title:
Nanoptera In Higher-Order Nonlinear Schr\"odinger Equations: Effects Of
  Discretization

abstract:
  We consider generalizations of nonlinear Schr\"odinger equations, which we
call Karpman equations, that include additional linear higher-order
derivatives. Singularly-perturbed Karpman equations produce generalized
solitary waves (GSWs) in the form of solitary waves with exponentially small
oscillatory tails. Nanoptera are a special case of GSWs in which these
oscillatory tails do not decay. Previous work on third-order and fourth-order
Karpman equations has shown that nanoptera occur in specific continuous
settings. We use exponential asymptotic techniques to identify traveling
nanoptera in singularly-perturbed Karpman equations. We then study the effect
of discretization on nanoptera by applying a finite-difference discretization
to Karpman equations and using exponential asymptotic analysis to study
traveling-wave solutions. By comparing nanoptera in lattice equations with
nanoptera in their continuous counterparts, we show that the discretization
process changes the amplitude and periodicity of the oscillations in nanoptera
tails. We also show that discretization changes the parameter values at which
there is a bifurcation between nanopteron and decaying oscillatory solutions.
Finally, by comparing different higher-order discretizations of the
fourth-order Karpman equation, we show that the bifurcation value tends to a
nonzero constant as the order increases, rather than to $0$ as in the
associated continuous Karpman equation.


title:
Multi-Scale Context-Guided Lumbar Spine Disease Identification with
  Coarse-to-fine Localization and Classification

abstract:
  Accurate and efficient lumbar spine disease identification is crucial for
clinical diagnosis. However, existing deep learning models with millions of
parameters often fail to learn with only hundreds or dozens of medical images.
These models also ignore the contextual relationship between adjacent objects,
such as between vertebras and intervertebral discs. This work introduces a
multi-scale context-guided network with coarse-to-fine localization and
classification, named CCF-Net, for lumbar spine disease identification.
Specifically, in learning, we divide the localization objective into two
parallel tasks, coarse and fine, which are more straightforward and effectively
reduce the number of parameters and computational cost. The experimental
results show that the coarse-to-fine design presents the potential to achieve
high performance with fewer parameters and data requirements. Moreover, the
multi-scale context-guided module can significantly improve the performance by
6.45% and 5.51% with ResNet18 and ResNet50, respectively. Our code is available
at https://github.com/czifan/CCFNet.pytorch.


title:
How to Learn from Risk: Explicit Risk-Utility Reinforcement Learning for
  Efficient and Safe Driving Strategies

abstract:
  Autonomous driving has the potential to revolutionize mobility and is hence
an active area of research. In practice, the behavior of autonomous vehicles
must be acceptable, i.e., efficient, safe, and interpretable. While vanilla
reinforcement learning (RL) finds performant behavioral strategies, they are
often unsafe and uninterpretable. Safety is introduced through Safe RL
approaches, but they still mostly remain uninterpretable as the learned
behaviour is jointly optimized for safety and performance without modeling them
separately. Interpretable machine learning is rarely applied to RL. This paper
proposes SafeDQN, which allows to make the behavior of autonomous vehicles safe
and interpretable while still being efficient. SafeDQN offers an
understandable, semantic trade-off between the expected risk and the utility of
actions while being algorithmically transparent. We show that SafeDQN finds
interpretable and safe driving policies for a variety of scenarios and
demonstrate how state-of-the-art saliency techniques can help to assess both
risk and utility.


title: Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again

abstract:
  The strong few-shot in-context learning capability of large pre-trained
language models (PLMs) such as GPT-3 is highly appealing for biomedical
applications where data annotation is particularly costly. In this paper, we
present the first systematic and comprehensive study to compare the few-shot
performance of GPT-3 in-context learning with fine-tuning smaller (i.e.,
BERT-sized) PLMs on two highly representative biomedical information extraction
tasks, named entity recognition and relation extraction. We follow the true
few-shot setting to avoid overestimating models' few-shot performance by model
selection over a large validation set. We also optimize GPT-3's performance
with known techniques such as contextual calibration and dynamic in-context
example retrieval. However, our results show that GPT-3 still significantly
underperforms compared with simply fine-tuning a smaller PLM using the same
small training set. Moreover, what is equally important for practical
applications is that adding more labeled data would reliably yield an
improvement in model performance. While that is the case when fine-tuning small
PLMs, GPT-3's performance barely improves when adding more data. In-depth
analyses further reveal issues of the in-context learning setting that may be
detrimental to information extraction tasks in general. Given the high cost of
experimenting with GPT-3, we hope our study provides guidance for biomedical
researchers and practitioners towards more promising directions such as
fine-tuning GPT-3 or small PLMs.


title:
FormNet: Structural Encoding beyond Sequential Modeling in Form Document
  Information Extraction

abstract:
  Sequence modeling has demonstrated state-of-the-art performance on natural
language and document understanding tasks. However, it is challenging to
correctly serialize tokens in form-like documents in practice due to their
variety of layout patterns. We propose FormNet, a structure-aware sequence
model to mitigate the suboptimal serialization of forms. First, we design Rich
Attention that leverages the spatial relationship between tokens in a form for
more precise attention score calculation. Second, we construct Super-Tokens for
each word by embedding representations from their neighboring tokens through
graph convolutions. FormNet therefore explicitly recovers local syntactic
information that may have been lost during serialization. In experiments,
FormNet outperforms existing methods with a more compact model size and less
pre-training data, establishing new state-of-the-art performance on CORD, FUNSD
and Payment benchmarks.


title:
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent
  Reinforcement Learning

abstract:
  Due to the partial observability and communication constraints in many
multi-agent reinforcement learning (MARL) tasks, centralized training with
decentralized execution (CTDE) has become one of the most widely used MARL
paradigms. In CTDE, centralized information is dedicated to learning the
allocation of the team reward with a mixing network, while the learning of
individual Q-values is usually based on local observations. The insufficient
utility of global observation will degrade performance in challenging
environments. To this end, this work proposes a novel Centralized Teacher with
Decentralized Student (CTDS) framework, which consists of a teacher model and a
student model. Specifically, the teacher model allocates the team reward by
learning individual Q-values conditioned on global observation, while the
student model utilizes the partial observations to approximate the Q-values
estimated by the teacher model. In this way, CTDS balances the full utilization
of global observation during training and the feasibility of decentralized
execution for online inference. Our CTDS framework is generic which is ready to
be applied upon existing CTDE methods to boost their performance. We conduct
experiments on a challenging set of StarCraft II micromanagement tasks to test
the effectiveness of our method and the results show that CTDS outperforms the
existing value-based MARL methods.


title:
Conductance Spectroscopy of Majorana Zero Modes in
  Superconductor-Magnetic Insulating Nanowire Hybrid Systems

abstract:
  There has been a recent interest in superconductor-magnetic insulator hybrid
Rashba nanowire setups for potentially hosting Majorana zero modes at smaller
external Zeeman fields. Using the Keldysh non-equilibrium Green's function
technique, we develop a detailed quantum transport approach that accounts for
the hybrid structure comprising the Rashba nanowire coupled to the bilayer
structure which includes the proximity inducing superconductor and the magnetic
insulator. We provide a detailed analysis of three terminal setups to probe the
local and non-local conductance spectra in both the pristine as well as the
disordered nanowire setups. We uncover the conductance quantization scaling
with the bilayer coupling and the signatures of the gap closing followed by the
emergence of near-zero energy states, which can be attributed to topological
zero modes in the clean limit. However, in the presence of a smoothly varying
disorder potential, trivial Andreev bound states may form with signatures
reminiscent of topological zero modes in the form of a premature gap closure in
the non-local conductance spectra. Our results therefore provide
transport-based analysis of the operating regimes that support the formation of
Majorana modes in these hybrid systems of current interest, while investigating
the effect of disorder on experimentally relevant device structures.


title: Unsupervised Semantic Segmentation by Distilling Feature Correspondences

abstract:
  Unsupervised semantic segmentation aims to discover and localize semantically
meaningful categories within image corpora without any form of annotation. To
solve this task, algorithms must produce features for every pixel that are both
semantically meaningful and compact enough to form distinct clusters. Unlike
previous works which achieve this with a single end-to-end framework, we
propose to separate feature learning from cluster compactification.
Empirically, we show that current unsupervised feature learning frameworks
already generate dense features whose correlations are semantically consistent.
This observation motivates us to design STEGO ($\textbf{S}$elf-supervised
$\textbf{T}$ransformer with $\textbf{E}$nergy-based $\textbf{G}$raph
$\textbf{O}$ptimization), a novel framework that distills unsupervised features
into high-quality discrete semantic labels. At the core of STEGO is a novel
contrastive loss function that encourages features to form compact clusters
while preserving their relationships across the corpora. STEGO yields a
significant improvement over the prior state of the art, on both the CocoStuff
($\textbf{+14 mIoU}$) and Cityscapes ($\textbf{+9 mIoU}$) semantic segmentation
challenges.


title: Sinkhorn MPC: Model predictive optimal transport over dynamical systems

abstract:
  We consider the optimal control problem of steering an agent population to a
desired distribution over an infinite horizon. This is an optimal transport
problem over a dynamical system, which is challenging due to its high
computational cost. In this paper, we propose Sinkhorn MPC, which is a
dynamical transport algorithm combining model predictive control and the
so-called Sinkhorn algorithm. The notable feature of the proposed method is
that it achieves cost-effective transport in real time by performing control
and transport planning simultaneously. In particular, for linear systems with
an energy cost, we reveal the fundamental properties of Sinkhorn MPC such as
ultimate boundedness and asymptotic stability.


title: On Higher-Order Reachability Games vs May Reachability

abstract:
  We consider the reachability problem for higher-order functional programs and
study the relationship between reachability games (i.e., the reachability
problem for programs with angelic and demonic nondeterminism) and
may-reachability (i.e., the reachability problem for programs with only angelic
nondeterminism). We show that reachability games for order-n programs can be
reduced to may-reachability for order-(n+1) programs, and vice versa. We
formalize the reductions by using higher-order fixpoint logic and prove their
correctness. We also discuss applications of the reductions to higher-order
program verification.


title:
On the Use of Fine-grained Vulnerable Code Statements for Software
  Vulnerability Assessment Models

abstract:
  Many studies have developed Machine Learning (ML) approaches to detect
Software Vulnerabilities (SVs) in functions and fine-grained code statements
that cause such SVs. However, there is little work on leveraging such detection
outputs for data-driven SV assessment to give information about exploitability,
impact, and severity of SVs. The information is important to understand SVs and
prioritize their fixing. Using large-scale data from 1,782 functions of 429 SVs
in 200 real-world projects, we investigate ML models for automating
function-level SV assessment tasks, i.e., predicting seven Common Vulnerability
Scoring System (CVSS) metrics. We particularly study the value and use of
vulnerable statements as inputs for developing the assessment models because
SVs in functions are originated in these statements. We show that vulnerable
statements are 5.8 times smaller in size, yet exhibit 7.5-114.5% stronger
assessment performance (Matthews Correlation Coefficient (MCC)) than
non-vulnerable statements. Incorporating context of vulnerable statements
further increases the performance by up to 8.9% (0.64 MCC and 0.75 F1-Score).
Overall, we provide the initial yet promising ML-based baselines for
function-level SV assessment, paving the way for further research in this
direction.


title:
Singularity formation for the general Poiseuille flow of nematic liquid
  crystals

abstract:
  We consider the Poiseuille flow of nematic liquid crystals via the full
Ericksen-Leslie model. The model is described by a coupled system consisting of
a heat equation and a quasilinear wave equation. In this paper, we will
construct an example with a finite time cusp singularity due to the
quasilinearity of the wave equation, extended from an earlier result on a
special case.


title:
Adverse effects of vaccinations against the Corona-virus SARS-CoV-2:
  insights and hindsights from a statistical perspective

abstract:
  Vaccinations against the virus SARS-CoV-2 have proven to be most effective
against a severe corona disease. However, a significant minority of people is
still critical of such a vaccination or even strictly reject it. One but surely
not the only reason for this is the fear of undesired adverse effects. During
the year 2021 in Germany alone approximately 150 million vaccination doses have
been applied. This should be a solid basis to assess the risk of adverse
effects of corona vaccinations. Based on publicly available data, especially
from the German Federal Institute of Vaccines and Biomedicines
(Paul-Ehrlich-Institut) and further scientific publications from Europe, Israel
and the United States, this paper tries to give sound quantitative statements
on the risk of (severe) adverse effects (e.g. myocarditis, thrombosis and
thrombocytopenia, venous thrombosis, including cerebral venous sinus thrombosis
and fatalities based thereon) of the various vaccination types, i.e. Comirnaty,
Vaxzevria, Janssen and the Spikevax Covid-19 vaccine. The paper also describes
some quite serious concerns about the lack of details in the publicly available
data and the variations in the structure of reports from the primary source
that prohibit even simple time series comparisons. However, from an overarching
view all provided vaccines are pretty safe and fall mostly into the same
frequency categories with respect to various clusters of undesired
complications. Nevertheless there are statistically significant differences
amongst the four vaccines which indicate that Vaxzevria is according to some
important aspects only the second best preferable choice.
  The paper aims to support a rational approach regarding the confidence in the
widely available SARS-CoV-2 vaccines through a purely statistical investigation
of the number of adverse effects in an unbiased manner.


title:
How do you Converse with an Analytical Chatbot? Revisiting Gricean
  Maxims for Designing Analytical Conversational Behavior

abstract:
  Chatbots have garnered interest as conversational interfaces for a variety of
tasks. While general design guidelines exist for chatbot interfaces, little
work explores analytical chatbots that support conversing with data. We explore
Gricean Maxims to help inform the basic design of effective conversational
interaction. We also draw inspiration from natural language interfaces for data
exploration to support ambiguity and intent handling. We ran Wizard of Oz
studies with 30 participants to evaluate user expectations for text and voice
chatbot design variants. Results identified preferences for intent
interpretation and revealed variations in user expectations based on the
interface affordances. We subsequently conducted an exploratory analysis of
three analytical chatbot systems (text + chart, voice + chart, voice-only) that
implement these preferred design variants. Empirical evidence from a second
30-participant study informs implications specific to data-driven conversation
such as interpreting intent, data orientation, and establishing trust through
appropriate system responses.


title: WegFormer: Transformers for Weakly Supervised Semantic Segmentation

abstract:
  Although convolutional neural networks (CNNs) have achieved remarkable
progress in weakly supervised semantic segmentation (WSSS), the effective
receptive field of CNN is insufficient to capture global context information,
leading to sub-optimal results. Inspired by the great success of Transformers
in fundamental vision areas, this work for the first time introduces
Transformer to build a simple and effective WSSS framework, termed WegFormer.
Unlike existing CNN-based methods, WegFormer uses Vision Transformer (ViT) as a
classifier to produce high-quality pseudo segmentation masks. To this end, we
introduce three tailored components in our Transformer-based framework, which
are (1) a Deep Taylor Decomposition (DTD) to generate attention maps, (2) a
soft erasing module to smooth the attention maps, and (3) an efficient
potential object mining (EPOM) to filter noisy activation in the background.
Without any bells and whistles, WegFormer achieves state-of-the-art 70.5% mIoU
on the PASCAL VOC dataset, significantly outperforming the previous best
method. We hope WegFormer provides a new perspective to tap the potential of
Transformer in weakly supervised semantic segmentation. Code will be released.


title: Attribute Group Editing for Reliable Few-shot Image Generation

abstract:
  Few-shot image generation is a challenging task even using the
state-of-the-art Generative Adversarial Networks (GANs). Due to the unstable
GAN training process and the limited training data, the generated images are
often of low quality and low diversity. In this work, we propose a new
editing-based method, i.e., Attribute Group Editing (AGE), for few-shot image
generation. The basic assumption is that any image is a collection of
attributes and the editing direction for a specific attribute is shared across
all categories. AGE examines the internal representation learned in GANs and
identifies semantically meaningful directions. Specifically, the class
embedding, i.e., the mean vector of the latent codes from a specific category,
is used to represent the category-relevant attributes, and the
category-irrelevant attributes are learned globally by Sparse Dictionary
Learning on the difference between the sample embedding and the class
embedding. Given a GAN well trained on seen categories, diverse images of
unseen categories can be synthesized through editing category-irrelevant
attributes while keeping category-relevant attributes unchanged. Without
re-training the GAN, AGE is capable of not only producing more realistic and
diverse images for downstream visual applications with limited data but
achieving controllable image editing with interpretable category-irrelevant
directions.


title: On-The-Go Robot-to-Human Handovers with a Mobile Manipulator

abstract:
  Existing approaches to direct robot-to-human handovers are typically
implemented on fixed-base robot arms, or on mobile manipulators that come to a
full stop before performing the handover. We propose "on-the-go" handovers
which permit a moving mobile manipulator to hand over an object to a human
without stopping. The on-the-go handover motion is generated with a reactive
controller that allows simultaneous control of the base and the arm. In a user
study, human receivers subjectively assessed on-the-go handovers to be more
efficient, predictable, natural, better timed and safer than handovers that
implemented a "stop-and-deliver" behavior.


title: A Language-Independent Analysis Platform for Source Code

abstract:
  In this paper, we present the CPG analysis platform, which enables the
translation of source code into a programming language-independent
representation, based on a code property graph. This allows security experts
and developers to capture language level semantics for security analyses or
identify patterns with respect to code compliance. Through the use of fuzzy
parsing, also incomplete or non-compilable code, written in different
programming languages, can be analyzed. The platform comprises an analysis
library and interfaces to query, interact with or visualize source code graphs.
This set of CPG tools allows finding common weaknesses in heterogeneous
software environments, independently of the underlying programming language.


title:
Whitepaper submitted to Snowmass21: Advanced accelerator linear collider
  demonstration facility at intermediate energy

abstract:
  It is widely accepted that the next lepton collider beyond a Higgs factory
would require center-of-mass energy of the order of up to 15 TeV. Since, given
reasonable space and cost restrictions, conventional accelerator technology
reaches its limits near this energy, high-gradient advanced acceleration
concepts are attractive. Advanced and novel accelerators (ANAs) are leading
candidates due to their ability to produce acceleration gradients on the order
of 1--100~GV/m, leading to compact acceleration structures. Over the last 10-15
years significant progress has been achieved in accelerating electron beams by
ANAs. For example, the demonstration of several-GeV electron beams from
laser-powered capillary discharge waveguides, as well as the proof-of-principle
coupling of two accelerating structures powered by different laser pulses, has
increased interest in ANAs as a viable technology to be considered for a
compact, TeV-class, lepton linear collider.
  However, intermediate facilities are required to test the technology and
demonstrate key subsystems. A 20-100 GeV center-of-mass energy ANA-based lepton
collider can be a possible candidate for an intermediate facility. Apart from
being a test beam facility for accelerator and detector studies, this collider
will provide opportunities to study muon and proton beam acceleration,
investigate charged particle interactions with extreme electromagnetic fields
(relevant for beam delivery system designs and to study the physics at the
interaction point), as well as precision Quantum Chromodynamics and Beyond the
Standard Model physics measurements. Possible applications of this collider
include the studies of $\gamma\gamma$ and $e$-ion collider designs.


title: Survey on Internet of Things enabled by 6G Wireless Networks

abstract:
  The 6G wireless technology is visualized to revolutionize multiple customer
services with the Internet of Things (IoT), thereby contributing to a
ubiquitous intelligent society comprising autonomous systems. In this chapter,
we conduct a detailed survey on the IoT networks with 6G wireless networks and
investigate the trending possibilities provided by the 6G technology within the
IoT networks and the related utilization; Firstly, we detail the breakthrough
IoT technologies and the technological drivers which are anticipated to
strengthen IoT networks in future. Next, we present the relevant use cases
detailing the discussion on the role of the 6G technology within a broad
spectrum of IoT potential applications. Lastly, we highlight the several
research scope and challenges and list the potential research needs and
encourage further research within the thrust area of IoT enabled by 6G
networks.


title: Laser Manipulation of H- Beams: a Snowmass 2022 White Paper

abstract:
  Future high-power (10 - 20 MW) hadron accelerators offer many unique
opportunities for fundamental science and nuclear applications. With the
completion of Proton Power Upgrade (PPU) at 2.8 MW [1] and European Spallation
Source (ESS) at 5 MW [2], the attention will be focused on the next high-power
machine in hadron accelerator community. Charge exchange injection into a
synchrotron using diamond stripper foils offers great advantage over direct
proton acceleration. However, there are clear limitations associated with foil
sublimation above critical beam power density and beam loss due to scattering
(~10 rem/hour at 10 MW for SNS).


title: Local time penalizations with various clocks for L\'{e}vy processes

abstract:
  Several long-time limit theorems of one-dimensional L\'{e}vy processes
weighted and normalized by functions of the local time are studied. The
long-time limits are taken via certain families of random times, called clocks.
The limit processes may differ according to the choice of the clocks when the
original L\'{e}vy process is recurrent and of finite variance.


title: A Survey of Machine Learning Algorithms for 6G Wireless Networks

abstract:
  The primary focus of Artificial Intelligence/Machine Learning (AI/ML)
integration within the wireless technology is to reduce capital expenditures,
optimize network performance, and build new revenue streams. Replacing
traditional algorithms with deep learning AI techniques have dramatically
reduced the power consumption and improved the system performance. Further,
implementation of ML algorithms also enables the wireless network service
providers to (i) offer high automation levels from distributed AI/ML
architectures applicable at the network edge, (ii) implement application-based
traffic steering across the access networks, (iii) enable dynamic network
slicing for addressing different scenarios with varying quality of service
requirements, and (iv) enable ubiquitous connectivity across the various 6G
communication platforms.
  In this chapter, we review/survey the ML techniques which are applicable to
the 6G wireless networks. and also list the open problems of research which
require timely solutions.


title:
Cross-Lingual Ability of Multilingual Masked Language Models: A Study of
  Language Structure

abstract:
  Multilingual pre-trained language models, such as mBERT and XLM-R, have shown
impressive cross-lingual ability. Surprisingly, both of them use multilingual
masked language model (MLM) without any cross-lingual supervision or aligned
data. Despite the encouraging results, we still lack a clear understanding of
why cross-lingual ability could emerge from multilingual MLM. In our work, we
argue that cross-language ability comes from the commonality between languages.
Specifically, we study three language properties: constituent order,
composition and word co-occurrence. First, we create an artificial language by
modifying property in source language. Then we study the contribution of
modified property through the change of cross-language transfer results on
target language. We conduct experiments on six languages and two cross-lingual
NLP tasks (textual entailment, sentence retrieval). Our main conclusion is that
the contribution of constituent order and word co-occurrence is limited, while
the composition is more crucial to the success of cross-linguistic transfer.


title:
Twist-3 Gluon Fragmentation Contribution to Hyperon Polarization in
  Semi-Inclusive Deep Inelastic Scattering

abstract:
  We derive the twist-3 gluon fragmentation function (FF) contribution to the
transversely polarized hyperon production in semi-inclusive deep inelastic
scattering, $ep\to e\Lambda^\uparrow X$, in the leading order (LO) with respect
to the QCD coupling in the framework of the collinear twist-3 factorization.
Together with the known result for the contribution from the twist-3
distribution in the proton and the twist-3 quark FFs for the hyperon, this
completes the LO cross section for this process. The constraint relations among
the twist-3 FFs are taken into account. The formula is relevant to large-$P_T$
hyperon production in the future Electron-Ion-Collider experiment.


title:
Elastodynamical resonances and cloaking of negative material structures
  beyond quasistatic approximation

abstract:
  Given the flexibility of choosing negative elastic parameters, we construct
material structures that can induce two resonance phenomena, referred to as the
elastodynamical resonances. They mimic the emerging plasmon/polariton resonance
and anomalous localized resonance in optics for subwavelength particles.
However, we study the peculiar resonance phenomena for linear elasticity beyond
the subwavelength regime. It is shown that the resonance behaviours possess
distinct characters, with some similar to the subwavelength resonances, but
some sharply different due to the frequency effect. It is particularly noted
that we construct a core-shell material structure that can induce anomalous
localized resonance as well as cloaking phenomena beyond the quasi-static
limit. The study is boiled down to analyzing the so-called elastic
Neumann-Poinc\'are (N-P) operator in the frequency regime. We provide an
in-depth analysis of the spectral properties of the N-P operator beyond the
quasi-static approximation,and these results are of independent interest to the
spectral theory of layer potential operators.


title: Partitions of cyclic words and Goldman-Turaev Lie bialgebra

abstract:
  The free $\mathbb{Z}$-module generated from the set of non-trivial homotopy
classes of closed curves on an oriented surface has a structure of Lie
bialgebra by two operations, the Goldman bracket and the Turaev cobracket. M.
Chas gave a combinatorial redefinition of these two operations through a
natural identification of the homotopy classes of closed curves on the surface
with the cyclic words in the generators and their inverses of the fundamental
group of the surface. In this paper, we give new definitions of the Turaev
cobracket and the Goldman bracket, which consist of information obtained from
the partitions of cyclic words.


title:
Deep Residual Error and Bag-of-Tricks Learning for Gravitational Wave
  Surrogate Modeling

abstract:
  Deep learning methods have been employed in gravitational-wave astronomy to
accelerate the construction of surrogate waveforms for the inspiral of
spin-aligned black hole binaries, among other applications. We demonstrate,
that the residual error of an artificial neural network that models the
coefficients of the surrogate waveform expansion (especially those of the phase
of the waveform) has sufficient structure to be learnable by a second network.
Adding this second network, we were able to reduce the maximum mismatch for
waveforms in a validation set by more than an order of magnitude. We also
explored several other ideas for improving the accuracy of the surrogate model,
such as the exploitation of similarities between waveforms, the augmentation of
the training set, the dissection of the input space, using dedicated networks
per output coefficient and output augmentation. In several cases, small
improvements can be observed, but the most significant improvement still comes
from the addition of a second network that models the residual error. Since the
residual error for more general surrogate waveform models (when e.g.
eccentricity is included) may also have a specific structure, one can expect
our method to be applicable to cases where the gain in accuracy could lead to
significant gains in computational time.


title:
DiFT: Differentiable Differential Feature Transform for Multi-View
  Stereo

abstract:
  We present a novel framework to automatically learn to transform the
differential cues from a stack of images densely captured with a rotational
motion into spatially discriminative and view-invariant per-pixel features at
each view. These low-level features can be directly fed to any existing
multi-view stereo technique for enhanced 3D reconstruction. The lighting
condition during acquisition can also be jointly optimized in a differentiable
fashion. We sample from a dozen of pre-scanned objects with a wide variety of
geometry and reflectance to synthesize a large amount of high-quality training
data. The effectiveness of our features is demonstrated on a number of
challenging objects acquired with a lightstage, comparing favorably with
state-of-the-art techniques. Finally, we explore additional applications of
geometric detail visualization and computational stylization of complex
appearance.


title:
Don't Say What You Don't Know: Improving the Consistency of Abstractive
  Summarization by Constraining Beam Search

abstract:
  Abstractive summarization systems today produce fluent and relevant output,
but often "hallucinate" statements not supported by the source text. We analyze
the connection between hallucinations and training data, and find evidence that
models hallucinate because they train on target summaries that are unsupported
by the source. Based on our findings, we present PINOCCHIO, a new decoding
method that improves the consistency of a transformer-based abstractive
summarizer by constraining beam search to avoid hallucinations. Given the model
states and outputs at a given step, PINOCCHIO detects likely model
hallucinations based on various measures of attribution to the source text.
PINOCCHIO backtracks to find more consistent output, and can opt to produce no
summary at all when no consistent generation can be found. In experiments, we
find that PINOCCHIO improves the consistency of generation (in terms of F1) by
an average of~67% on two abstractive summarization datasets.


title:
General form of almost instantaneous fixed-to-variable-length codes and
  optimal code tree construction

abstract:
  A general class of the almost instantaneous fixed-to-variable-length (AIFV)
codes is proposed, which contains every possible binary code we can make when
allowing finite bits of decoding delay. The proposed codes, N-bit-delay AIFV
codes, are represented by multiple code trees with high flexibility. The paper
guarantees them to be uniquely decodable and present a code-tree construction
algorithm under a reasonable condition. The presented algorithm provides us
with a set of code trees, which achieves minimum expected code length, among a
subset of N-bit-delay AIFV codes for an arbitrary source. The experiments show
that the proposed codes can perform more efficiently compared to the
conventional AIFV-m and Huffman codes. Additionally, in some reasonable cases,
the proposed codes even outperform the 32-bit-precision range codes. The
theoretical and experimental results in this paper are expected to be very
useful for further study on AIFV codes.


title: On Sombor index of graphs with a given number of cut-vertices

abstract:
  Introduced by Gutman in 2021, the Sombor index is a novel graph-theoretic
topological descriptor possessing potential applications in the modeling of
thermodynamic properties of compounds. Let G^k_n be the set of all n-vertex
connected graphs with k cut-vertices. In this paper, we present minimum Sombor
indices of graphs in G^k_n. The corresponding extremal graphs have been
characterized as well.


title:
Instance-level loss based multiple-instance learning for acoustic scene
  classification

abstract:
  In acoustic scene classification (ASC) task, an acoustic scene consists of
diverse attributes and is inferred by identifying combinations of some distinct
attributes among them. This study aims to extract and cluster these attributes
effectively using a multiple-instance learning (MIL) framework for ASC. MIL,
known as one of the weakly supervised learning methods, is a way to extract
instances from input data and infer a scene corresponding to the input data
with those unlabeled instances. We develop the MIL framework more suitable for
ASC systems, adopting instance-level labels and instance-level loss, which are
effective in extracting and clustering instances. As a result, the witness rate
increases significantly compared to the framework without instance-level loss
and labels. Also in several MIL-based ASC systems, the classification accuracy
improves by about 5 to 11% than without instance-level loss. In addition, we
designed a fully separated convolutional module which is a low-complexity
neural network consisting of pointwise, frequency-sided depthwise, and
temporal-sided depthwise convolutional filters. Considering both complexity and
performance, our proposed system is more practical compared to previous systems
on the DCASE 2019 challenge task 1-A leader board. We surpassed the third-place
model by achieving a performance of 82.3\% with only the model complexity of
417K, which is at least 40 times fewer than other systems.


title:
Sparse Bayesian Inference on Positive-valued Data using Global-local
  Shrinkage Priors

abstract:
  In various applications, we deal with high-dimensional positive-valued data
that often exhibits sparsity. This paper develops a new class of continuous
global-local shrinkage priors tailored to analyzing positive-valued data where
most of the underlying means are concentrated around a certain value. Unlike
existing shrinkage priors, our new prior is a shape-scale mixture of
inverse-gamma distributions, which has a desirable interpretation of the form
of the posterior mean. We show that the proposed prior has two desirable
theoretical properties; Kullback-Leibler super-efficiency under sparsity and
robust shrinkage rules for large observations. We propose an efficient sampling
algorithm to carry out posterior inference. The performance of the proposed
method is illustrated through simulation and two real data examples, the
average length of hospital stay for COVID-19 in South Korea and adaptive
variance estimation of gene expression data.


title:
Open Set Recognition using Vision Transformer with an Additional
  Detection Head

abstract:
  Deep neural networks have demonstrated prominent capacities for image
classification tasks in a closed set setting, where the test data come from the
same distribution as the training data. However, in a more realistic open set
scenario, traditional classifiers with incomplete knowledge cannot tackle test
data that are not from the training classes. Open set recognition (OSR) aims to
address this problem by both identifying unknown classes and distinguishing
known classes simultaneously. In this paper, we propose a novel approach to OSR
that is based on the vision transformer (ViT) technique. Specifically, our
approach employs two separate training stages. First, a ViT model is trained to
perform closed set classification. Then, an additional detection head is
attached to the embedded features extracted by the ViT, trained to force the
representations of known data to class-specific clusters compactly. Test
examples are identified as known or unknown based on their distance to the
cluster centers. To the best of our knowledge, this is the first time to
leverage ViT for the purpose of OSR, and our extensive evaluation against
several OSR benchmark datasets reveals that our approach significantly
outperforms other baseline methods and obtains new state-of-the-art
performance.


title:
Understanding and Improving Sequence-to-Sequence Pretraining for Neural
  Machine Translation

abstract:
  In this paper, we present a substantial step in better understanding the SOTA
sequence-to-sequence (Seq2Seq) pretraining for neural machine
translation~(NMT). We focus on studying the impact of the jointly pretrained
decoder, which is the main difference between Seq2Seq pretraining and previous
encoder-based pretraining approaches for NMT. By carefully designing
experiments on three language pairs, we find that Seq2Seq pretraining is a
double-edged sword: On one hand, it helps NMT models to produce more diverse
translations and reduce adequacy-related translation errors. On the other hand,
the discrepancies between Seq2Seq pretraining and NMT finetuning limit the
translation quality (i.e., domain discrepancy) and induce the over-estimation
issue (i.e., objective discrepancy). Based on these observations, we further
propose simple and effective strategies, named in-domain pretraining and input
adaptation to remedy the domain and objective discrepancies, respectively.
Experimental results on several language pairs show that our approach can
consistently improve both translation performance and model robustness upon
Seq2Seq pretraining.


title:
On commutator-based linearization of vector-spinor nonlinear
  supersymmetry and Rarita-Schwinger fields

abstract:
  We show that a vector-spinor (spin-3/2) nonlinear supersymmetry (vsNLSUSY) is
linearized for both $N = 1$ and $N$-extended SUSY in flat spacetime based on
the commutator algebra by using functionals (composites) of spin-3/2
Nambu-Goldstone (NG) fermions, which are expressed as simple products of powers
of the spin-3/2 NG fermions and a fundamental determinant in the vsNLSUSY
theory. By using those functionals, we define basic component fields in a
linearized vsSUSY theory including spin-3/2 fields, general auxiliary ones and
a $D$-term. The general forms of linear (rigid) vsSUSY transformations for the
component fields are determined uniquely from the commutator-based
linearization procedure. Appropriate recombinations of the functionals of the
spin-3/2 NG fermions are also considered for $N = 1$ SUSY as a generalization
of the Wess-Zumino gauge. We find that variations of the recombinations under
the vsNLSUSY transformations include linear spin-1/2 SUSY ones of spin-(3/2, 1)
fields with $U(1)$ gauge invariance.


title:
Panini-Net: GAN Prior Based Degradation-Aware Feature Interpolation for
  Face Restoration

abstract:
  Emerging high-quality face restoration (FR) methods often utilize pre-trained
GAN models (\textit{i.e.}, StyleGAN2) as GAN Prior. However, these methods
usually struggle to balance realness and fidelity when facing various
degradation levels. Besides, there is still a noticeable visual quality gap
compared with pre-trained GAN models. In this paper, we propose a novel GAN
Prior based degradation-aware feature interpolation network, dubbed Panini-Net,
for FR tasks by explicitly learning the abstract representations to distinguish
various degradations. Specifically, an unsupervised degradation representation
learning (UDRL) strategy is first developed to extract degradation
representations (DR) of the input degraded images. Then, a degradation-aware
feature interpolation (DAFI) module is proposed to dynamically fuse the two
types of informative features (\textit{i.e.}, features from input images and
features from GAN Prior) with flexible adaption to various degradations based
on DR. Ablation studies reveal the working mechanism of DAFI and its potential
for editable FR. Extensive experiments demonstrate that our Panini-Net achieves
state-of-the-art performance for multi-degradation face restoration and face
super-resolution. The source code is available at
https://github.com/jianzhangcs/panini.


title:
Structurally Diverse Sampling Reduces Spurious Correlations in Semantic
  Parsing Datasets

abstract:
  A rapidly growing body of research has demonstrated the inability of NLP
models to generalize compositionally and has tried to alleviate it through
specialized architectures, training schemes, and data augmentation, among other
approaches. In this work, we study a different relatively under-explored
approach: sampling diverse train sets that encourage compositional
generalization. We propose a novel algorithm for sampling a structurally
diverse set of instances from a labeled instance pool with structured outputs.
Evaluating on 5 semantic parsing datasets of varying complexity, we show that
our algorithm performs competitively with or better than prior algorithms in
not only compositional template splits but also traditional IID splits of all
but the least structurally diverse datasets. In general, we find that diverse
train sets lead to better generalization than random training sets of the same
size in 9 out of 10 dataset-split pairs, with over 10% absolute improvement in
5, providing further evidence to their sample efficiency. Moreover, we show
that structural diversity also makes for more comprehensive test sets that
require diverse training to succeed on. Finally, we use information theory to
show that reduction in spurious correlations between substructures may be one
reason why diverse training sets improve generalization.


title:
Resource estimations for the Hamiltonian simulation in correlated
  electron materials

abstract:
  Correlated electron materials, such as superconductors and magnetic
materials, are regarded as fascinating targets in quantum computing. However,
the quantitative resources, specifically the number of quantum gates and
qubits, required to perform a quantum algorithm for the correlated electron
materials remain unclear. In this study, we estimate the resources required for
the Hamiltonian simulation algorithm for correlated electron materials,
specifically for organic superconductors, iron-based superconductors, binary
transition metal oxides, and perovskite oxides, using the fermionic swap
network. The effective Hamiltonian derived using the $ab~initio$ downfolding
method is adopted for the Hamiltonian simulation, and a procedure for the
resource estimation by using the fermionic swap network for the effective
Hamiltonians including the exchange interactions is proposed. For example, in
the system for the $10^2$ unit cells, the estimated number of gates per Trotter
step and qubits are approximately $10^7$ and $10^3$, respectively, on average
for the correlated electron materials. Furthermore, our results show that the
number of interaction terms in the effective Hamiltonian, especially for the
Coulomb interaction terms, is dominant in the gate resources when the number of
unit cells constituting the whole system is up to $10^2$, whereas the number of
fermionic swap operations is dominant when the number of unit cells is more
than $10^3$.


title:
Characterization and modeling of the influence of initial microstructure
  on recrystallization of zircaloy-4 during hot forming

abstract:
  The present article proposes a detailed study of recrystallization of
zircaloy-4 under hot forming conditions by means of experimental and numerical
tools. Thermomechanical tests and characterization campaigns that have been
necessary for this work are described. Then, the different microstructure
evolution mechanisms are characterized, from the simplest one to the most
complex. Grain growth kinetics is quantified and the influence of second phase
particle population is analyzed. Then, a complete study of dynamic and
post-dynamic recrystallization is provided. The occurrence of a continuous
mechanism is confirmed and the influence of thermomechanical conditions upon
recrystallization is assessed. Later, the numerical framework used to simulate
grain growth, continuous and post-dynamic recrystallization is presented. After
having successfully reproduced the grain coarsening kinetics with and without
second phase particles, the model is used to describe continuous dynamic
recrystallization and post-dynamic recrystallization from an initial equiaxed
and fully recrystallized microstructure. The agreement between experimental and
numerical results is assessed in detail. Finally, post-dynamic
recrystallization is simulated, starting from two deformed microstructures
characterized by electron back-scattered diffraction technique and immersed
into simulations. This allows to discuss and reproduce the influence of initial
microstructure.


title:
Playing with blocks: Toward re-usable deep learning models for
  side-channel profiled attacks

abstract:
  This paper introduces a deep learning modular network for side-channel
analysis. Our deep learning approach features the capability to exchange part
of it (modules) with others networks. We aim to introduce reusable trained
modules into side-channel analysis instead of building architectures for each
evaluation, reducing the body of work when conducting those. Our experiments
demonstrate that our architecture feasibly assesses a side-channel evaluation
suggesting that learning transferability is possible with the network we
propose in this paper.


title:
Photonic quantum kinetic theory in curved spacetime and the spin Hall
  effect

abstract:
  We formulate the quantum kinetic theory for polarized photons in curved
spacetime based on quantum field theory. From this framework, the photonic
chiral/zilch vortical effects are reproduced in a rigidly rotating coordinate.
In a spatially inhomogeneous coordinate, we derive the spin Hall effect for the
photon helicity current and energy current in equilibrium. Our derivation
reveals that such photonic Hall effects are related to the photonic vortical
effects via the Lorentz invariance and their transport coefficients match each
other.


title:
The Devil Is in the Details: Window-based Attention for Image
  Compression

abstract:
  Learned image compression methods have exhibited superior rate-distortion
performance than classical image compression standards. Most existing learned
image compression models are based on Convolutional Neural Networks (CNNs).
Despite great contributions, a main drawback of CNN based model is that its
structure is not designed for capturing local redundancy, especially the
non-repetitive textures, which severely affects the reconstruction quality.
Therefore, how to make full use of both global structure and local texture
becomes the core problem for learning-based image compression. Inspired by
recent progresses of Vision Transformer (ViT) and Swin Transformer, we found
that combining the local-aware attention mechanism with the global-related
feature learning could meet the expectation in image compression. In this
paper, we first extensively study the effects of multiple kinds of attention
mechanisms for local features learning, then introduce a more straightforward
yet effective window-based local attention block. The proposed window-based
attention is very flexible which could work as a plug-and-play component to
enhance CNN and Transformer models. Moreover, we propose a novel Symmetrical
TransFormer (STF) framework with absolute transformer blocks in the
down-sampling encoder and up-sampling decoder. Extensive experimental
evaluations have shown that the proposed method is effective and outperforms
the state-of-the-art methods. The code is publicly available at
https://github.com/Googolxx/STF.


title:
Estimate Analysis of a Fully Discrete Mixed Finite Element Scheme for
  Stochastic Incompressible Navier-Stokes Equations with Multiplicative Noise

abstract:
  This paper is concerned with stochastic incompressible Navier-Stokes
equations with multiplicative noise in two dimensions with respect to periodic
boundary conditions. Based on the Helmholtz decomposition of the multiplicative
noise, semi-discrete and fully discrete time-stepping algorithms are proposed.
The convergence rates for mixed finite element methods based space-time
approximation with respect to convergence in probability for the velocity and
the pressure are obtained. By using the negative norm technique, the $H^1$ and
$L^2$ convergence of this scheme for the velocity is derived.


title: Can Pre-trained Language Models Interpret Similes as Smart as Human?

abstract:
  Simile interpretation is a crucial task in natural language processing.
Nowadays, pre-trained language models (PLMs) have achieved state-of-the-art
performance on many tasks. However, it remains under-explored whether PLMs can
interpret similes or not. In this paper, we investigate the ability of PLMs in
simile interpretation by designing a novel task named Simile Property Probing,
i.e., to let the PLMs infer the shared properties of similes. We construct our
simile property probing datasets from both general textual corpora and
human-designed questions, containing 1,633 examples covering seven main
categories. Our empirical study based on the constructed datasets shows that
PLMs can infer similes' shared properties while still underperforming humans.
To bridge the gap with human performance, we additionally design a
knowledge-enhanced training objective by incorporating the simile knowledge
into PLMs via knowledge embedding methods. Our method results in a gain of
8.58% in the probing task and 1.37% in the downstream task of sentiment
classification. The datasets and code are publicly available at
https://github.com/Abbey4799/PLMs-Interpret-Simile.


title:
Break the efficiency limitations of dissipative Kerr soliton using
  nonlinear couplers

abstract:
  Dissipative Kerr soliton (DKS) offers a compact solution of coherent comb
sources and holds huge potential for applications, but has long been suffering
from poor power conversion efficiency when driving by a continuous-wave laser.
Here, a general approach to resolving this challenge is provided. By deriving
the critical coupling condition of a multimode nonlinear optics system in a
generalized theoretical framework, two efficiency limitations of the
conventional pump method of DKS are revealed: the effective coupling rate is
too small and is also power-dependent. Nonlinear couplers are proposed to
sustain the DKS indirectly through nonlinear energy conversion processes,
realizing a power-adaptive effective coupling rate to the DKS and matching the
total dissipation rate of the system, which promises near-unity power
conversion efficiencies. For instance, a conversion efficiency exceeding
$90\:\%$ is predicted for aluminum nitride microrings with a nonlinear coupler
utilizing second-harmonic generation. The nonlinear coupler approach for
high-efficiency generation of DKS is experimentally feasible as its mechanism
applies to various nonlinear processes, including Raman and Brillouin
scattering, and thus paves the way of micro-solitons towards practical
applications.


title:
Coach-assisted Multi-Agent Reinforcement Learning Framework for
  Unexpected Crashed Agents

abstract:
  Multi-agent reinforcement learning is difficult to be applied in practice,
which is partially due to the gap between the simulated and real-world
scenarios. One reason for the gap is that the simulated systems always assume
that the agents can work normally all the time, while in practice, one or more
agents may unexpectedly "crash" during the coordination process due to
inevitable hardware or software failures. Such crashes will destroy the
cooperation among agents, leading to performance degradation. In this work, we
present a formal formulation of a cooperative multi-agent reinforcement
learning system with unexpected crashes. To enhance the robustness of the
system to crashes, we propose a coach-assisted multi-agent reinforcement
learning framework, which introduces a virtual coach agent to adjust the crash
rate during training. We design three coaching strategies and the re-sampling
strategy for our coach agent. To the best of our knowledge, this work is the
first to study the unexpected crashes in the multi-agent system. Extensive
experiments on grid-world and StarCraft II micromanagement tasks demonstrate
the efficacy of adaptive strategy compared with the fixed crash rate strategy
and curriculum learning strategy. The ablation study further illustrates the
effectiveness of our re-sampling strategy.


title: Low-rank Parareal: a low-rank parallel-in-time integrator

abstract:
  The Parareal algorithm of Lions, Maday, and Turinici is a well-known time
parallel algorithm for evolution problems. It is based on a Newton-like
iteration, with cheap coarse corrections performed sequentially, and expensive
fine solves performed in parallel. In this work, we apply Parareal to evolution
problems that admit good low-rank approximations and for which the dynamical
low-rank approximation (DLRA), proposed by Koch and Lubich, can be used as time
stepper. Many discrete integrators for DLRA have recently been proposed, based
on splitting the projected vector field or by applying projected Runge--Kutta
methods. The cost and accuracy of these methods are mostly governed by the rank
chosen for the approximation. We want to use these properties in a new method,
that we call low-rank Parareal, in order to obtain a time-parallel DLRA solver
for evolution problems. We propose an analysis of the algorithm on affine
linear problems and illustrate our results numerically.


title:
PPCD-GAN: Progressive Pruning and Class-Aware Distillation for
  Large-Scale Conditional GANs Compression

abstract:
  We push forward neural network compression research by exploiting a novel
challenging task of large-scale conditional generative adversarial networks
(GANs) compression. To this end, we propose a gradually shrinking GAN
(PPCD-GAN) by introducing progressive pruning residual block (PP-Res) and
class-aware distillation. The PP-Res is an extension of the conventional
residual block where each convolutional layer is followed by a learnable mask
layer to progressively prune network parameters as training proceeds. The
class-aware distillation, on the other hand, enhances the stability of training
by transferring immense knowledge from a well-trained teacher model through
instructive attention maps. We train the pruning and distillation processes
simultaneously on a well-known GAN architecture in an end-to-end manner. After
training, all redundant parameters as well as the mask layers are discarded,
yielding a lighter network while retaining the performance. We comprehensively
illustrate, on ImageNet 128x128 dataset, PPCD-GAN reduces up to 5.2x (81%)
parameters against state-of-the-arts while keeping better performance.


title:
A distributionally robust optimization approach to two-sided chance
  constrained stochastic model predictive control with unknown noise
  distribution

abstract:
  In this work, we propose a distributionally robust stochastic model
predictive control (DR-SMPC) algorithm to address the problem of two-sided
chance constrained discrete-time linear system corrupted by additive noise. The
prevalent mechanism to cope with two-sided chance constraints is the so-called
risk allocation approach, which conservatively approximates the two-sided
chance constraints with two single chance constraints by applying the Boole's
inequality. In this proposed DR-SMPC framework, an exact tractable second-order
cone (SOC) approach is adopted to abstract the two-sided chance constraints by
considering the first and second moments of the noise. The proposed DR-SMPC
algorithm is able to guarantee that the worst-case probability of violating
both the upper and lower limits of safety constraints is within the
pre-specified maximum probability (PsMP). By flexibly adjusting this PsMP, the
feasible region of the initial states can be increased for the SMPC problem.
The recursive feasibility and convergence of the proposed DR-SMPC are
established rigorously by introducing binary initialization strategy of nominal
state. Simulation studies of two practical cases are conducted to demonstrate
the effectiveness of the proposed DR-SMPC algorithm.


title: Simple machine-learned interatomic potentials for complex alloys

abstract:
  Developing data-driven machine-learning interatomic potentials for materials
containing many elements becomes increasingly challenging due to the vast
configuration space that must be sampled by the training data. We study the
learning rates and achievable accuracy of machine-learning interatomic
potentials for many-element alloys with different combinations of descriptors
for the local atomic environments. We show that for a five-element alloy
system, potentials using simple low-dimensional descriptors can reach
meV/atom-accuracy with modestly sized training datasets, significantly
outperforming the high-dimensional SOAP descriptor in data efficiency,
accuracy, and speed. In particular, we develop a computationally fast
machine-learned and tabulated Gaussian approximation potential (tabGAP) for
Mo-Nb-Ta-V-W alloys with a combination of two-body, three-body, and a new
simple scalar many-body density descriptor based on the embedded atom method.


title: KinyaBERT: a Morphology-aware Kinyarwanda Language Model

abstract:
  Pre-trained language models such as BERT have been successful at tackling
many natural language processing tasks. However, the unsupervised sub-word
tokenization methods commonly used in these models (e.g., byte-pair encoding -
BPE) are sub-optimal at handling morphologically rich languages. Even given a
morphological analyzer, naive sequencing of morphemes into a standard BERT
architecture is inefficient at capturing morphological compositionality and
expressing word-relative syntactic regularities. We address these challenges by
proposing a simple yet effective two-tier BERT architecture that leverages a
morphological analyzer and explicitly represents morphological
compositionality. Despite the success of BERT, most of its evaluations have
been conducted on high-resource languages, obscuring its applicability on
low-resource languages. We evaluate our proposed method on the low-resource
morphologically rich Kinyarwanda language, naming the proposed model
architecture KinyaBERT. A robust set of experimental results reveal that
KinyaBERT outperforms solid baselines by 2% in F1 score on a named entity
recognition task and by 4.3% in average score of a machine-translated GLUE
benchmark. KinyaBERT fine-tuning has better convergence and achieves more
robust results on multiple tasks even in the presence of translation noise.


title:
Anisotropic constant-roll inflationary scenario with complex
  quintessence field and swampland conjectures

abstract:
  In this paper, with the presence of swampland conjecture, we use the complex
form of a scalar field and investigate the anisotropic constant-roll of an
inflationary scenario. Recently, the complex quintessence field has been used
to describe the accelerating expansion of the universe which has had
interesting results connecting with various conditions. Therefore, the
Lagrangian density of the quintessence field leads to obtaining the equations
of the complex scalar field. Also, we use the anisotropic constant-roll
conditions and the field equation and calculate the exact solutions for some
cosmology parameters such as Hubble parameter and potential. Using the exact
solution of potential and refined swampland conditions, we plot some figures.
In that case, the figures lead us to have challenges between three concepts as
complex quintessence, anisotropic constant-roll conditions, and swampland
conjectures. Then we discuss their compatibility and incompatibility with
corresponding results. Finally, we will analyze the complex quintessence in
examining the inflationary scenario.


title:
Searching for Islands of Reionization: A Potential Ionized Bubble
  Powered by a Spectroscopic Overdensity at z = 8.7

abstract:
  We present the results from a spectroscopic survey using the MOSFIRE
near-infrared spectrograph on the 10m Keck telescope to search for Ly$\alpha$
emission from candidate galaxies at z ~9-10 in four of the CANDELS fields
(GOODS-N, EGS, UDS, and COSMOS). We observed 11 target galaxies, detecting
Ly$\alpha$ from one object in ~8.1 hours of integration, at z = 8.665 +/- 0.001
with an integrated signal-to-noise > 7. This galaxy is in the CANDELS Extended
Groth Strip (EGS) field, and lies physically close (3.5 physical Mpc [pMpc]) to
another confirmed galaxy in this field with Ly$\alpha$ detected at z=8.683
(Zitrin et al. 2015). The detection of Ly$\alpha$ suggests the existence of
large (~1 pMpc) ionized bubbles fairly early in the reionization process. We
explore the ionizing output needed to create bubbles of this size at this epoch
and find that such a bubble requires more than the ionizing power provided by
the full expected population of galaxies (by integrating the UV Luminosity
Function down to M_UV = -13). The Ly$\alpha$ we detect would be able to escape
the predominantly-neutral intergalactic medium at this epoch if our galaxy is
inhabiting an overdensity, which would be consistent with the photometric
overdensity previously identified in this region by Finkelstein et al. 2021.
This implies that the CANDELS EGS field is hosting an overdensity at z = 8.7
which is powering one (or more) ionized bubbles, a hypothesis that will be
imminently testable with forthcoming James Webb Space Telescope observations in
this field.


title: A Meshless Solution of a Small-Strain Plasticity Problem

abstract:
  When the deformations of a solid body are sufficiently large, parts of the
body undergo a permanent deformation commonly refereed to as plastic
deformation. Several plasticity models describing such phenomenon have been
proposed, e.g. von Mises, Tresca, etc. Traditionally, the finite element method
(FEM) is the numerical tool of choice for engineers who are solving such
problems. In this work, however, we present the implementation of the von Mises
plasticity model with non-linear isotropic hardening in our in-house developed
MEDUSA library, utilizing a variant of meshless methods -- namely the radial
basis function-generated finite differences (RBF-FD). We define a simple plane
stress case, where a 2D block is fixed at one edge, and a tensile force, which
causes the block to deform, is applied to it at the opposite edge. We show that
results are in good agreement with the numerical solution obtained by Abaqus
FEA, a commercial FEM solver.


title:
A Strategy for Low-Mass Dark Matter Searches with Cryogenic Detectors in
  the SuperCDMS SNOLAB Facility

abstract:
  The SuperCDMS Collaboration is currently building SuperCDMS SNOLAB, a dark
matter search focused on nucleon-coupled dark matter in the 1-5 GeV mass range.
Looking to the future, the Collaboration has developed a set of
experience-based upgrade scenarios, as well as novel directions, to extend the
search for dark matter using the SuperCDMS technology in the SNOLAB facility.
The experienced-based scenarios are forecasted to probe many square decades of
unexplored dark matter parameter space below 5 GeV, covering over 6 decades in
mass: 1-100 eV for dark photons and axion-like particles, 1-100 MeV for
dark-photon-coupled light dark matter, and 0.05-5 GeV for nucleon-coupled dark
matter. They will reach the neutrino fog in the 0.5-5 GeV mass range and test a
variety of benchmark models and sharp targets. The novel directions involve
greater departures from current SuperCDMS technology but promise even greater
reach in the long run, and their development must begin now for them to be
available in a timely fashion.
  The experienced-based upgrade scenarios rely mainly on dramatic improvements
in detector performance based on demonstrated scaling laws and reasonable
extrapolations of current performance. Importantly, these improvements in
detector performance obviate significant reductions in background levels beyond
current expectations for the SuperCDMS SNOLAB experiment. Given that the
dominant limiting backgrounds for SuperCDMS SNOLAB are cosmogenically created
radioisotopes in the detectors, likely amenable only to isotopic purification
and an underground detector life-cycle from before crystal growth to detector
testing, the potential cost and time savings are enormous and the necessary
improvements much easier to prototype.

categories: physics.ins-det astro-ph.IM hep-ex nucl-ex


title: Metriplectic foundations of gyrokinetic Vlasov-Maxwell-Landau theory

abstract:
  This letter reports on a metriplectic formulation of collisional, nonlinear
full-$f$ electromagnetic gyrokinetic theory compliant with energy conservation
and monotonic entropy production. In an axisymmetric background magnetic field,
the toroidal angular momentum is also conserved. Notably, a new collisional
current, contributing to the gyrokinetic Maxwell-Amp\`ere equation and the
gyrokinetic charge conservation law, is discovered.


title: Building AI Innovation Labs together with Companies

abstract:
  In the future, most companies will be confronted with the topic of Artificial
Intelligence (AI) and will have to decide on their strategy in this regards.
Currently, a lot of companies are thinking about whether and how AI and the
usage of data will impact their business model and what potential use cases
could look like. One of the biggest challenges lies in coming up with
innovative solution ideas with a clear business value. This requires business
competencies on the one hand and technical competencies in AI and data
analytics on the other hand. In this article, we present the concept of AI
innovation labs and demonstrate a comprehensive framework, from coming up with
the right ideas to incrementally implementing and evaluating them regarding
their business value and their feasibility based on a company's capabilities.
The concept is the result of nine years of working on data-driven innovations
with companies from various domains. Furthermore, we share some lessons learned
from its practical applications. Even though a lot of technical publications
can be found in the literature regarding the development of AI models and many
consultancy companies provide corresponding services for building AI
innovations, we found very few publications sharing details about what an
end-to-end framework could look like.


title:
On recurrence in zero-dimensional locally compact flow with compactly
  generated phase group

abstract:
  Let $X$ be a zero-dimensional locally compact Hausdorff space not necessarily
metric and $G$ a compactly generated topological group not necessarily abelian
or countable. We define recurrence at a point for any continuous action of $G$
on $X$, and then, show that if $\overline{Gx}$ is compact for all $x\in X$, the
conditions (i) this dynamics is pointwise recurrent, (ii) $X$ is a union of
$G$-minimal sets, (iii) the $G$-orbit closure relation is closed in $X\times
X$, and (iv) $X\ni x\mapsto \overline{Gx}\in 2^X$ is continuous, are pairwise
equivalent. Consequently, if this dynamics is distal, then it is
equicontinuous.


title: Inverse mean curvature flow with a free boundary in hyperbolic space

abstract:
  We study inverse mean curvature flow with free boundary supported on geodesic
spheres in hyperbolic space. Starting from any convex hypersurface inside a
geodesic ball with a free boundary, the flow converges to a totally geodesic
disk in finite time. Using the convergence result, we show a Willmore type
inequality.


title: Infinitely many families of Sasaki-Einstein metrics on spheres

abstract:
  We show that there exist infinitely many families of Sasaki-Einstein metrics
on every odd-dimensional standard sphere of dimension at least $5$. We also
show that the same result is true for all odd-dimensional exotic spheres that
bound parallelizable manifolds.


title: Observability for Non-autonomous Systems

abstract:
  We study non-autonomous observation systems \begin{align*}
  \dot{x}(t) = A(t) x(t),\quad y(t) = C(t) x(t),\quad x(0) = x_0\in X,
\end{align*} where $(A(t))$ is a strongly measurable family of closed operators
on a Banach space $X$ and $(C(t))$ is a family of bounded observation operators
from $X$ to a Banach space $Y$. Based on an abstract uncertainty principle and
a dissipation estimate, we prove that the observation system satisfies a
final-state observability estimate in $\mathrm{L}^r(E; Y)$ for measurable
subsets $E \subseteq [0,T], T > 0$. An application of the above result to
families of uniformly strongly elliptic differential operators $A(t)$ on
$\mathrm{L}^p(\mathbb{R}^d)$ and observation operators $C(t)u =
\mathbf{1}_{\Omega(t)} u$ is presented. In this setting, we give sufficient and
necessary geometric conditions on the family of sets $(\Omega(t))$ such that
the corresponding observation system satisfies a final-state observability
estimate.


title:
Experimental Demonstration of High-Rate Discrete-Modulated
  Continuous-Variable Quantum Key Distribution System

abstract:
  A high-rate continuous-variable quantum key distribution (CV-QKD) system
based on high-order discrete modulation is experimentally investigated. With
the help of the novel system scheme, effective digital signal processing
algorithms and advanced analytical security proof method, the transmission
results of 5 km, 10 km, 25 km, and 50 km are achieved for the 1 GBaud optimized
quantum signals. Correspondingly, the asymptotic secret key rate (SKR) is
288.421 Mbps, 159.395 Mbps, 50.004 Mbps and 7.579 Mbps for discrete Gaussian
(DG) 64QAM, and 326.708 Mbps, 179.348 Mbps, 50.623 Mbps and 9.212 Mbps for DG
256QAM. Under the same parameters, the achieved SKRs of DG 256QAM is almost
same to ideal Gaussian modulation. In this case, the demonstrated high-rate
discrete modulated CV-QKD system has the application potential for high speed
security communication under tens of kilometers.


title:
Direct laser printing of high-resolution physically unclonable
  anti-counterfeit labels

abstract:
  Security labels combining facile structural color readout and physically
unclonable one-way function (PUF) approach provide promising strategy for
fighting against forgery of marketable products. Here, we justify direct
femtosecond-laser printing, a simple and scalable technology, for fabrication
of high-resolution (12500 dots per inch) and durable PUF labels with a
substantially large encoding capacity of 10$^{895}$ and a simple
spectroscopy-free optical signal readout. The proposed tags are comprised of
laser-printed plasmonic nanostructures exhibiting unique light scattering
behavior and unclonable 3D geometry. Uncontrollable stochastic variation of the
nanostructure geometry in the process of their spot-by-spot printing results in
random and broadband variation of the scattering color of each laser printed
"pixel", making laser-printed patterns unique and suitable for PUF labeling.


title:
Fusing Local Similarities for Retrieval-based 3D Orientation Estimation
  of Unseen Objects

abstract:
  In this paper, we tackle the task of estimating the 3D orientation of
previously-unseen objects from monocular images. This task contrasts with the
one considered by most existing deep learning methods which typically assume
that the testing objects have been observed during training. To handle the
unseen objects, we follow a retrieval-based strategy and prevent the network
from learning object-specific features by computing multi-scale local
similarities between the query image and synthetically-generated reference
images. We then introduce an adaptive fusion module that robustly aggregates
the local similarities into a global similarity score of pairwise images.
Furthermore, we speed up the retrieval process by developing a fast
clustering-based retrieval strategy. Our experiments on the LineMOD,
LineMOD-Occluded, and T-LESS datasets show that our method yields a
significantly better generalization to unseen objects than previous works.


title:
Constraints on Neutron-Mirror-Neutron Oscillation from Neutron Star
  Cooling

abstract:
  We address a method of limiting neutron-mirror neutron mixing ($\epsilon_{n
n'}$) by analyzing its effect on neutron star (NS) heating. This method employs
observational bounds on the surface temperature of NSs to constrain
$\epsilon_{n n'}$. The obtained bound is so stringent that it would exclude any
discovery of $n-n'$ oscillation by the planned terrestrial experiments.
Motivated by this last crucially important fact, we critically analyze this
suggestion and note a very interesting new effect present in nearly exact
mirror models, which significantly affect this bound. The new element in our
discussion is that in mirror models there is the $\beta$ decay $n' \to p'+ e'
+\bar{\nu}'_{e}$, which creates a cloud of mirror particles $n'$, $p'$, $e'$
and $D'$ inside the NS. The $e'$ can "rob" the energy generated by the $n \to
n'$ transition from the NS. This is achieved via $e-e'$ scattering enabled by
the presence of a (minute) milli-charge in mirror particles. This energy is
emitted as unobserved mirror photons via fast mirror bremsstrahlung. This will
lead to relaxing of the stringent bounds on $\epsilon_{nn'}$.


title:
Optimal remote state preparation in high dimensions via a non-maximally
  entangled channel with limited quantum resources

abstract:
  Remote state preparation serves as a useful way for transmitting information
encoded in quantum states between distant places without physically sending
themselves. While an ideal remote state preparation scheme requires a pure
maximally entangled channel, the reality is that shared entanglement is always
severely degraded due to various decoherence mechanisms, seriously
deteriorating the performance of the remote state preparation as a result.
Herein, we proposed a distinctive remote state preparation scheme where a
general quantum state is prepared deterministically via a generally entangled
channel. The success probability of preparing a d-dimensional quantum state is
maintained at 100\% no matter how weak the entanglement of the quantum channel
is. Our work contributes to mitigating the effect of decoherence to deliver
quantum information more efficiently and paves the way for realizing quantum
communication practically.


title: QED theory of the nuclear magnetic shielding in H and $^3$He

abstract:
  The derivation of leading quantum electrodynamic corrections to the nuclear
magnetic shielding in light hydrogen- and helium-like atomic systems is
described in detail. The presented theoretical approach applies to any light
atomic and molecular systems, enabling the determination of the magnetic moment
of light nuclei with much higher precision than known presently.


title:
Comparing the frequentist and Bayesian periodic signal detection: rates
  of statistical mistakes and sensitivity to priors

abstract:
  We perform extensive Monte Carlo simulations to systematically compare the
frequentist and Bayesian treatments of the Lomb--Scargle periodogram. The goal
is to investigate whether the Bayesian period search is advantageous over the
frequentist one in terms of the detection efficiency, how much if yes, and how
sensitive it is regarding the choice of the priors, in particular in case of a
misspecified prior (whenever the adopted prior does not match the actual
distribution of physical objects). We find that the Bayesian and frequentist
analyses always offer nearly identical detection efficiency in terms of their
tradeoff between type-I and type-II mistakes. Bayesian detection may reveal a
formal advantage if the frequency prior is nonuniform, but this results in only
$\sim 1$ per cent extra detected signals. In case if the prior was misspecified
(adopting nonuniform one over the actual uniform) this may turn into an
opposite advantage of the frequentist analysis. Finally, we revealed that Bayes
factor of this task appears rather overconservative if used without a
calibration against type-I mistakes (false positives), thereby necessitating
such a calibration in practice.


title: Emotion Recognition using Machine Learning and ECG signals

abstract:
  Various emotions can produce variations in electrocardiograph (ECG) signals,
distinct emotions can be distinguished by different changes in ECG signals.
This study is about emotion recognition using ECG signals. Data for four
emotions, namely happy, exciting, calm, and tense, is gathered. The raw data is
then de-noised with a finite impulse filter. We use the Discrete Cosine
Transform (DCT) to extract characteristics from the obtained data to increase
the accuracy of emotion recognition. The classifiers Support Vector Machine
(SVM), Random Forest, and K-NN are explored. To find the optimal parameters for
the SVM classifier, the Particle Swarm Optimization (PSO) technique is used.
The results of the comparison of these classification methods demonstrate that
the SVM approach has a greater accuracy in emotion recognition, which may be
applied in practice


title:
A binary origin for the first isolated stellar-mass black hole detected
  with astrometric microlensing

abstract:
  The Milky Way is believed to host hundreds of millions of quiescent
stellar-mass black holes (BHs). In the last decade, some of these objects have
been potentially uncovered via gravitational microlensing events. All these
detections resulted in a degeneracy between the velocity and the mass of the
lens. This degeneracy has been lifted, for the first time, with the recent
astrometric microlensing detection of OB110462. However, two independent
studies reported very different lens mass for this event. Sahu et al. (2022)
inferred a lens mass of 7.1 $\pm$ 1.3 Msun, consistent with a BH, while Lam et
al. (2022) inferred 1.6-4.2 Msun, consistent with either a neutron star or a
BH. Here, we study the landscape of isolated BHs formed in the field. In
particular, we focus on the mass and center-of-mass speed of four
sub-populations: isolated BHs from single-star origin, disrupted BHs of
binary-star origin, main-sequence stars with a compact object companion, and
double compact object mergers. Our model predicts that most ($\gtrsim$ 50%)
isolated BHs in the Milky Way are of binary origin. Moreover, the origin of
low-speed (< 50 km/s) isolated BHs depends on their mass: at least $\approx$
70% of low-mass ($\lesssim$ 10 Msun) BHs are from binary origin. Under the
assumption that OB110462 is a free-floating compact object, we conclude that it
is more likely to be a BH originally belonging to a binary system. Our results
suggest that low-speed BH microlensing events can be useful to understand
binary evolution of massive stars in the Milky Way.


title: Data Efficient 3D Learner via Knowledge Transferred from 2D Model

abstract:
  Collecting and labeling the registered 3D point cloud is costly. As a result,
3D resources for training are typically limited in quantity compared to the 2D
images counterpart. In this work, we deal with the data scarcity challenge of
3D tasks by transferring knowledge from strong 2D models via RGB-D images.
Specifically, we utilize a strong and well-trained semantic segmentation model
for 2D images to augment RGB-D images with pseudo-label. The augmented dataset
can then be used to pre-train 3D models. Finally, by simply fine-tuning on a
few labeled 3D instances, our method already outperforms existing
state-of-the-art that is tailored for 3D label efficiency. We also show that
the results of mean-teacher and entropy minimization can be improved by our
pre-training, suggesting that the transferred knowledge is helpful in
semi-supervised setting. We verify the effectiveness of our approach on two
popular 3D models and three different tasks. On ScanNet official evaluation, we
establish new state-of-the-art semantic segmentation results on the
data-efficient track.


title:
E-KAR: A Benchmark for Rationalizing Natural Language Analogical
  Reasoning

abstract:
  The ability to recognize analogies is fundamental to human cognition.
Existing benchmarks to test word analogy do not reveal the underneath process
of analogical reasoning of neural models. Holding the belief that models
capable of reasoning should be right for the right reasons, we propose a
first-of-its-kind Explainable Knowledge-intensive Analogical Reasoning
benchmark (E-KAR). Our benchmark consists of 1,655 (in Chinese) and 1,251 (in
English) problems sourced from the Civil Service Exams, which require intensive
background knowledge to solve. More importantly, we design a free-text
explanation scheme to explain whether an analogy should be drawn, and manually
annotate them for each and every question and candidate answer. Empirical
results suggest that this benchmark is very challenging for some
state-of-the-art models for both explanation generation and analogical question
answering tasks, which invites further research in this area.


title: Pseudo-Q: Generating Pseudo Language Queries for Visual Grounding

abstract:
  Visual grounding, i.e., localizing objects in images according to natural
language queries, is an important topic in visual language understanding. The
most effective approaches for this task are based on deep learning, which
generally require expensive manually labeled image-query or patch-query pairs.
To eliminate the heavy dependence on human annotations, we present a novel
method, named Pseudo-Q, to automatically generate pseudo language queries for
supervised training. Our method leverages an off-the-shelf object detector to
identify visual objects from unlabeled images, and then language queries for
these objects are obtained in an unsupervised fashion with a pseudo-query
generation module. Then, we design a task-related query prompt module to
specifically tailor generated pseudo language queries for visual grounding
tasks. Further, in order to fully capture the contextual relationships between
images and language queries, we develop a visual-language model equipped with
multi-level cross-modality attention mechanism. Extensive experimental results
demonstrate that our method has two notable benefits: (1) it can reduce human
annotation costs significantly, e.g., 31% on RefCOCO without degrading original
model's performance under the fully supervised setting, and (2) without bells
and whistles, it achieves superior or comparable performance compared to
state-of-the-art weakly-supervised visual grounding methods on all the five
datasets we have experimented. Code is available at
https://github.com/LeapLabTHU/Pseudo-Q.


title:
Multiple solutions for Schr\"odinger equations on Riemannian manifolds
  via $\nabla$-theorems

abstract:
  We consider a smooth, complete and non-compact Riemannian manifold
$(\mathcal{M},g)$ of dimension $d \geq 3$, and we look for positive solutions
to the semilinear elliptic equation
  $$
  -\Delta_g w + V w = \alpha f(w) + \lambda w \quad\hbox{in $\mathcal{M}$}.
  $$
  The potential $V \colon \mathcal{M} \to \mathbb{R}$ is a continuous function
which is coercive in a suitable sense, while the nonlinearity $f$ has a
subcritical growth in the sense of Sobolev embeddings. By means of
$\nabla$-Theorems introduced by Marino and Saccon, we prove that at least three
solution exists as soon as the parameter $\lambda$ is sufficiently close to an
eigenvalue of the operator $-\Delta_g$.


title:
QS-Attn: Query-Selected Attention for Contrastive Learning in I2I
  Translation

abstract:
  Unpaired image-to-image (I2I) translation often requires to maximize the
mutual information between the source and the translated images across
different domains, which is critical for the generator to keep the source
content and prevent it from unnecessary modifications. The self-supervised
contrastive learning has already been successfully applied in the I2I. By
constraining features from the same location to be closer than those from
different ones, it implicitly ensures the result to take content from the
source. However, previous work uses the features from random locations to
impose the constraint, which may not be appropriate since some locations
contain less information of source domain. Moreover, the feature itself does
not reflect the relation with others. This paper deals with these problems by
intentionally selecting significant anchor points for contrastive learning. We
design a query-selected attention (QS-Attn) module, which compares feature
distances in the source domain, giving an attention matrix with a probability
distribution in each row. Then we select queries according to their measurement
of significance, computed from the distribution. The selected ones are regarded
as anchors for contrastive loss. At the same time, the reduced attention matrix
is employed to route features in both domains, so that source relations
maintain in the synthesis. We validate our proposed method in three different
I2I datasets, showing that it increases the image quality without adding
learnable parameters.


title: XCC: An X-ray FEL-based $\gamma\gamma$ Collider Higgs Factory

abstract:
  This report describes the design of a $\gamma\gamma$ Higgs factory in which
62.8 GeV electron beams collide with 1 keV X-ray free electron laser (XFEL)
beams to produce colliding beams of 62.5 GeV photons. The Higgs boson
production rate is 32,000 Higgs bosons per $10^7$ second year, roughly the same
as the ILC Higgs rate. The electron accelerator is based on cold copper
distributed coupling (C$^3$) accelerator technology. The 0.7 J pulse energy of
the XFEL represents a 300-fold increase over the pulse energy of current soft
x-ray FEL's. Design challenges are discussed, along with the R\&D to address
them, including demonstrators.


title: PointAttN: You Only Need Attention for Point Cloud Completion

abstract:
  Point cloud completion referring to completing 3D shapes from partial 3D
point clouds is a fundamental problem for 3D point cloud analysis tasks.
Benefiting from the development of deep neural networks, researches on point
cloud completion have made great progress in recent years. However, the
explicit local region partition like kNNs involved in existing methods makes
them sensitive to the density distribution of point clouds. Moreover, it serves
limited receptive fields that prevent capturing features from long-range
context information. To solve the problems, we leverage the cross-attention and
self-attention mechanisms to design novel neural network for processing point
cloud in a per-point manner to eliminate kNNs. Two essential blocks Geometric
Details Perception (GDP) and Self-Feature Augment (SFA) are proposed to
establish the short-range and long-range structural relationships directly
among points in a simple yet effective way via attention mechanism. Then based
on GDP and SFA, we construct a new framework with popular encoder-decoder
architecture for point cloud completion. The proposed framework, namely
PointAttN, is simple, neat and effective, which can precisely capture the
structural information of 3D shapes and predict complete point clouds with
highly detailed geometries. Experimental results demonstrate that our PointAttN
outperforms state-of-the-art methods by a large margin on popular benchmarks
like Completion3D and PCN. Code is available at:
https://github.com/ohhhyeahhh/PointAttN


title: Digital synchronization for continuous-variable quantum key distribution

abstract:
  Continuous variable quantum key distribution (CV-QKD) is a promising emerging
technology for the distribution of secure keys for symmetric encryption. It can
be readily implemented using commercial off-the-shelf optical
telecommunications components. A key requirement of the CV-QKD receiver is the
ability to measure the quantum states at the correct time instance and rate
using the correct orthogonal observables, referred to as synchronization. We
propose a digital synchronization procedure for a modern CV-QKD system with
locally generated local oscillator for coherent reception. Our proposed method
is modulation format independent allowing it to be used in a variety of CV-QKD
systems. We experimentally investigate its performance with a Gaussian
modulated CV-QKD system operating over a 10-20 km span of standard single mode
fibre. Since the procedure does not require hardware modifications it paves the
way for cost-effective QKD solutions that can adapt rapidly to changing
environmental conditions.


title:
Wavelet methods to study the pointwise regularity of the generalized
  Rosenblatt process

abstract:
  We prove that we can identify three types of pointwise behaviour in the
regularity of the (generalized) Rosenblatt process. This extends to a non
Gaussian setting previous results known for the (fractional) Brownian motion.
On this purpose, fine bounds on the increments of the Rosenblatt process are
needed. Our analysis is essentially based on various wavelet methods.


title: Pushing the limits of raw waveform speaker recognition

abstract:
  In recent years, speaker recognition systems based on raw waveform inputs
have received increasing attention. However, the performance of such systems
are typically inferior to the state-of-the-art handcrafted feature-based
counterparts, which demonstrate equal error rates under 1% on the popular
VoxCeleb1 test set. This paper proposes a novel speaker recognition model based
on raw waveform inputs. The model incorporates recent advances in machine
learning and speaker verification, including the Res2Net backbone module and
multi-layer feature aggregation. Our best model achieves an equal error rate of
0.89%, which is competitive with the state-of-the-art models based on
handcrafted features, and outperforms the best model based on raw waveform
inputs by a large margin. We also explore the application of the proposed model
in the context of self-supervised learning framework. Our self-supervised model
outperforms single phase-based existing works in this line of research.
Finally, we show that self-supervised pre-training is effective for the
semi-supervised scenario where we only have a small set of labelled training
data, along with a larger set of unlabelled examples.


title:
Bayesian Analysis of Formula One Race Results: Disentangling Driver
  Skill and Constructor Advantage

abstract:
  Successful performance in Formula One is determined by combination of both
the driver's skill and race-car constructor advantage. This makes key
performance questions in the sport difficult to answer. For example, who is the
best Formula One driver, which is the best constructor, and what is their
relative contribution to success? In this paper, we answer these questions
based on data from the hybrid era in Formula One (2014 - 2021 seasons). We
present a novel Bayesian multilevel Beta regression method to model individual
race success as the proportion of outperformed competitors. We show that our
modelling approach describes our data well, which allows for precise inferences
about driver skill and constructor advantage. We conclude that Hamilton and
Verstappen are the best drivers in the hybrid era, the top-three teams
(Mercedes, Ferrari, and Red Bull) clearly outperform other constructors, and
around 86% of the variance in race results is explained by the constructor. We
argue that this modeling approach may prove useful for sports beyond Formula
One, as it creates performance ratings for independent components contributing
to success.


title: Learning Audio Representations with MLPs

abstract:
  In this paper, we propose an efficient MLP-based approach for learning audio
representations, namely timestamp and scene-level audio embeddings. We use an
encoder consisting of sequentially stacked gated MLP blocks, which accept 2D
MFCCs as inputs. In addition, we also provide a simple temporal
interpolation-based algorithm for computing scene-level embeddings from
timestamp embeddings. The audio representations generated by our method are
evaluated across a diverse set of benchmarks at the Holistic Evaluation of
Audio Representations (HEAR) challenge, hosted at the NeurIPS 2021 competition
track. We achieved first place on the Speech Commands (full), Speech Commands
(5 hours), and the Mridingham Tonic benchmarks. Furthermore, our approach is
also the most resource-efficient among all the submitted methods, in terms of
both the number of model parameters and the time required to compute
embeddings.


title:
Deepchecks: A Library for Testing and Validating Machine Learning Models
  and Data

abstract:
  This paper presents Deepchecks, a Python library for comprehensively
validating machine learning models and data. Our goal is to provide an
easy-to-use library comprising of many checks related to various types of
issues, such as model predictive performance, data integrity, data distribution
mismatches, and more. The package is distributed under the GNU Affero General
Public License (AGPL) and relies on core libraries from the scientific Python
ecosystem: scikit-learn, PyTorch, NumPy, pandas, and SciPy. Source code,
documentation, examples, and an extensive user guide can be found at
\url{https://github.com/deepchecks/deepchecks} and
\url{https://docs.deepchecks.com/}.


title: Resilient Neural Forecasting Systems

abstract:
  Industrial machine learning systems face data challenges that are often
under-explored in the academic literature. Common data challenges are data
distribution shifts, missing values and anomalies. In this paper, we discuss
data challenges and solutions in the context of a Neural Forecasting
application on labor planning.We discuss how to make this forecasting system
resilient to these data challenges. We address changes in data distribution
with a periodic retraining scheme and discuss the critical importance of model
stability in this setting. Furthermore, we show how our deep learning model
deals with missing values natively without requiring imputation. Finally, we
describe how we detect anomalies in the input data and mitigate their effect
before they impact the forecasts. This results in a fully autonomous
forecasting system that compares favorably to a hybrid system consisting of the
algorithm and human overrides.


title:
$B\to D_1(2420)$ and $B\to D_1'(2430)$ form factors from QCD light-cone
  sum rules

abstract:
  We perform the first calculation of form factors in the semileptonic decays
$B\!\to\! D_1(2420)\ell\nu_\ell$ and $B \to D_1^\prime (2430)\ell \nu_\ell$
using QCD light-cone sum rules (LCSRs) with $B$-meson distribution amplitudes.
In this calculation the $c$-quark mass is finite. Analytical expressions for
two-particle contributions up to twist four are obtained. To disentangle the
$D_1$ and $D_1^\prime$ contributions in the LCSRs, we suggest a novel approach
that introduces a combination of two interpolating currents for these charmed
mesons. To fix all the parameters in the LCSRs, we use the two-point QCD sum
rules for the decay constants of $D_1$ and $D_1^\prime$ mesons augmented by a
single experimental input, that is the $B \to D_1(2420)\ell\nu_\ell$ decay
width. We provide numerical results for all $B\to D_1$ and $B\to D_1^\prime$
form factors. As a byproduct, we also obtain the $D_1$- and $D_1'$-meson decay
constants and predict the lepton-flavour universality ratios $R(D_1)$ and
$R(D_1')$.