-class classification
appear like an available solution. On the other hand non-linear approaches
could give very useful information. The aim of our project is to contribute to
earlier diagnosis of AD and better estimates of its severity by using automatic
analysis performed through new biomarkers extracted from speech signal. The
methods selected in this case are speech biomarkers oriented to Spontaneous
Speech and Emotional Response Analysis. In this approach One-class classifiers
and two-class classifiers are analyzed. The use of information about outlier
and Fractal Dimension features improves the system performance.


title: Faster Randomized Block Sparse Kaczmarz by Averaging

abstract:
  The standard randomized sparse Kaczmarz (RSK) method is an algorithm to
compute sparse solutions of linear systems of equations and uses sequential
updates, and thus, does not take advantage of parallel computations. In this
work, we introduce a parallel (mini batch) version of RSK based on averaging
several Kaczmarz steps. Naturally, this method allows for parallelization and
we show that it can also leverage large over-relaxation. We prove linear
expected convergence and show that, given that parallel computations can be
exploited, the method provably provides faster convergence than the standard
method. This method can also be viewed as a variant of the linearized Bregman
algorithm, a randomized dual block coordinate descent update, a stochastic
mirror descent update, or a relaxed version of RSK and we recover the standard
RSK method when the batch size is equal to one. We also provide estimates for
inconsistent systems and show that the iterates convergence to an error in the
order of the noise level. Finally, numerical examples illustrate the benefits
of the new algorithm.


title:
TCM-SD: A Large Dataset for Syndrome Differentiation in Traditional
  Chinese Medicine

abstract:
  Traditional Chinese Medicine (TCM) is a natural, safe, and effective therapy
that has spread and been applied worldwide. The unique TCM diagnosis and
treatment system requires a comprehensive analysis of a patient's symptoms
hidden in the clinical record written in free text. Prior studies have shown
that this system can be informationized and intelligentized with the aid of
artificial intelligence (AI) technology, such as natural language processing
(NLP). However, existing datasets are not of sufficient quality nor quantity to
support the further development of data-driven AI technology in TCM. Therefore,
in this paper, we focus on the core task of the TCM diagnosis and treatment
system -- syndrome differentiation (SD) -- and we introduce the first public
large-scale dataset for SD, called TCM-SD. Our dataset contains 54,152
real-world clinical records covering 148 syndromes. Furthermore, we collect a
large-scale unlabelled textual corpus in the field of TCM and propose a
domain-specific pre-trained language model, called ZY-BERT. We conducted
experiments using deep neural networks to establish a strong performance
baseline, reveal various challenges in SD, and prove the potential of
domain-specific pre-trained language model. Our study and analysis reveal
opportunities for incorporating computer science and linguistics knowledge to
explore the empirical validity of TCM theories.


title: From conformal correlators to analytic S-matrices: CFT$_1$/QFT$_2$

abstract:
  We study families of one-dimensional CFTs relevant for describing gapped QFTs
in AdS$_2$. Using the Polyakov bootstrap as our main tool, we explain how
S-matrices emerge from the flat space limit of CFT correlators. In this limit
we prove that the CFT OPE density matches that of a generalized free field, and
that this implies unitarity of the S-matrix. We establish a CFT dispersion
formula for the S-matrix, proving its analyticity except for singularities on
the real axis which we characterize in terms of the CFT data. In particular
positivity of the OPE establishes that any such S-matrix must satisfy extended
unitarity conditions. We also carefully prove that for physical kinematics the
S-matrix may be more directly described by a phase shift formula. Our results
crucially depend on the assumption of a certain gap in the spectrum of
operators. We bootstrap perturbative AdS bubble, triangle and box diagrams and
find that the presence of anomalous thresholds in S-matrices are precisely
signaled by an unbounded OPE arising from violating this assumption. Finally we
clarify the relation between unitarity saturating S-matrices and extremal CFTs,
establish a mapping between the dual S-matrix and CFT bootstraps, and discuss
how our results help understand UV completeness or lack thereof for specific
S-matrices.


title:
Relating moments of self-adjoint polynomials in two orthogonal
  projections

abstract:
  Given two orthogonal projections $\{P,Q\}$ in a non commutative tracial
probability space, we prove relations between the moments of $P+Q$, of
$\sqrt{-1}(PQ-QP)$ and of $P+QPQ$ and those of the angle operator $PQP$. Our
proofs are purely algebraic and enumerative and does not assume $P,Q$
satisfying Voiculescu's freeness property or being in general position. As far
as the sum and the commutator are concerned, the obtained relations follow from
binomial-type formulas satisfied by the orthogonal symmetries associated to $P$
and $Q$ together with the trace property. In this respect, they extend those
corresponding to the cases where one of the two projections is rotated by a
free Haar unitary operator or more generally by a free unitary Brownian motion.
As to the operator $P+QPQ$, we derive autonomous recurrence relations for the
coefficients (double sequence) of the expansion of its moments as linear
combinations of those of $PQP$ and determine explicitly few of them. These
relations are obtained after a careful analysis of the structure of words in
the alphabet $\{P, QPQ\}$.


title: Radio detections of IR-selected runaway stellar bow shocks

abstract:
  Massive stars moving at supersonic peculiar velocities through the
interstellar medium (ISM) can create bow shocks, arc-like structures at the
interface between the stellar wind and the ISM. Many such bow shocks have been
detected and catalogued at IR wavelengths, but detections in other wavebands
remain rare. Strikingly, while electrons are expected to be accelerated in the
bow shock and their non-thermal emission may include synchrotron emission at
low frequencies, only two massive runaway stellar bow shocks have to date been
detected in the radio band. Here, we examine a sample of fifty IR-detected bow
shocks from the E-BOSS catalogues in recently released radio images from the
Rapid ASKAP Continuum Survey (RACS). We identify three confident and three
likely counterparts, as well as three inconclusive candidates requiring
confirmation via follow-up observations. These detections significantly
increase the number of known radio massive stellar bow shocks and highlight the
advantage of dedicated searches with current and next-generation radio
telescopes. We investigate the underlying radio emission mechanism for these
radio sources, finding a mix of free-free-dominated and synchrotron-dominated
systems. We also discuss the non-detected targets by putting constraints on
their emission properties and investigating their detectability with future
observations. Finally, we propose several future avenues of research to advance
the study and understanding of bow shocks at radio frequencies.


title:
The GALAH Survey: A New Sample of Extremely Metal-Poor Stars Using A
  Machine Learning Classification Algorithm

abstract:
  Extremely Metal-Poor (EMP) stars provide a valuable probe of early chemical
enrichment in the Milky Way. Here we leverage a large sample of $\sim600,000$
high-resolution stellar spectra from the GALAH survey plus a machine learning
algorithm to find 54 candidates with estimated [Fe/H]~$\leq$~-3.0, 6 of which
have [Fe/H]~$\leq$~-3.5. Our sample includes $\sim 20 \%$ main sequence EMP
candidates, unusually high for \emp surveys. We find the magnitude-limited
metallicity distribution function of our sample is consistent with previous
work that used more complex selection criteria. The method we present has
significant potential for application to the next generation of massive stellar
spectroscopic surveys, which will expand the available spectroscopic data well
into the millions of stars.


title:
Lean Evolutionary Reinforcement Learning by Multitasking with Importance
  Sampling

abstract:
  Studies have shown evolution strategies (ES) to be a promising approach for
reinforcement learning (RL) with deep neural networks. However, the issue of
high sample complexity persists in applications of ES to deep RL. In this
paper, we address the shortcoming of today's methods via a novel
neuroevolutionary multitasking (NuEMT) algorithm, designed to transfer
information from a set of auxiliary tasks (of short episode length) to the
target (full length) RL task at hand. The artificially generated auxiliary
tasks allow an agent to update and quickly evaluate policies on shorter time
horizons. The evolved skills are then transferred to guide the longer and
harder task towards an optimal policy. We demonstrate that the NuEMT algorithm
achieves data-lean evolutionary RL, reducing expensive agent-environment
interaction data requirements. Our key algorithmic contribution in this setting
is to introduce, for the first time, a multitask information transfer mechanism
based on the statistical importance sampling technique. In addition, an
adaptive resource allocation strategy is utilized to assign computational
resources to auxiliary tasks based on their gleaned usefulness. Experiments on
a range of continuous control tasks from the OpenAI Gym confirm that our
proposed algorithm is efficient compared to recent ES baselines.


title: Neural Token Segmentation for High Token-Internal Complexity

abstract:
  Tokenizing raw texts into word units is an essential pre-processing step for
critical tasks in the NLP pipeline such as tagging, parsing, named entity
recognition, and more. For most languages, this tokenization step
straightforward. However, for languages with high token-internal complexity,
further token-to-word segmentation is required. Previous canonical segmentation
studies were based on character-level frameworks, with no contextualised
representation involved. Contextualized vectors a la BERT show remarkable
results in many applications, but were not shown to improve performance on
linguistic segmentation per se. Here we propose a novel neural segmentation
model which combines the best of both worlds, contextualised token
representation and char-level decoding, which is particularly effective for
languages with high token-internal complexity and extreme morphological
ambiguity. Our model shows substantial improvements in segmentation accuracy on
Hebrew and Arabic compared to the state-of-the-art, and leads to further
improvements on downstream tasks such as Part-of-Speech Tagging, Dependency
Parsing and Named-Entity Recognition, over existing pipelines. When comparing
our segmentation-first pipeline with joint segmentation and labeling in the
same settings, we show that, contrary to pre-neural studies, the pipeline
performance is superior.


title: The role of regularization in data-driven predictive control

abstract:
  Data-driven predictive control (DDPC) has been recently proposed as an
effective alternative to traditional model-predictive control (MPC) for its
unique features of being time-efficient and unbiased with respect to the oracle
solution. Nonetheless, it has also been observed in many examples that the
noise on the output data may strongly jeopardize the final closed-loop
performance, since it affects both the data-based system representation and the
control update computed from the online measurements. Recent studies have
empirically shown that regularization is potentially a successful tool to
counteract the effect of noise. In this paper, the link between the stochastic
data matrices and the predictive control problem is studied in detail. Then,
leveraging on these results, we delve deep into the role of regularization for
data-driven control, discussing when and how it should be applied. A benchmark
numerical case study illustrates how control design is simplified when a method
inspired to the proposed perspective, called $\gamma$-DDPC, is adopted.
Specifically, when regularization can be avoided, no closed-loop experiments
are required to tune the regularization weights via cross-validation.


title: Counterfactual quantum indistinguishability

abstract:
  Quantum indistinguishability and counterfactuality are two counterintuitive
features of quantum mechanics. The latter refers to the possibility of
detecting a particle without an interaction, whilst the former to the
indistinguishability of identical particles. In this work, we describe a
phenomenon, that we call "counterfactual indistinguishability", which combines
these two features, and whereby the detection of a particle at a given station
indicates a blockade on an indistinguishable particle elsewhere. From a
foundational perspective, it is of interest to note that despite being a
multi-particle phenomenon, this is ideally realized using single-photon (rather
than multi-photon) interference.


title: Narain CFTs and error-correcting codes on finite fields

abstract:
  We construct Narain CFTs from self-dual codes on the finite field $F_p$
through even self-dual lattices for any prime $p>2$. Using this correspondence,
we can relate the spectral gap and the partition function of the CFT to the
error correction capability and the extended enumerator polynomial of the code.
In particular, we calculate specific spectral gaps of CFTs constructed from
codes and compare them with the largest spectral gap among all Narain CFTs.


title:
Pathways of organic micropollutants degradation in atmospheric pressure
  plasma processing

abstract:
  Concern of toxic compounds and their, potentially more harmful degradation
products, present in aquatic environment alarmed scientific community and
research on the development of novel technologies for wastewater treatment had
become of great interest. Up to this date, many papers pointed out the
challenges and limitations of conventional wastewater treatment and of some
advanced oxidation processes. Advanced technologies based on the use of
non-equilibrium or non-thermal plasma had been recognized as a possible
solution for, not only degradation, but for complete removal of recalcitrant
organic micropollutants. While previous review papers have been focused on
plasma physics and chemistry of different types of discharges for few organic
micropollutants, this paper brings comprehensive review of current knowledge on
the chemistry and degradation pathways by using different non-thermal plasma
types for several micropollutants classes, such as pharmaceuticals,
perfluorinated compounds, pesticides, phenols and dyes and points out some
major research gaps.