title:
Software Engineers Response to Public Crisis: Lessons Learnt from
  Spontaneously Building an Informative COVID-19 Dashboard

abstract:
  The Coronavirus disease 2019 (COVID-19) outbreak quickly spread around the
world, resulting in over 240 million infections and 4 million deaths by Oct
2021. While the virus is spreading from person to person silently, fear has
also been spreading around the globe. The COVID-19 information from the
Australian Government is convincing but not timely or detailed, and there is
much information on social networks with both facts and rumors. As software
engineers, we have spontaneously and rapidly constructed a COVID-19 information
dashboard aggregating reliable information semi-automatically checked from
different sources for providing one-stop information sharing site about the
latest status in Australia. Inspired by the John Hopkins University COVID-19
Map, our dashboard contains the case statistics, case distribution, government
policy, latest news, with interactive visualization. In this paper, we present
a participant's in-person observations in which the authors acted as founders
of https://covid-19-au.com/ serving more than 830K users with 14M page views
since March 2020. According to our first-hand experience, we summarize 9
lessons for developers, researchers and instructors. These lessons may inspire
the development, research and teaching in software engineer aspects for coping
with similar public crises in the future.


title:
Rigorous analysis of the effects of electron-phonon interactions on
  magnetic properties in the one-electron Kondo lattice model

abstract:
  The Kondo lattice model (KLM) is a typical model describing heavy fermion
systems. In this paper, we consider the interaction of phonons with the system
described by the one-electron KLM. Magnetic properties of the ground state of
this model are revealed in a rigorous form. Furthermore, we derive the
effective Hamiltonian in the strong coupling limit ($J\to \infty$) for the
strength of the spin-exchange interaction $J$; we examine the magnetic
properties of the ground state of the effective Hamiltonian and prove that the
Aizenman--Lieb theorem concerning the magnetization holds for the effective
Hamiltonian at finite temperatures. Generalizing the obtained results, we
clarify a mechanism for the stability of magnetic properties of the ground
state in the one-electron KLM system.


title:
Auto-Icon+: An Automated End-to-End Code Generation Tool for Icon
  Designs in UI Development

abstract:
  Approximately 50% of development resources are devoted to UI development
tasks [9]. Occupying a large proportion of development resources, developing
icons can be a time-consuming task, because developers need to consider not
only effective implementation methods but also easy-to-understand descriptions.
In this paper, we present Auto-Icon+, an approach for automatically generating
readable and efficient code for icons from design artifacts. According to our
interviews to understand the gap between designers (icons are assembled from
multiple components) and developers (icons as single images), we apply a
heuristic clustering algorithm to compose the components into an icon image. We
then propose an approach based on a deep learning model and computer vision
methods to convert the composed icon image to fonts with descriptive labels,
thereby reducing the laborious manual effort for developers and facilitating UI
development. We quantitatively evaluate the quality of our method in the real
world UI development environment and demonstrate that our method offers
developers accurate, efficient, readable, and usable code for icon designs, in
terms of saving 65.2% implementing time.


title:
Finite amplitude method on the deformed relativistic Hartree-Bogoliubov
  theory in continuum: The isoscalar giant monopole resonance in exotic nuclei

abstract:
  Finite amplitude method based on the deformed relativistic Hartree-Bogoliubov
theory in continuum (DRHBc-FAM) is developed and applied to study isoscalar
giant monopole resonance in exotic nuclei. Validation of the numerical
implementation is examined for $^{208}\textrm{Pb}$. The isoscalar giant
monopole resonances for even-even calcium isotopes from $^{40}\textrm{Ca}$ to
the last bound neutron-rich nucleus $^{80}\textrm{Ca}$ are calculated, and a
good agreement with the available experimental centroid energies is obtained
for $^{40-48}\textrm{Ca}$. For the exotic calcium isotopes, e.g.,
$^{68}\textrm{Ca}$ and $^{80}\textrm{Ca}$, the DRHBc-FAM calculated results are
closer to the energy weighted sum rule than the calculations on the harmonic
oscillator basis, which highlights the advantages of DRHBc-FAM in describing
giant resonances for exotic nuclei. In order to explore the soft monopole mode
in the exotic nuclei, the giant monopole resonance for the deformed exotic
nucleus $^{200}\textrm{Nd}$ is investigated, where the prolate shape and the
oblate shape coexist. A soft monopole mode near 6.0 MeV is found in the prolate
case, and another one near 4.5 MeV is found in the oblate case. The transition
density of the soft monopole mode shows in phase or out-of-phase vibrations
near the surface region, which is generated by quadrupole vibrations.


title: Turning vector bundles

abstract:
  We define a turning of a rank-$2k$ vector bundle $E \to B$ to be a homotopy
of bundle automorphisms $\psi_t$ from $\mathbb{Id}_E$, the identity of $E$, to
$-\mathbb{Id}_E$, minus the identity, and call a pair $(E, \psi_t)$ a turned
bundle. We investigate when vector bundles admit turnings and develop the
theory of turnings and their obstructions. In particular, we determine which
rank-$2k$ bundles over the $2k$-sphere are turnable.
  If a bundle is turnable, then it is orientable. In the other direction,
complex bundles are turned bundles and for bundles over finite $CW$-complexes
with rank in the stable range, Bott's proof of his periodicity theorem shows
that a turning of $E$ defines a homotopy class of complex structure on $E$. On
the other hand, we give examples of rank-$2k$ bundles over $2k$-dimensional
spaces, including the tangent bundles of some $2k$-manifolds, which are
turnable but do not admit a complex structure. Hence turned bundles can be
viewed as a generalisation of complex bundles.
  We also generalise the definition of turning to other settings, including
other paths of automorphisms, and we relate the generalised turnability of
vector bundles to the low-dimensional topology of their gauge groups.


title:
On multi-soliton solutions to the Heisenberg ferromagnetic spin chain
  equation in (2+1)-dimensions

abstract:
  This paper concentrates on the Heisenberg ferromagnetic spin chain (HFSC)
equation in (2+1)-dimensions modelling nonlinear wave propagation in
ferromagnetic spin chain. A variable transformation is first employed to reduce
the studied equation. And then an associated matrix Riemann-Hilbert problem is
built on the real line through analyzing spectral problem of the reduced
equation. As a consequence, solving the obtained matrix Riemann-Hilbert problem
with the identity jump matrix, corresponding to the reflectionless, the general
multi-soliton solutions to the HFSC equation in (2+1)-dimensions are acquired.
Specially, the one- and two-soliton solutions are worked out and analyzed
graphically.


title:
Not All Tokens Are Equal: Human-centric Visual Analysis via Token
  Clustering Transformer

abstract:
  Vision transformers have achieved great successes in many computer vision
tasks. Most methods generate vision tokens by splitting an image into a regular
and fixed grid and treating each cell as a token. However, not all regions are
equally important in human-centric vision tasks, e.g., the human body needs a
fine representation with many tokens, while the image background can be modeled
by a few tokens. To address this problem, we propose a novel Vision
Transformer, called Token Clustering Transformer (TCFormer), which merges
tokens by progressive clustering, where the tokens can be merged from different
locations with flexible shapes and sizes. The tokens in TCFormer can not only
focus on important areas but also adjust the token shapes to fit the semantic
concept and adopt a fine resolution for regions containing critical details,
which is beneficial to capturing detailed information. Extensive experiments
show that TCFormer consistently outperforms its counterparts on different
challenging human-centric tasks and datasets, including whole-body pose
estimation on COCO-WholeBody and 3D human mesh reconstruction on 3DPW. Code is
available at https://github.com/zengwang430521/TCFormer.git


title:
A generalized echo squeezing protocol with near-Heisenberg limit
  sensitivity and strong robustness against excess noise and variation in
  squeezing parameter

abstract:
  We present a generalized echo squeezing protocol (GESP) as a generalization
of the Schr\"odinger cat state protocol (SCSP) with the value of the squeezing
parameter being an arbitrary number rather than pi/2. We show analytically that
over a broad range of the squeezing parameter the sensitivity reaches the
Heisenberg limit within a factor of root-2. For a large number of particles, N,
this plateau interval is almost the whole range from zero to pi/2, and the
sensitivity is independent of the parity of N. Therefore, it is possible to
operate a sensor over wide interval of the squeezing parameter without changing
the sensitivity. This is to be contrasted with the conventional echo squeezing
protocol (CESP) which only works for a very small interval of the squeezing
parameter. We also show that, in contrast to the CESP, the sensitivity of the
GESP is close to the quantum Cram\'er-Rao bound over the whole range of the
squeezing parameter, indicating that the phase shift information is
near-optimally extracted. We find that the enhancement in sensitivity in the
case of the GESP is due to a combination of two parameters: the phase
magnification (PMF) and the noise amplification factor (NAF). As the value of
the squeezing parameter increases, both PMF and NAF increase, while keeping the
ratio of PMF/NAF essentially constant, yielding a net enhancement of
sensitivity that at the Heisenberg limit within a factor of root-2 over the
whole plateau interval. An important consequence of this behavior is that the
robustness of the GESP against excess noise easily exceeds that of the CESP for
a broad range of values of the squeezing parameter. As such, in the context of
an experimental study, it should be possible to achieve a net enhancement in
sensitivity higher than that for the CESP, under typical conditions where the
excess noise exceeds the unsqueezed quantum projection noise.


title:
Investigation of a Data Split Strategy Involving the Time Axis in
  Adverse Event Prediction Using Machine Learning

abstract:
  Adverse events are a serious issue in drug development and many prediction
methods using machine learning have been developed. The random split
cross-validation is the de facto standard for model building and evaluation in
machine learning, but care should be taken in adverse event prediction because
this approach tends to be overoptimistic compared with the real-world
situation. The time split, which uses the time axis, is considered suitable for
real-world prediction. However, the differences in model performance obtained
using the time and random splits are not fully understood. To understand the
differences, we compared the model performance between the time and random
splits using eight types of compound information as input, eight adverse events
as targets, and six machine learning algorithms. The random split showed higher
area under the curve values than did the time split for six of eight targets.
The chemical spaces of the training and test datasets of the time split were
similar, suggesting that the concept of applicability domain is insufficient to
explain the differences derived from the splitting. The area under the curve
differences were smaller for the protein interaction than for the other
datasets. Subsequent detailed analyses suggested the danger of confounding in
the use of knowledge-based information in the time split. These findings
indicate the importance of understanding the differences between the time and
random splits in adverse event prediction and suggest that appropriate use of
the splitting strategies and interpretation of results are necessary for the
real-world prediction of adverse events.


title: Imbalanced Classification via a Tabular Translation GAN

abstract:
  When presented with a binary classification problem where the data exhibits
severe class imbalance, most standard predictive methods may fail to accurately
model the minority class. We present a model based on Generative Adversarial
Networks which uses additional regularization losses to map majority samples to
corresponding synthetic minority samples. This translation mechanism encourages
the synthesized samples to be close to the class boundary. Furthermore, we
explore a selection criterion to retain the most useful of the synthesized
samples. Experimental results using several downstream classifiers on a variety
of tabular class-imbalanced datasets show that the proposed method improves
average precision when compared to alternative re-weighting and oversampling
techniques.


title:
Particle Creation and Entanglement in Dispersive Model with Step
  Velocity Profile

abstract:
  We investigate particle creation and entanglement structure in a dispersive
model with subliminal dispersion relation. Assuming the step function spatial
velocity profile of the background flow, mode functions for a massless scalar
field is exactly obtained by the matching method. Power spectrums of created
particles are calculated for the subsonic and the transsonic flow cases. For
the transsonic case, the sonic horizon exists and created particles show the
Planckian distribution for low frequency region but the thermal property
disappears for high frequency region near the cutoff frequency introduced by
the non-linear dispersion. For the subsonic case, although the sonic horizon
does not exist, the effective group velocity horizon appears due to the
non-linear dispersion for high frequency region and approximate thermal
property of the power spectrum arises. Relation between particle creation and
entanglement between each mode is also discussed.


title:
Enhanced optical trapping assisted by resonant energy backflow in a
  perforated dielectric microsphere

abstract:
  Optical energy flow inside a dielectric microsphere exposed to an optical
wave is usually codirected with its wave vector. At the same time, if the
optical field in a microparticle is in resonance with a high-quality spatial
eigenmode, referred to as the whispering-gallery mode (WGM), at least two
regions of reverse energy flow emerge in the illuminated and shadow particle
hemispheres. These areas are of considerable practical interest due to their
enhanced optical trapping potential provided they should be previously cleared
from particle material. In this paper, we consider a perforated microsphere
with an air-filled pinhole fabricated along the particle diameter and
theoretically analyze the conditions for WGMs excitation. A pinhole isolates
the energy backflow regions of WGM and multiple enhances the optical pull-in
force that transforms a perforated microsphere into an efficient optical
tweezer for trapping various nanoobjects.


title: Audio-Visual Wake Word Spotting System For MISP Challenge 2021

abstract:
  This paper presents the details of our system designed for the Task 1 of
Multimodal Information Based Speech Processing (MISP) Challenge 2021. The
purpose of Task 1 is to leverage both audio and video information to improve
the environmental robustness of far-field wake word spotting. In the proposed
system, firstly, we take advantage of speech enhancement algorithms such as
beamforming and weighted prediction error (WPE) to address the multi-microphone
conversational audio. Secondly, several data augmentation techniques are
applied to simulate a more realistic far-field scenario. For the video
information, the provided region of interest (ROI) is used to obtain visual
representation. Then the multi-layer CNN is proposed to learn audio and visual
representations, and these representations are fed into our two-branch
attention-based network which can be employed for fusion, such as transformer
and conformed. The focal loss is used to fine-tune the model and improve the
performance significantly. Finally, multiple trained models are integrated by
casting vote to achieve our final 0.091 score.


title:
Many Episode Learning in a Modular Embodied Agent via End-to-End
  Interaction

abstract:
  In this work we give a case study of an embodied machine-learning (ML)
powered agent that improves itself via interactions with crowd-workers. The
agent consists of a set of modules, some of which are learned, and others
heuristic. While the agent is not "end-to-end" in the ML sense, end-to-end
interaction is a vital part of the agent's learning mechanism. We describe how
the design of the agent works together with the design of multiple annotation
interfaces to allow crowd-workers to assign credit to module errors from
end-to-end interactions, and to label data for individual modules. Over
multiple automated human-agent interaction, credit assignment, data annotation,
and model re-training and re-deployment, rounds we demonstrate agent
improvement.


title:
DecBERT: Enhancing the Language Understanding of BERT with Causal
  Attention Masks

abstract:
  Since 2017, the Transformer-based models play critical roles in various
downstream Natural Language Processing tasks. However, a common limitation of
the attention mechanism utilized in Transformer Encoder is that it cannot
automatically capture the information of word order, so explicit position
embeddings are generally required to be fed into the target model. In contrast,
Transformer Decoder with the causal attention masks is naturally sensitive to
the word order. In this work, we focus on improving the position encoding
ability of BERT with the causal attention masks. Furthermore, we propose a new
pre-trained language model DecBERT and evaluate it on the GLUE benchmark.
Experimental results show that (1) the causal attention mask is effective for
BERT on the language understanding tasks; (2) our DecBERT model without
position embeddings achieve comparable performance on the GLUE benchmark; and
(3) our modification accelerates the pre-training process and DecBERT w/ PE
achieves better overall performance than the baseline systems when pre-training
with the same amount of computational resources.


title:
Generating Authentic Adversarial Examples beyond Meaning-preserving with
  Doubly Round-trip Translation

abstract:
  Generating adversarial examples for Neural Machine Translation (NMT) with
single Round-Trip Translation (RTT) has achieved promising results by releasing
the meaning-preserving restriction. However, a potential pitfall for this
approach is that we cannot decide whether the generated examples are
adversarial to the target NMT model or the auxiliary backward one, as the
reconstruction error through the RTT can be related to either. To remedy this
problem, we propose a new criterion for NMT adversarial examples based on the
Doubly Round-Trip Translation (DRTT). Specifically, apart from the
source-target-source RTT, we also consider the target-source-target one, which
is utilized to pick out the authentic adversarial examples for the target NMT
model. Additionally, to enhance the robustness of the NMT model, we introduce
the masked language models to construct bilingual adversarial pairs based on
DRTT, which are used to train the NMT model directly. Extensive experiments on
both the clean and noisy test sets (including the artificial and natural noise)
show that our approach substantially improves the robustness of NMT models.


title: Independence Testing for Bounded Degree Bayesian Network

abstract:
  We study the following independence testing problem: given access to samples
from a distribution $P$ over $\{0,1\}^n$, decide whether $P$ is a product
distribution or whether it is $\varepsilon$-far in total variation distance
from any product distribution. For arbitrary distributions, this problem
requires $\exp(n)$ samples. We show in this work that if $P$ has a sparse
structure, then in fact only linearly many samples are required. Specifically,
if $P$ is Markov with respect to a Bayesian network whose underlying DAG has
in-degree bounded by $d$, then $\tilde{\Theta}(2^{d/2}\cdot n/\varepsilon^2)$
samples are necessary and sufficient for independence testing.

categories: cs.DS cs.DM cs.IT cs.LG math.IT math.ST stat.TH


title: A Thin Format Vision-Based Tactile Sensor with A Micro Lens Array (MLA)

abstract:
  Vision-based tactile sensors have been widely studied in the robotics field
for high spatial resolution and compatibility with machine learning algorithms.
However, the currently employed sensor's imaging system is bulky limiting its
further application. Here we present a micro lens array (MLA) based vison
system to achieve a low thickness format of the sensor package with high
tactile sensing performance. Multiple micromachined micro lens units cover the
whole elastic touching layer and provide a stitched clear tactile image,
enabling high spatial resolution with a thin thickness of 5 mm. The thermal
reflow and soft lithography method ensure the uniform spherical profile and
smooth surface of micro lens. Both optical and mechanical characterization
demonstrated the sensor's stable imaging and excellent tactile sensing,
enabling precise 3D tactile information, such as displacement mapping and force
distribution with an ultra compact-thin structure.


title: Time Domain Adversarial Voice Conversion for ADD 2022

abstract:
  In this paper, we describe our speech generation system for the first Audio
Deep Synthesis Detection Challenge (ADD 2022). Firstly, we build an any-to-many
voice conversion (VC) system to convert source speech with arbitrary language
content into the target speaker%u2019s fake speech. Then the converted speech
generated from VC is post-processed in the time domain to improve the deception
ability. The experimental results show that our system has adversarial ability
against anti-spoofing detectors with a little compromise in audio quality and
speaker similarity. This system ranks top in Track 3.1 in the ADD 2022, showing
that our method could also gain good generalization ability against different
detectors.


title:
A filtering monotonization approach for DG discretizations of hyperbolic
  problems

abstract:
  We introduce a filtering technique for Discontinuous Galerkin approximations
of hyperbolic problems. Following an approach already proposed for the
Hamilton-Jacobi equations by other authors, we aim at reducing the spurious
oscillations that arise in presence of discontinuities when high order spatial
discretizations are employed. This goal is achieved using a filter function
that keeps the high order scheme when the solution is regular and switches to a
monotone low order approximation if it is not. The method has been implemented
in the framework of the $deal.II$ numerical library, whose mesh adaptation
capabilities are also used to reduce the region in which the low order
approximation is used. A number of numerical experiments demonstrate the
potential of the proposed filtering technique.


title:
Linear-Quadratic Optimal Controls for Stochastic Volterra Integral
  Equations: Causal State Feedback and Path-Dependent Riccati Equations

abstract:
  A linear-quadratic optimal control problem for a forward stochastic Volterra
integral equation (FSVIE, for short) is considered. Under the usual convexity
conditions, open-loop optimal control exists, which can be characterized by the
optimality system, a coupled system of an FSVIE and a Type-II backward SVIE
(BSVIE, for short). To obtain a causal state feedback representation for the
open-loop optimal control, a path-dependent Riccati equation for an
operator-valued function is introduced, via which the optimality system can be
decoupled. In the process of decoupling, a Type-III BSVIE is introduced whose
adapted solution can be used to represent the adapted M-solution of the
corresponding Type-II BSVIE. Under certain conditions, it is proved that the
path-dependent Riccati equation admits a unique solution, which means that the
decoupling field for the optimality system is found. Therefore a causal state
feedback representation of the open-loop optimal control is constructed. An
additional interesting finding is that when the control only appears in the
diffusion term, not in the drift term of the state system, the causal state
feedback reduces to a Markovian state feedback.


title: Automated Application Processing

abstract:
  Recruitment in large organisations often involves interviewing a large number
of candidates. The process is resource intensive and complex. Therefore, it is
important to carry it out efficiently and effectively. Planning the selection
process consists of several problems, each of which maps to one or the other
well-known computing problem. Research that looks at each of these problems in
isolation is rich and mature. However, research that takes an integrated view
of the problem is not common. In this paper, we take two of the most important
aspects of the application processing problem, namely review/interview panel
creation and interview scheduling. We have implemented our approach as a
prototype system and have used it to automatically plan the interview process
of a real-life data set. Our system provides a distinctly better plan than the
existing practice, which is predominantly manual. We have explored various
algorithmic options and have customised them to solve these panel creation and
interview scheduling problems. We have evaluated these design options
experimentally on a real data set and have presented our observations. Our
prototype and experimental process and results may be a very good starting
point for a full-fledged development project for automating application
processing process.


title:
CTCNet: A CNN-Transformer Cooperation Network for Face Image
  Super-Resolution

abstract:
  Recently, deep convolution neural networks (CNNs) steered face
super-resolution methods have achieved great progress in restoring degraded
facial details by jointly training with facial priors. However, these methods
have some obvious limitations. On the one hand, multi-task joint learning
requires additional marking on the dataset, and the introduced prior network
will significantly increase the computational cost of the model. On the other
hand, the limited receptive field of CNN will reduce the fidelity and
naturalness of the reconstructed facial images, resulting in suboptimal
reconstructed images. In this work, we propose an efficient CNN-Transformer
Cooperation Network (CTCNet) for face super-resolution tasks, which uses the
multi-scale connected encoder-decoder architecture as the backbone.
Specifically, we first devise a novel Local-Global Feature Cooperation Module
(LGCM), which is composed of a Facial Structure Attention Unit (FSAU) and a
Transformer block, to promote the consistency of local facial detail and global
facial structure restoration simultaneously. Then, we design an efficient Local
Feature Refinement Module (LFRM) to enhance the local facial structure
information. Finally, to further improve the restoration of fine facial
details, we present a Multi-scale Feature Fusion Unit (MFFU) to adaptively fuse
the features from different stages in the encoder procedure. Comprehensive
evaluations on various datasets have assessed that the proposed CTCNet can
outperform other state-of-the-art methods significantly.


title:
A Multi-Opinion Based Metric for Quantifying Polarization on Social
  Networks: A Case Study from India

abstract:
  Social media has been known to be a hotbed of political and social
communications and analyzing the polarization of opinions has been gaining
attention. In this study, we have proposed a measure for quantifying
polarization on social networks. The proposed metric, unlike state-of-the-art
methods, does not assume a two-opinion scenario and applies to multiple
opinions. Our metric was tested on both binary opinion based benchmark networks
as well as synthetically opinion-labeled social networks with a multi-opinion
scenario and varying degrees of polarization. The metric showed promising
results for social networks with different levels of polarization. The method
was then employed to study polarization using data obtained from Twitter
concerning the tri-opinion ("pro", "anti" and "neutral") based communications
regarding the implementation of the Citizenship Amendment Act (CAA) in India.
We have measured the polarization on a variety of social networks such as
communication networks based on retweets or mentions, a social relationships
network based on follower-followee connections and finally hybrid networks by
combining the communication networks with the social relationships network. The
proposed method suggested a high level of polarization among the users with
respect to sharing posts on Twitter, thereby indicating the highly contentious
nature of the issue. We also obtained a high polarization score for a social
relationships network. Thus indicating the presence of homophily among users
i.e. opinion on CAA is in-line with their social relationships on the platform.
For a retweet based hybrid network, the scores returned by our polarization
metric highlighted opinion to be the key driver in retweeting behaviour
irrespective of the social relationships among users. On the contrary, the
mention based communications among the users were not polarized in nature.


title:
Efficient Generation of Tunable Magnetic and Optical Vortices Using
  Plasmas

abstract:
  Plasma is an attractive medium for generating strong microscopic magnetic
structures and tunable electromagnetic radiation with predictable topologies
due to its extraordinary ability to sustain and manipulate high currents and
strong fields. Here, using theory and simulations, we show efficient generation
of multi-megagauss magnetic and tunable optical vortices when a sharp
relativistic ionization front (IF) passes through a relatively long-wavelength
Laguerre-Gaussian (LG) laser pulse with orbital angular momentum (OAM). The
optical vortex is frequency upshifted within a wide spectral range simply by
changing the plasma density and compressed in duration. The topological charges
of both vortices can be manipulated by controlling the OAM mode of the incident
LG laser and/or by controlling the topology and density of the IF. For
relatively high (low) plasma densities, most energy of the incident LG laser
pulse is converted to the magnetic (optical) vortex.


title:
Weak tension accelerates hybridization and dehybridization of short
  oligonucleotides

abstract:
  The hybridization and dehybridization of DNA subject to tension is relevant
to fundamental genetic processes and to the design of DNA-based mechanobiology
assays. While strong tension accelerates DNA melting and decelerates DNA
annealing, the effects of tension weaker than 5 pN are less clear. In this
study, we developed a DNA bow assay, which uses the bending rigidity of
double-stranded DNA (dsDNA) to exert weak tension on a single-stranded DNA
(ssDNA) target in the range of 2 pN to 6 pN. Combining this assay with
single-molecule FRET, we measured the hybridization and dehybridization
kinetics between a 15 nt ssDNA under tension and a 8-9 nt oligo, and found that
both the hybridization and dehybridization rates monotonically increase with
tension for various nucleotide sequences tested. These findings suggest that
the nucleated duplex in its transition state is more extended than the pure
dsDNA or ssDNA counterpart. Our simulations using the coarse-grained oxDNA2
model indicate that the increased extension of the transition state is due to
exclusion interactions between unpaired ssDNA regions in close proximity to one
another. This study highlights an example where the ideal worm-like chain
models fail to explain the kinetic behavior of DNA in the low force regime.


title: Multi-shot Solution Prediction for Combinatorial Optimization

abstract:
  This paper aims to predict optimal solutions for combinatorial optimization
problems (COPs) via machine learning (ML). To find high-quality solutions
efficiently, existing methods use a ML model to predict the optimal solution
and use the ML prediction to guide the search. Prediction of the optimal
solution to sufficient accuracy is critical, however it is challenging due to
the high complexity of COPs. Nevertheless, these existing methods are
single-shot, i.e., predicting the optimal solution only for once. This paper
proposes a framework that enables a ML model to predict the optimal solution in
multiple shots, namely multi-shot solution prediction (MSSP), which can improve
the quality of a ML prediction by harnessing feedback from search.
Specifically, we employ a set of statistical measures as features, to extract
useful information from feasible solutions found by the search method and
inform the ML model as to which value a decision variable is likely to take in
high-quality solutions. Our experiments on three NP-hard COPs show that MSSP
improves the quality of a ML prediction substantially and achieves competitive
results as compared with other search methods in terms of solution quality.
Furthermore, we demonstrate that MSSP can be used as a pricing heuristic for
column generation, to boost a branch-and-price algorithm for solving the graph
coloring problem.


title:
Quantum bath engineering of a high impedance microwave mode through
  quasiparticle tunneling

abstract:
  We demonstrate a new approach to dissipation engineering in microwave quantum
optics. For a single mode, dissipation usually corresponds to quantum jumps,
where photons are lost one by one. Here, we are able to tune the minimal number
of lost photons per jump to be two (or more) with a simple dc voltage. As a
consequence, different quantum states experience different dissipation.
Causality implies that the states must also experience different energy shifts.
Our measurements of these Lamb shifts are in good agreement with the
predictions of the Kramers-Kronig relations for single quantum states in a
regime of highly non-linear bath coupling. This work opens new possibilities
for quantum state manipulation in circuit QED, without relying on the Josephson
effect.


title:
Software Engineering Approaches for TinyML based IoT Embedded Vision: A
  Systematic Literature Review

abstract:
  Internet of Things (IoT) has catapulted human ability to control our
environments through ubiquitous sensing, communication, computation, and
actuation. Over the past few years, IoT has joined forces with Machine Learning
(ML) to embed deep intelligence at the far edge. TinyML (Tiny Machine Learning)
has enabled the deployment of ML models for embedded vision on extremely lean
edge hardware, bringing the power of IoT and ML together. However, TinyML
powered embedded vision applications are still in a nascent stage, and they are
just starting to scale to widespread real-world IoT deployment. To harness the
true potential of IoT and ML, it is necessary to provide product developers
with robust, easy-to-use software engineering (SE) frameworks and best
practices that are customized for the unique challenges faced in TinyML
engineering. Through this systematic literature review, we aggregated the key
challenges reported by TinyML developers and identified state-of-art SE
approaches in large-scale Computer Vision, Machine Learning, and Embedded
Systems that can help address key challenges in TinyML based IoT embedded
vision. In summary, our study draws synergies between SE expertise that
embedded systems developers and ML developers have independently developed to
help address the unique challenges in the engineering of TinyML based IoT
embedded vision.


title: On Function Spaces Related to H-sober Spaces

abstract:
  In this paper, we mainly study the function spaces related to H-sober spaces.
For an irreducible subset system H and $T_{0}$ spaces $X$ and $Y$, it is proved
that $Y$ is H-sober iff the function space $\mathbb{C}(X, Y)$ of all continuous
functions $f : X\longrightarrow Y$ equipped with the topology of pointwise
convergence is H-sober iff the function space $\mathbb{C}(X, Y)$ equipped with
the Isbell topology is H-sober. One immediate corollary is that for a $T_{0}$
space $X$, $Y$ is a sober space (resp., $d$-space, well-filtered space) iff the
function space $\mathbb{C}(X, Y)$ equipped with the topology of pointwise
convergence is a sober space (resp., $d$-space, well-filtered space) iff the
function space $\mathbb{C}(X, Y)$ equipped with the the Isbell topology is a
sober space (resp., $d$-space, well-filtered space). It is shown that $T_{0}$
spaces $X$ and $Y$, if the function space $\mathbb{C}(X, Y)$ equipped with the
compact-open topology is H-sober, then $Y$ is H-sober. The function space
$\mathbb{C}(X, Y)$ equipped with the Scott topology is also discussed.


title:
Computational studies on electrochemical performances of doped and
  substituted $Ti_3C_2O_2$ MXene

abstract:
  Using Density functional theory (DFT) in conjunction with a solvation model
we have investigated the phenomenon of eletrode-electrolyte interaction at the
electrode surface and its consequences on the electrochemical properties like
the charge storage and total capacitance of doped and substituted oxygen
functionalised Ti$_{3}$C$_{2}$ supercapcitor electrode. We have studied
nitrogen doped, nitrogen substituted and molybdenum substituted Mxenes in
acidic electrolyte H$_{2}$SO$_{4}$ solution. By considering nitrogen doping at
different sites, we found that the greatest capacitance is obtained for doping
at functional sites. Our results agree well with the available experiment. We
also found that the enhancement in capacitances due to nitrogen doping is due
to amplifications in the pseudocapcitances. We propose that the primary
mechanism leading to the enhanced value of the capacitances due to nitrogen
doping is surface redox activity. The performances for substituted systems, on
the other hand, are degraded in comparison to the pristine ones. This suggests
that better storage capacities in Ti$_3$C$_{2}$O$_{2}$ electrode can be
obtained by doping only. We provide insights into the reasons behind
contrasting behaviour in doped and substituted systems and suggest ways to
further improve the capacitances in doped system.


title:
Comments on noncommutative quantum mechanical systems associated with
  Lie algebras

abstract:
  We consider quantum mechanics on the noncommutative spaces characterized by
the commutation relations $$ [x_a, x_b] \ =\ i\theta f_{abc} x_c\,, $$ where
$f_{abc}$ are the structure constants of a Lie algebra. We note that this
problem can be reformulated as an ordinary quantum problem in a commuting
momentum space. The coordinates are then represented as linear differential
operators $\hat x_a = -i\hat D_a = -iR_{ab} (p)\, \partial /\partial p_b $.
Generically, the matrix $R_{ab}(p)$ represents a certain infinite series over
the deformation parameter $\theta$: $R_{ab} = \delta_{ab} + \ldots$. The
deformed Hamiltonian, $\hat H = -\frac 12 \hat D_a^2\,,$ describes the motion
along the corresponding group manifolds with the characteristic size of order
$\theta^{-1}$. Their metrics are also expressed into certain infinite series in
$\theta$. For the algebras $su(2)$ and $u(2)$, it has been possible to
represent the operators $\hat x_a$ in a simple finite form. A byproduct of our
study are new nonstandard formulas for the metrics on $SU(2) \equiv S^3$ and on
$SO(3)$.


title:
Recycling End-of-life Polycarbonate in Steelmaking; $\textit{Ab Initio}$
  Study of Carbon Dissolution in Molten Iron

abstract:
  The scarcity of fossil fuels as carbon resources has motivated the
steelmaking industry to search for new carbon sources such as end-of-life
polymeric products. Using $\textit{ab initio}$ molecular dynamics simulation,
we demonstrate that 41% of polycarbonate's carbon content is readily dissolved
in molten iron's interface at $T$ = 1823 K which is comparable to graphite with
$\sim$ 58% carbon content dissolution. More importantly, we demonstrate that
polycarbonate's hydrogen content does not dissolve in molten iron but rather
escape in gaseous form. Therefore, waste polycarbonate constitutes a feasible
carbon source for steelmaking.


title:
Unsupervised Contrastive Hashing for Cross-Modal Retrieval in Remote
  Sensing

abstract:
  The development of cross-modal retrieval systems that can search and retrieve
semantically relevant data across different modalities based on a query in any
modality has attracted great attention in remote sensing (RS). In this paper,
we focus our attention on cross-modal text-image retrieval, where queries from
one modality (e.g., text) can be matched to archive entries from another (e.g.,
image). Most of the existing cross-modal text-image retrieval systems in RS
require a high number of labeled training samples and also do not allow fast
and memory-efficient retrieval. These issues limit the applicability of the
existing cross-modal retrieval systems for large-scale applications in RS. To
address this problem, in this paper we introduce a novel unsupervised
cross-modal contrastive hashing (DUCH) method for text-image retrieval in RS.
To this end, the proposed DUCH is made up of two main modules: 1) feature
extraction module, which extracts deep representations of two modalities; 2)
hashing module that learns to generate cross-modal binary hash codes from the
extracted representations. We introduce a novel multi-objective loss function
including: i) contrastive objectives that enable similarity preservation in
intra- and inter-modal similarities; ii) an adversarial objective that is
enforced across two modalities for cross-modal representation consistency; and
iii) binarization objectives for generating hash codes. Experimental results
show that the proposed DUCH outperforms state-of-the-art methods. Our code is
publicly available at https://git.tu-berlin.de/rsim/duch.


title:
Non-Linear Dimensionality Reduction with a Variational Autoencoder
  Decoder to Understand Convective Processes in Climate Models

abstract:
  Deep learning can accurately represent sub-grid-scale convective processes in
climate models, learning from high resolution simulations. However, deep
learning methods usually lack interpretability due to large internal
dimensionality, resulting in reduced trustworthiness in these methods. Here, we
use Variational AutoEncoder (VAE) decoder structures, a non-linear
dimensionality reduction technique, to learn and understand convective
processes in an aquaplanet superparameterized climate model simulation, where
deep convective processes are simulated explicitly. We show that similar to
previous deep learning studies based on feed-forward neural nets, the VAE is
capable of learning and accurately reproducing convective processes. In
contrast to past work, we show this can be achieved by compressing the original
information into only five latent nodes. As a result, the VAE can be used to
understand convective processes and delineate modes of convection through the
exploration of its latent dimensions. A close investigation of the latent space
enables the identification of different convective regimes: a) stable
conditions are clearly distinguished from deep convection with low outgoing
longwave radiation and strong precipitation; b) high optically thin cirrus-like
clouds are separated from low optically thick cumulus clouds; and c) shallow
convective processes are associated with large-scale moisture content and
surface diabatic heating. Our results demonstrate that VAEs can accurately
represent convective processes in climate models, while enabling
interpretability and better understanding of sub-grid-scale physical processes,
paving the way to increasingly interpretable machine learning parameterizations
with promising generative properties.


title: Two-level Quantum Walkers on Directed Graphs II: An Application to qRAM

abstract:
  This is the second paper in a series of two. Using a multi-particle
continuous-time quantum walk with two internal states, which has been
formulated in the first paper (arXiv:2112.08119), we physically implement a
quantum random access memory (qRAM). Data with address information are
dual-rail encoded into quantum walkers. The walkers pass through perfect binary
trees to access the designated memory cells and copy the data stored in the
cells. A roundabout gate allocated at each node serves as a router to move the
walker from the parent node to one of two child nodes, depending on the
internal state of the walker. In this process, the address information is
sequentially encoded into the internal states so that the walkers are
adequately delivered to the target cells. The present qRAM, which processes
$2^n$ $m$-qubit data, is implemented in a quantum circuit of depth
$O(n\log(n+m))$ and requires $O(n+m)$ qubit resources. This is more efficient
than the conventional bucket-brigade qRAM that requires $O(n^2+nm)$ steps and
$O(2^{n}+m)$ qubit resources for processing. Moreover, since the walkers are
not entangled with any device on the binary trees, the cost of maintaining
coherence could be reduced. Notably, by simply passing quantum walkers through
binary trees, data can be automatically extracted in a quantum superposition
state. In other words, any time-dependent control is not required.


title:
Gamma-ray burst data strongly favor the three-parameter fundamental
  plane (Dainotti) correlation relation over the two-parameter one

abstract:
  Gamma-ray bursts (GRBs), observed to redshift $z=9.4$, are potential probes
of the largely unexplored $z\sim 2.7-9.4$ part of the early Universe. Thus,
finding relevant relations among GRB physical properties is crucial. We find
that the Platinum GRB data compilation, with 50 long GRBs (with relatively flat
plateaus and no flares) in the redshift range $0.553 \leq z \leq 5.0$, and the
LGRB95 data compilation, with 95 long GRBs in $0.297 \leq z \leq 9.4$, as well
as the 145 GRB combination of the two, strongly favor the three-dimensional
(3D) fundamental plane (Dainotti) correlation relation (between the peak prompt
lumininosity, the luminosity at the end of the plateau emission, and its rest
frame duration) over the two-dimensional one (between the luminosity at the end
of the plateau emission and its duration). The 3D Dainotti correlations in the
three data sets are standardizable. We find that while LGRB95 data have
$\sim50$\% larger intrinsic scatter parameter values than the better-quality
Platinum data, they provide somewhat tighter constraints on cosmological-model
and GRB-correlation parameters, perhaps solely due to the larger number of data
points, 95 versus 50. This suggests that when compiling GRB data for the
purpose of constraining cosmological parameters, given the quality of current
GRB data, intrinsic scatter parameter reduction must be balanced against
reduced sample size.


title: Adaptive Conductance Control

abstract:
  Neuromodulation is central to the adaptation and robustness of animal nervous
systems. This paper explores the classical paradigm of indirect adaptive
control to design neuromodulatory controllers in conductance-based neuronal
models. The adaptive control of maximal conductance parameters is shown to
provide a methodology aligned with the central concepts of neuromodulation in
physiology and of impedance control in robotics.


title: Towards In Silico Mining for Superconductors -- Cutting the Gordian Knot

abstract:
  A random forest regression based supervised machine learning method to
predict experimental critical temperature of superconductivity from the
electronic band structure, as obtained from Density Functional Theory, is
demonstrated. This complementarity between experiment and theory draws
inspiration from the merging of Kohn-Sham and Bogoliubov-De Gennes equations
[W. Kohn, W, EKU Gross, and LN Oliveira, Int. J. of Quant. Chem., 36(23),
611-615 (1989)]. Features in the Kohn-Sham Density Functional Theory band
structure away from EF becoming decisive for the superconducting gap
demonstrates this divide-and-conquer physical understanding. Not committing to
any microscopic mechanism for the SC at this stage, it implies that in
different classes of materials, different electronic features are responsible
for the superconductivity. However, training on known members of a class, the
performance of new members may be predicted. The method is validated for the
A15 materials, including both binary A3X and ternary A6XY intermetallics, A=V,
Nb, demonstrating that the two do indeed belong to the same class of
superconductors.


title:
Emu: A Case Study for TDI-like Imaging for Infrared Observation from
  Space

abstract:
  A wide-field zenith-looking telescope operating in a mode similar to
Time-Delay-Integration (TDI) or drift scan imaging can perform an infrared sky
survey without active pointing control but it requires a high-speed, low-noise
infrared detector. Operating from a hosted payload platform on the
International Space Station (ISS), the Emu space telescope employs the
paradigm-changing properties of the Leonardo SAPHIRA electron avalanche
photodiode array to provide powerful new observations of cool stars at the
critical water absorption wavelength (1.4 $\mu$m) largely inaccessible to
ground-based telescopes due to the Earth's own atmosphere. Cool stars,
especially those of spectral-type M, are important probes across contemporary
astrophysics, from the formation history of the Galaxy to the formation of
rocky exoplanets. Main sequence M-dwarf stars are the most abundant stars in
the Galaxy and evolved M-giant stars are some of the most distant stars that
can be individually observed. The Emu sky survey will deliver critical stellar
properties of these cool stars by inferring oxygen abundances via measurement
of the water absorption band strength at 1.4 $\mu$m. Here we present the
TDI-like imaging capability of Emu mission, its science objectives, instrument
details and simulation results.


title: NAFSSR: Stereo Image Super-Resolution Using NAFNet

abstract:
  Stereo image super-resolution aims at enhancing the quality of
super-resolution results by utilizing the complementary information provided by
binocular systems. To obtain reasonable performance, most methods focus on
finely designing modules, loss functions, and etc. to exploit information from
another viewpoint. This has the side effect of increasing system complexity,
making it difficult for researchers to evaluate new ideas and compare methods.
This paper inherits a strong and simple image restoration model, NAFNet, for
single-view feature extraction and extends it by adding cross attention modules
to fuse features between views to adapt to binocular scenarios. The proposed
baseline for stereo image super-resolution is noted as NAFSSR. Furthermore,
training/testing strategies are proposed to fully exploit the performance of
NAFSSR. Extensive experiments demonstrate the effectiveness of our method. In
particular, NAFSSR outperforms the state-of-the-art methods on the KITTI 2012,
KITTI 2015, Middlebury, and Flickr1024 datasets. With NAFSSR, we won 1st place
in the NTIRE 2022 Stereo Image Super-resolution Challenge. Codes and models
will be released at https://github.com/megvii-research/NAFNet.


title:
$L^p$ boundedness of the Bergman Projection on generalizations of the
  Hartogs triangle in $\mathbb{C}^{n+1}$

abstract:
  In this paper, we investigate a class of domains $\Omega^{n+1}_\gamma
=\{(z,w)\in \mathbb{C}^n\times \mathbb{C}: |z|^\gamma < |w| < 1\}$ for
$\gamma>0$ that generalizes the Hartogs triangle. We obtain a sharp range of
$p$ for the boundedness of the Bergman projection on the domain considered
here. It generalizes the results by Edholm and McNeal \cite{LD1} for n = 1 to
any dimension n.


title:
Chiral perturbation theory for heavy hadrons and chiral effective field
  theory for heavy hadronic molecules

abstract:
  Chiral symmetry and its spontaneous breaking play an important role both in
the light hadron and heavy hadron systems. In this work, we shall review the
investigations on the chiral corrections to the properties of the heavy mesons
and baryons within the framework of the chiral perturbation theory ($\chi$PT).
We will also review the scatterings of the light pseudoscalar mesons and heavy
hadrons. Moreover, the modern nuclear force was built upon the chiral effective
field theory ($\chi$EFT). In the past decades many new hadron states were
observed experimentally. A large group of these states are near-threshold
resonances, such as the charged charmoniumlike $Z_c$ and $Z_{cs}$ states,
bottomoniumlike $Z_b$ states, hidden-charm pentaquark $P_c$ and $P_{cs}$ states
and the doubly charmed $T_{cc}$ state etc. They are very good candidates of the
loosely bound molecular states composed of a pair of charmed hadrons. The same
chiral dynamics not only governs the nuclei and forms the deuteron, but also
dictates the above shallow bound states or resonances. We will perform an
extensive review on the progress on the heavy hadronic molecular states within
the framework of $\chi$EFT.


title: Shape-Aware Monocular 3D Object Detection

abstract:
  The detection of 3D objects through a single perspective camera is a
challenging issue. The anchor-free and keypoint-based models receive increasing
attention recently due to their effectiveness and simplicity. However, most of
these methods are vulnerable to occluded and truncated objects. In this paper,
a single-stage monocular 3D object detection model is proposed. An
instance-segmentation head is integrated into the model training, which allows
the model to be aware of the visible shape of a target object. The detection
largely avoids interference from irrelevant regions surrounding the target
objects. In addition, we also reveal that the popular IoU-based evaluation
metrics, which were originally designed for evaluating stereo or LiDAR-based
detection methods, are insensitive to the improvement of monocular 3D object
detection algorithms. A novel evaluation metric, namely average depth
similarity (ADS) is proposed for the monocular 3D object detection models. Our
method outperforms the baseline on both the popular and the proposed evaluation
metrics while maintaining real-time efficiency.


title:
Formation of the SDC13 Hub-Filament System: A Cloud-Cloud Collision
  Imprinted on The Multiscale Magnetic Field

abstract:
  Hub-filament systems (HFSs) are potential sites of protocluster and massive
star formation, and play a key role in mass accumulation. We report JCMT POL-2
850 $\mu$m polarization observations toward the massive HFS SDC13. We detect an
organized magnetic field near the hub center with a cloud-scale "U-shape"
morphology following the western edge of the hub. Together with larger-scale
APEX 13CO and PLANCK polarization data, we find that SDC13 is located at the
convergent point of three giant molecular clouds (GMCs) along a large-scale,
partially spiral-like magnetic field. The smaller "U-shape" magnetic field is
perpendicular to the large-scale magnetic field and the converging GMCs. We
explain this as the result of a cloud-cloud collision. Within SDC13, we find
that local gravity and velocity gradients point toward filament ridges and hub
center. This suggests that gas can locally be pulled onto filaments and overall
converges to the hub center. A virial analysis of the central hub shows that
gravity dominates magnetic and kinematic energy. Combining large- and
small-scale analyses, we propose that SDC13 is initially formed from a
collision of clouds moving along the large-scale magnetic field. In the
post-shock regions, after the initial turbulent energy has dissipated, gravity
takes over and starts to drive the gas accretion along the filaments toward the
hub center.


title: On the Rational Bredon Cohomology of Equivariant Configuration Spaces

abstract:
  Bredon cohomology is a cohomology theory that applies to topological spaces
equipped with the group actions. For any group G, given a real linear
representation V , the configuration space of V has a natural diagonal
G-action. In the paper we study this group action on the configuration space
and give a decomposition of the homology Bredon coefficient system of the
configuration space and apply this to compute rational Bredon cohomology of the
configuration space for small nonabelian group G.


title: Audio Deep Fake Detection System with Neural Stitching for ADD 2022

abstract:
  This paper describes our best system and methodology for ADD 2022: The First
Audio Deep Synthesis Detection Challenge\cite{Yi2022ADD}. The very same system
was used for both two rounds of evaluation in Track 3.2 with a similar training
methodology. The first round of Track 3.2 data is generated from
Text-to-Speech(TTS) or voice conversion (VC) algorithms, while the second round
of data consists of generated fake audio from other participants in Track 3.1,
aiming to spoof our systems. Our systems use a standard 34-layer ResNet, with
multi-head attention pooling \cite{india2019self} to learn the discriminative
embedding for fake audio and spoof detection. We further utilize neural
stitching to boost the model's generalization capability in order to perform
equally well in different tasks, and more details will be explained in the
following sessions. The experiments show that our proposed method outperforms
all other systems with a 10.1% equal error rate(EER) in Track 3.2.


title: Multimodal Token Fusion for Vision Transformers

abstract:
  Many adaptations of transformers have emerged to address the single-modal
vision tasks, where self-attention modules are stacked to handle input sources
like images. Intuitively, feeding multiple modalities of data to vision
transformers could improve the performance, yet the inner-modal attentive
weights may also be diluted, which could thus undermine the final performance.
In this paper, we propose a multimodal token fusion method (TokenFusion),
tailored for transformer-based vision tasks. To effectively fuse multiple
modalities, TokenFusion dynamically detects uninformative tokens and
substitutes these tokens with projected and aggregated inter-modal features.
Residual positional alignment is also adopted to enable explicit utilization of
the inter-modal alignments after fusion. The design of TokenFusion allows the
transformer to learn correlations among multimodal features, while the
single-modal transformer architecture remains largely intact. Extensive
experiments are conducted on a variety of homogeneous and heterogeneous
modalities and demonstrate that TokenFusion surpasses state-of-the-art methods
in three typical vision tasks: multimodal image-to-image translation, RGB-depth
semantic segmentation, and 3D object detection with point cloud and images.


title: Quantum State Transfer on Neighborhood Corona of Two Graphs

abstract:
  Given two graphs $G_{1}$ of order $n_{1}$ and $G_{2}$, the neighborhood
corona of $G_{1}$ and $G_{2}$, denoted by $G_{1}\bigstar G_{2}$, is the graph
obtained by taking one copy of $G_{1}$ and taking $n_{1}$ copies of $G_{2}$, in
the meanwhile, linking all the neighbors of the $i$-th vertex of $G_{1}$ with
all vertices of the $i$-th copy of $G_{2}$. In our work, we give some
conditions that $G_{1}\bigstar G_{2}$ is not periodic. Furthermore, we
demonstrate some sufficient conditions for $G_{1}\bigstar G_{2}$ having no
perfect state transfer. Some examples are provided to explain our results. In
addition, for the reason that the graph admitting perfect state transfer is
rare, we also consider pretty good state transfer on neighborhood corona of two
graphs. We show some sufficient conditions for $G_{1}\bigstar G_{2}$ admitting
pretty good state transfer.


title: Data Collection by an Informed Seller

abstract:
  A seller faces a consumer with an uncertain value for the product. The seller
has imperfect private information about the value and requests additional data
to set the price. The consumer can decline any request. The consumer's
willingness to provide data depends on his belief about the seller's type which
in turn depends on the request. We show that the type uncertainty limits the
scope of data collection: All equilibrium payoffs are spanned by fully pooling
equilibria in which the seller collects the same data regardless of the type.
The seller's private information lowers efficiency and profits, but benefits
the consumer by fueling his skepticism and preventing excessive data
collection. Having less private information may enable the seller to collect
more data directly from the consumer and may lower the overall consumer
welfare.


title:
Scaling relations of z~0.25-1.5 galaxies in various environments from
  the morpho-kinematic analysis of the MAGIC sample

abstract:
  The evolution of galaxies is influenced by many physical processes which may
vary depending on their environment. We combine Hubble Space Telescope (HST)
and Multi-Unit Spectroscopic Explorer (MUSE) data of galaxies at 0.25<z<1.5 to
probe the impact of environment on the size-mass relation, the Main Sequence
(MS) and the Tully-Fisher relation (TFR).
  We perform a morpho-kinematic modelling of 593 [Oii] emitters in various
environments in the COSMOS area from the MUSE-gAlaxy Groups In Cosmos (MAGIC)
survey. The HST F814W images are modelled with a bulge-disk decomposition to
estimate their bulge-disk ratio, effective radius and disk inclination. We use
the [Oii]{\lambda}{\lambda}3727, 3729 doublet to extract the ionised gas
kinematic maps from the MUSE cubes, and we model them for a sample of 146 [Oii]
emitters, with bulge and disk components constrained from morphology and a dark
matter halo.
  We find an offset of 0.03 dex on the size-mass relation zero point between
the field and the large structure subsamples, with a richness threshold of N=10
to separate between small and large structures, and of 0.06 dex with N=20.
Similarly, we find a 0.1 dex difference on the MS with N=10 and 0.15 dex with
N=20. These results suggest that galaxies in massive structures are smaller by
14% and have star formation rates reduced by a factor of 1.3-1.5 with respect
to field galaxies at z=0.7. Finally, we do not find any impact of the
environment on the TFR, except when using N=20 with an offset of 0.04 dex. We
discard the effect of quenching for the largest structures that would lead to
an offset in the opposite direction. We find that, at z=0.7, if quenching
impacts the mass budget of galaxies in structures, these galaxies would have
been affected quite recently, for roughly 0.7-1.5 Gyr. This result holds when
including the gas mass, but vanishes once we include the asymmetric drift
correction.


title: Mitigating laser imprint with a foam overcoating

abstract:
  Foam has been suggested to reduce laser imprint because of its low density.
In this paper, the two-dimensional radiation hydrodynamic code FLASH is applied
to investigate and characterize the strength of laser imprint through analyzing
areal density perturbation. There are two important factors for the mitigation
of laser imprint besides the thermal smoothing of the conduction region
(between the ablation front and the critical density surface) and the mass
ablation of the ablation front. First, radiation ablation dynamically modulates
density distribution not only to increase the frequency of the perturbed
ablation front oscillation but also to decrease the amplitude of oscillation.
Second, a larger length of the shocked compression region reduces the amplitude
of the perturbed shock front oscillation. The smaller the perturbation of both
ablation front and shock front, the smaller the areal density perturbation.
Based on the above physical mechanisms, the optimal way of mitigating laser
imprint with foam is that the dynamically modulated density distribution
further reduces the amplitude of perturbation reaching the solid CH when the
areal density perturbation of foam oscillates to the first minimum value. The
optimal ranges of foam parameters to mitigate laser imprint are proposed with
the aid of dimensional analysis: the foam thickness is about 2~3 times the
perturbation wavelength, and the foam density is about 1/2~3/2 times the mass
density corresponding to the critical density.


title: Jacobian Ensembles Improve Robustness Trade-offs to Adversarial Attacks

abstract:
  Deep neural networks have become an integral part of our software
infrastructure and are being deployed in many widely-used and safety-critical
applications. However, their integration into many systems also brings with it
the vulnerability to test time attacks in the form of Universal Adversarial
Perturbations (UAPs). UAPs are a class of perturbations that when applied to
any input causes model misclassification. Although there is an ongoing effort
to defend models against these adversarial attacks, it is often difficult to
reconcile the trade-offs in model accuracy and robustness to adversarial
attacks. Jacobian regularization has been shown to improve the robustness of
models against UAPs, whilst model ensembles have been widely adopted to improve
both predictive performance and model robustness. In this work, we propose a
novel approach, Jacobian Ensembles-a combination of Jacobian regularization and
model ensembles to significantly increase the robustness against UAPs whilst
maintaining or improving model accuracy. Our results show that Jacobian
Ensembles achieves previously unseen levels of accuracy and robustness, greatly
improving over previous methods that tend to skew towards only either accuracy
or robustness.


title: Euclid: Discovering pair-instability supernovae with the Deep Survey

abstract:
  Pair-instability supernovae are theorized supernovae that have not yet been
observationally confirmed. They are predicted to exist in low-metallicity
environments. Because overall metallicity becomes lower at higher redshifts,
deep near-infrared transient surveys probing high-redshift supernovae are
suitable to discover pair-instability supernovae. The Euclid satellite, which
is planned to be launched in 2023, has a near-infrared wide-field instrument
that is suitable for a high-redshift supernova survey. Although no dedicated
supernova survey is currently planned during the Euclid's 6 year primary
mission, the Euclid Deep Survey is planned to make regular observations of
three Euclid Deep Fields (40 deg2 in total) spanning six years. While the
observations of the Euclid Deep Fields are not frequent, we show that the
predicted long duration of pair-instability supernovae would allow us to search
for high-redshift pair-instability supernovae with the Euclid Deep Survey.
Based on the current observational plan of the Euclid mission, we conduct
survey simulations in order to estimate the expected numbers of
pair-instability supernova discoveries. We find that up to several hundred
pair-instability supernovae at z < ~ 3.5 can be discovered by the Euclid Deep
Survey. We also show that pair-instability supernova candidates can be
efficiently identified by their duration and color that can be determined with
the current Euclid Deep Survey plan. We conclude that the Euclid mission can
lead to the first confident discovery of pair-instability supernovae if their
event rates are as high as those predicted by recent theoretical studies. We
also update the expected numbers of superluminous supernova discoveries in the
Euclid Deep Survey based on the latest observational plan.

categories: astro-ph.HE astro-ph.CO astro-ph.GA astro-ph.SR


title:
Towards Brin's conjecture on frame flow ergodicity: new progress and
  perspectives

abstract:
  We report on some recent progress achieved in [arXiv:2111.14811] on the
ergodicity of the frame flow of negatively-curved Riemannian manifolds. We
explain the new ideas leading to ergodicity for nearly $0.25$-pinched manifolds
and give perspectives for future work.


title:
Roots of a characteristic equation with complex coefficients associated
  with differential-difference equations

abstract:
  We analyse placement of roots of a characteristic exponential polynomial with
complex coefficients associated with a first order differential-difference
equation. We provide necessary and sufficient conditions for all the roots to
be in the complex open left half-plane assuring stability of the
differential-difference equation. The conditions are expressed explicitly in
terms of complex coefficients of the characteristic exponential polynomial,
what makes them easy to use in applications.


title: A Stackelberg game for incentive-based demand response in energy markets

abstract:
  In modern buildings renewable energy generators and storage devices are
spreading, and consequently the role of the users in the power grid is shifting
from passive to active. We design a demand response scheme that exploits the
prosumers' flexibility to provide ancillary services to the main grid. We
propose a hierarchical scheme to coordinate the interactions between the
distribution system operator and a community of smart prosumers. The framework
inherits characteristics from price-based and incentive-based schemes and it
retains the advantages of both. We cast the problem as a Stackelberg game with
the prosumers as followers and the distribution system operator as leader. We
solve the resulting bilevel optimization program via a KKT reformulation,
proving the existence and the convergence to a local Stackelberg equilibrium.
Finally, we provide numerical simulations to corroborate our claims on the
benefits of the proposed framework.


title: Protostellar and Protoplanetary Disk Masses in the Serpens Region

abstract:
  We present the results from an Atacama Large Millimeter/Submillimeter Array
(ALMA) 1.3 mm continuum and $^{12}$CO ($J=2-1$) line survey spread over 10
square degrees in the Serpens star-forming region of 320 young stellar objects,
302 of which are likely members of Serpens (16 Class I, 35 Flat spectrum, 235
Class II, and 16 Class III). From the continuum data, we derive disk dust
masses and show that they systematically decline from Class I to Flat spectrum
to Class II sources. Grouped by stellar evolutionary state, the disk mass
distributions are similar to other young ($<3$ Myr) regions, indicating that
the large scale environment of a star-forming region does not strongly affect
its overall disk dust mass properties. These comparisons between populations
reinforce previous conclusions that disks in the Ophiuchus star-forming region
have anomalously low masses at all evolutionary stages. Additionally, we find a
single deeply embedded protostar that has not been documented elsewhere in the
literature and, from the CO line data, 15 protostellar outflows which we
catalog here.


title:
A lensing multi-messenger channel: Combining LIGO-Virgo-Kagra lensed
  gravitational-wave measurements with Euclid observations

abstract:
  Despite forecasts of upcoming detections, it remains unclear how feasible the
full spectrum of applications of strongly-lensed gravitational-wave
observations are. One application may be in the multi-messenger domain:
localisation of strongly lensed black hole mergers inside their associated lens
systems in gravitational-wave (GW) and electromagnetic (EM) bands. This is
intriguing because combining the observations of electromagnetically detected
lens systems (via optical or infrared imaging) with their associated
strongly-lensed gravitational waves could yield a wide array of valuable,
otherwise inaccessible information. One might, for instance, independently
verify strong lensing candidates, localise black holes to sub-arcsecond
precision, study the binary-galaxy connection, and more accurately test
gravitational-wave polarisation and propagation.
  Thus, here we perform a comprehensive simulation of the projected
capabilities of LIGO-Virgo-Kagra, combined with Euclid, to find strongly lensed
gravitational-wave events and then pinpoint the location of the black-hole
merger. We find that for 20-50% of the detected events, the lens is detectable
with Euclid-like imaging. For a 1-5 deg^2 sky localisation, with Euclid-like
images of host candidates, we expect to identify the EM-counterpart for
34.6-21.9% of lensed GWs when four events are detected. For three and two
events, this drops to 29.8-14.9% and 16.4-6.6% respectively.
  A dedicated follow-up of detected lensed events with space-based instruments
in the EM and continued upgrades in the current and planned GW detectors, as
well as lensed sub-threshold search pipelines are likely crucial to the success
of this program.


title:
Probabilistic guarantees on the objective value for the scenario
  approach via sensitivity analysis

abstract:
  This paper is concerned with objective value performance of the scenario
approach for robust convex optimization. A novel method is proposed to derive
probabilistic bounds for the objective value from scenario programs with a
finite number of samples. This method relies on a max-min reformulation and the
concept of complexity of robust optimization problems. With additional
continuity and regularity conditions, via sensitivity analysis, we also provide
explicit bounds which outperform an existing result in the literature. To
illustrate the improvements of our results, we also provide a numerical
example.


title: Muffin: Testing Deep Learning Libraries via Neural Architecture Fuzzing

abstract:
  Deep learning (DL) techniques are proven effective in many challenging tasks,
and become widely-adopted in practice. However, previous work has shown that DL
libraries, the basis of building and executing DL models, contain bugs and can
cause severe consequences. Unfortunately, existing testing approaches still
cannot comprehensively exercise DL libraries. They utilize existing trained
models and only detect bugs in model inference phase. In this work we propose
Muffin to address these issues. To this end, Muffin applies a
specifically-designed model fuzzing approach, which allows it to generate
diverse DL models to explore the target library, instead of relying only on
existing trained models. Muffin makes differential testing feasible in the
model training phase by tailoring a set of metrics to measure the
inconsistencies between different DL libraries. In this way, Muffin can best
exercise the library code to detect more bugs. To evaluate the effectiveness of
Muffin, we conduct experiments on three widely-used DL libraries. The results
demonstrate that Muffin can detect 39 new bugs in the latest release versions
of popular DL libraries, including Tensorflow, CNTK, and Theano.


title:
Neural Collapse Inspired Attraction-Repulsion-Balanced Loss for
  Imbalanced Learning

abstract:
  Class imbalance distribution widely exists in real-world engineering.
However, the mainstream optimization algorithms that seek to minimize error
will trap the deep learning model in sub-optimums when facing extreme class
imbalance. It seriously harms the classification precision, especially on the
minor classes. The essential reason is that the gradients of the classifier
weights are imbalanced among the components from different classes. In this
paper, we propose Attraction-Repulsion-Balanced Loss (ARB-Loss) to balance the
different components of the gradients. We perform experiments on the
large-scale classification and segmentation datasets and our ARB-Loss can
achieve state-of-the-art performance via only one-stage training instead of
2-stage learning like nowadays SOTA works.


title:
Planning problem for continuous-time finite state mean field game with
  compact action space

abstract:
  The planning problem for the mean field game implies the one tries to
transfer the system of infinitely many identical rational agents from the given
distribution to the final one using the choice of the terminal payoff. It can
be formulated as the mean field game system with the boundary condition only on
the measure variable. In the paper, we consider the continuous-time finite
state mean field game assuming that the space of actions for each player is
compact. It is shown that the planning problem in this case may not admit a
solution even if the final distribution is reachable from the initial one.
Further, we introduce the concept of generalized solution of the planning
problem for the finite state mean field game based on the minimization of
regret of the representative player. This minimal regret solution always
exists. Additionally, the set of minimal regret solution is the closure of the
set of classical solution of the planning problem provided that the latter is
nonempty.


title: Glass transition as a topological phase transition

abstract:
  The glass transition is considered as a phase transition in the system of
topologically protected excitations in matter structure. The critical behavior
of the system is considered both in statics and dynamics cases. It is shown in
the simple model describing the topological defects system in the elastic
medium with non-zero shear modulus, most of characteristic thermodynamic and
kinetic properties of glass transition are reproduced: the
Vogel--Fulcher--Tammann law; the behavior of susceptibility, and non-linear
susceptibilities; heat capacity behavior; and boson peak near the glass
transition temperature.


title: On locally finite dimensional traces

abstract:
  We resolve three open questions on approximation properties of traces on
\C-algebras. We answer two questions raised by Nate Brown by showing that
locally finite dimensional (LFD) traces form a convex set and that they are
automatically uniformly LFD on locally reflexive \C-algebras. We prove that all
the traces on the reduced \C-algebra $C^*_r(\Gamma)$ of a discrete amenable ICC
group $\Gamma$ are uniformly LFD, and conclude that $C^*_r(\Gamma)$ is
strong-NF in the sense of Blackadar-Kirchberg in this case. This partially
answers another open question raised by Brown.


title:
Characterization of timing and spacial resolution of novel TI-LGAD
  structures before and after irradiation

abstract:
  The characterization of spacial and timing resolution of the novel Trench
Isolated LGAD (TI-LGAD) technology is presented. This technology has been
developed at FBK with the goal of achieving 4D pixels, where an accurate
position resolution is combined in a single device with the precise timing
determination for Minimum Ionizing Particles (MIPs). In the TI-LGAD technology,
the pixelated LGAD pads are separated by physical trenches etched in the
silicon. This technology can reduce the interpixel dead area, mitigating the
fill factor problem. The TI-RD50 production studied in this work is the first
one of pixelated TI-LGADs. The characterization was performed using a scanning
TCT setup with an infrared laser and a $^{90}$Sr source setup.


title:
A tutorial for computer scientists on finite extensive games with
  perfect information

abstract:
  We provide a self-contained introduction to finite extensive games with
perfect information. In these games players proceed in turns having, at each
stage, finitely many moves to their disposal, each play always ends, and in
each play the players have complete knowledge of the previously made moves.
Almost all discussed results are well-known, but often they are not presented
in an optimal form. Also, they usually appear in the literature aimed at
economists or mathematicians, so the algorithmic or logical aspects are
underrepresented.


title: Influence of Repetition through Limited Recall

abstract:
  Decision makers who receive many signals are subject to imperfect recall.
This is especially important when learning from feeds that aggregate messages
from many senders on social media platforms. In this paper, we study a stylized
model of learning from feeds and highlight the inefficiencies that arise due to
imperfect recall. In our model, failure to recall a specific message comes from
the accumulation of messages which creates interference. We characterize the
influence of each sender according to the rate at which she sends messages and
to the strength of interference. Our analysis indicates that imperfect recall
not only leads to double-counting and extreme opinions in finite populations,
but also impedes the ability of the receiver to learn the true state as the
population of the senders increases. We estimate the strength of interference
in an online experiment where participants are exposed to (non-informative)
repeated messages and they need to estimate the opinion of others. Results show
that interference plays a significant role and is weaker among participants who
disagree with each other. Our work has implication for the diffusion of
information in networks, especially when it is false because it is shared and
repeated more than true information.


title: CoFHEE: A Co-processor for Fully Homomorphic Encryption Execution

abstract:
  The migration of computation to the cloud has raised privacy concerns as
sensitive data becomes vulnerable to attacks since they need to be decrypted
for processing. Fully Homomorphic Encryption (FHE) mitigates this issue as it
enables meaningful computations to be performed directly on encrypted data.
Nevertheless, FHE is orders of magnitude slower than unencrypted computation,
which hinders its practicality and adoption. Therefore, improving FHE
performance is essential for its real world deployment. In this paper, we
present a year-long effort to design, implement, fabricate, and post-silicon
validate a hardware accelerator for Fully Homomorphic Encryption dubbed CoFHEE.
With a design area of $12mm^2$, CoFHEE aims to improve performance of
ciphertext multiplications, the most demanding arithmetic FHE operation, by
accelerating several primitive operations on polynomials, such as polynomial
additions and subtractions, Hadamard product, and Number Theoretic Transform.
CoFHEE supports polynomial degrees of up to $n = 2^{14}$ with a maximum
coefficient sizes of 128 bits, while it is capable of performing ciphertext
multiplications entirely on chip for $n \leq 2^{13}$. CoFHEE is fabricated in
55nm CMOS technology and achieves 250 MHz with our custom-built low-power
digital PLL design. In addition, our chip includes two communication interfaces
to the host machine: UART and SPI. This manuscript presents all steps and
design techniques in the ASIC development process, ranging from RTL design to
fabrication and validation. We evaluate our chip with performance and power
experiments and compare it against state-of-the-art software implementations
and other ASIC designs. Developed RTL files are available in an open-source
repository.


title:
On the Use of Causal Graphical Models for Designing Experiments in the
  Automotive Domain

abstract:
  Randomized field experiments are the gold standard for evaluating the impact
of software changes on customers. In the online domain, randomization has been
the main tool to ensure exchangeability. However, due to the different
deployment conditions and the high dependence on the surrounding environment,
designing experiments for automotive software needs to consider a higher number
of restricted variables to ensure conditional exchangeability. In this paper,
we show how at Volvo Cars we utilize causal graphical models to design
experiments and explicitly communicate the assumptions of experiments. These
graphical models are used to further assess the experiment validity, compute
direct and indirect causal effects, and reason on the transportability of the
causal conclusions.


title: Proposal-free Lidar Panoptic Segmentation with Pillar-level Affinity

abstract:
  We propose a simple yet effective proposal-free architecture for lidar
panoptic segmentation. We jointly optimize both semantic segmentation and
class-agnostic instance classification in a single network using a pillar-based
bird's-eye view representation. The instance classification head learns
pairwise affinity between pillars to determine whether the pillars belong to
the same instance or not. We further propose a local clustering algorithm to
propagate instance ids by merging semantic segmentation and affinity
predictions. Our experiments on nuScenes dataset show that our approach
outperforms previous proposal-free methods and is comparable to proposal-based
methods which requires extra annotation from object detection.


title:
Augmentation of Atmospheric Turbulence Effects on Thermal Adapted Object
  Detection Models

abstract:
  Atmospheric turbulence has a degrading effect on the image quality of
long-range observation systems. As a result of various elements such as
temperature, wind velocity, humidity, etc., turbulence is characterized by
random fluctuations in the refractive index of the atmosphere. It is a
phenomenon that may occur in various imaging spectra such as the visible or the
infrared bands. In this paper, we analyze the effects of atmospheric turbulence
on object detection performance in thermal imagery. We use a geometric
turbulence model to simulate turbulence effects on a medium-scale thermal image
set, namely "FLIR ADAS v2". We apply thermal domain adaptation to
state-of-the-art object detectors and propose a data augmentation strategy to
increase the performance of object detectors which utilizes turbulent images in
different severity levels as training data. Our results show that the proposed
data augmentation strategy yields an increase in performance for both turbulent
and non-turbulent thermal test images.


title:
A Bi-level assessment of Twitter in predicting the results of an
  election: Delhi Assembly Elections 2020

abstract:
  Elections are the backbone of any democratic country, where voters elect the
candidates as their representatives. The emergence of social networking sites
has provided a platform for political parties and their candidates to connect
with voters in order to spread their political ideas. Our study aims to use
Twitter in assessing the outcome of Delhi Assembly elections held in 2020,
using a bi-level approach, i.e., concerning political parties and their
candidates. We analyze the correlation of election results with the activities
of different candidates and parties on Twitter, and the response of voters on
them, especially the mentions and sentiment of voters towards a party. The
Twitter profiles of the candidates are compared both at the party level as well
as the candidate level to evaluate their association with the outcome of the
election. We observe that the number of followers and the replies to the tweets
of candidates are good indicators for predicting actual election outcome.
However, we observe that the number of tweets mentioning a party and the
sentiment of voters towards the party shown in tweets are not aligned with the
election result. We also use machine learning models on various features such
as linguistic, word embeddings and moral dimensions for predicting the election
result (win or lose). The random forest model using tweet features provides
promising results for predicting if the tweet belongs to a winning or losing
candidate.


title:
Multi-View Spatial-Temporal Network for Continuous Sign Language
  Recognition

abstract:
  Sign language is a beautiful visual language and is also the primary language
used by speaking and hearing-impaired people. However, sign language has many
complex expressions, which are difficult for the public to understand and
master. Sign language recognition algorithms will significantly facilitate
communication between hearing-impaired people and normal people. Traditional
continuous sign language recognition often uses a sequence learning method
based on Convolutional Neural Network (CNN) and Long Short-Term Memory Network
(LSTM). These methods can only learn spatial and temporal features separately,
which cannot learn the complex spatial-temporal features of sign language. LSTM
is also difficult to learn long-term dependencies. To alleviate these problems,
this paper proposes a multi-view spatial-temporal continuous sign language
recognition network. The network consists of three parts. The first part is a
Multi-View Spatial-Temporal Feature Extractor Network (MSTN), which can
directly extract the spatial-temporal features of RGB and skeleton data; the
second is a sign language encoder network based on Transformer, which can learn
long-term dependencies; the third is a Connectionist Temporal Classification
(CTC) decoder network, which is used to predict the whole meaning of the
continuous sign language. Our algorithm is tested on two public sign language
datasets SLR-100 and PHOENIX-Weather 2014T (RWTH). As a result, our method
achieves excellent performance on both datasets. The word error rate on the
SLR-100 dataset is 1.9%, and the word error rate on the RWTHPHOENIX-Weather
dataset is 22.8%.


title:
Effects of Spin-Exchange Collisions on the Fluctuation Spectra of Hot
  Alkali Vapors

abstract:
  High-density alkali vapors in the spin-exchange-relaxation free (SERF) regime
are used in the highest-sensitivity optically-pumped magnetometers, atomic
gyroscopes, and searches for physics beyond the standard model, and have been
used to produce record numbers of entangled particles. Their power derives from
a counter-intuitive effect (the SERF effect) in which spin-exchange collisions
-- a decoherence mechanism at low density -- act to preserve coherence at high
density. Here we present a first-principles analysis of the noise spectra of
alkali vapors in and out of the SERF regime, and predict additional
non-intuitive features, with potential to further improve the sensitivity of
SERF media, and which must be taken into account in their use in quantum
optical applications. Studying the process of spin-noise spectroscopy (SNS), we
derive analytic formulae for the observable noise spectra, and for the
correlation functions among different hyperfine components, which give
additional insight into the spin dynamics. The analytic results indicate a
variety of distortions of the spin-noise spectrum relative to simpler models,
including a broad spectral background that mimics optical shot noise,
interference of noise contributions from the two ground state hyperfine levels,
noise reduction at the spin-precession frequency, and "hiding" of spin-noise
power that can introduce a systematic error in noise-based calibrations, e.g.
for spin-squeezing experiments.


title:
Unravelling the Nature of the Spin Excitations Disentangled from the
  Charge Contributions in a Doped Cuprate Superconductor

abstract:
  The nature of the spin excitations in superconducting cuprates is a key
question toward a unified understanding of the cuprate physics from long-range
antiferromagnetism to superconductivity. The intense spin excitations up to the
over-doped regime revealed by resonant inelastic X-ray scattering bring new
insights as well as problems -- like understanding their persistence or their
relation to the collective excitations in ordered magnets (magnons). Here, we
study the evolution of the spin excitations upon hole-doping the
superconducting cuprate Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$ by disentangling the
spin from the charge excitations in the experimental cross section. We compare
our experimental results against density matrix renormalization group
calculations for a $t$-$J$-like model on a square lattice. Our results
unambiguously confirm the persistence of the spin excitations, which are
closely connected to the persistence of short-range magnetic correlations up to
high doping. This suggests that the spin excitations in hole-doped cuprates are
related to magnons -- albeit short-ranged.


title:
Near K-Edge Photoionization and Photoabsorption of Singly, Doubly, and
  Triply Charged Silicon Ions

abstract:
  Experimental and theoretical results are presented for double, triple, and
quadruple photoionization of Si$^+$ and Si$^{2+}$ ions and for double
photoionization of Si$^{3+}$ ions by a single photon. The experiments employed
the photon-ion merged-beams technique at a synchrotron light source. The
experimental photon-energy range 1835--1900 eV comprises resonances associated
with the excitation of a $1s$ electron to higher subshells and subsequent
autoionization. Energies, widths, and strengths of these resonances are
extracted from high-resolution photoionization measurements, and the core-hole
lifetime of K-shell ionized neutral silicon is inferred. In addition,
theoretical cross sections for photoabsorption and multiple photoionization
were obtained from large-scale Multi-Configuration Dirac-Hartree-Fock (MCDHF)
calculations. The present calculations agree with the experiment much better
than previously published theoretical results. The importance of an accurate
energy calibration of laboratory data is pointed out. The present benchmark
results are particularly useful for discriminating between silicon absorption
in the gaseous and in the solid component (dust grains) of the interstellar
medium.


title: Islands in Generalized Dilaton Theories

abstract:
  In this work we systematically study the island formula in the general
asymptotically flat eternal black holes in generalized dilaton gravity theories
or in higher dimensional spherical black holes. Under some reasonable and mild
assumptions we prove that the island always appears barely outside of the
horizon in the late time of Hawking radiation so that the information paradox
is resolved. In particular, we find proper island in Liouville black hole which
solves the puzzle of arXiv:2105.03271.


title:
Expected $L_2-$discrepancy bound for a class of new stratified sampling
  models

abstract:
  We introduce a class of convex equivolume partitions. Expected
$L_2-$discrepancy are discussed under these partitions. There are two main
results. First, under this kind of partitions, we generate random point sets
with smaller expected $L_2-$discrepancy than classical jittered sampling for
the same sampling number. Second, an explicit expected $L_2-$discrepancy upper
bound under this kind of partitions is also given. Further, among these new
partitions, there is optimal expected $L_2-$discrepancy upper bound.


title: Table-based Fact Verification with Self-adaptive Mixture of Experts

abstract:
  The table-based fact verification task has recently gained widespread
attention and yet remains to be a very challenging problem. It inherently
requires informative reasoning over natural language together with different
numerical and logical reasoning on tables (e.g., count, superlative,
comparative). Considering that, we exploit mixture-of-experts and present in
this paper a new method: Self-adaptive Mixture-of-Experts Network (SaMoE).
Specifically, we have developed a mixture-of-experts neural network to
recognize and execute different types of reasoning -- the network is composed
of multiple experts, each handling a specific part of the semantics for
reasoning, whereas a management module is applied to decide the contribution of
each expert network to the verification result. A self-adaptive method is
developed to teach the management module combining results of different experts
more efficiently without external knowledge. The experimental results
illustrate that our framework achieves 85.1% accuracy on the benchmark dataset
TabFact, comparable with the previous state-of-the-art models. We hope our
framework can serve as a new baseline for table-based verification. Our code is
available at https://github.com/THUMLP/SaMoE.


title: An Efficient Algorithm for the Proximity Connected Two Center Problem

abstract:
  Given a set $P$ of $n$ points in the plane, the $k$-center problem is to find
$k$ congruent disks of minimum possible radius such that their union covers all
the points in $P$. The $2$-center problem is a special case of the $k$-center
problem that has been extensively studied in the recent past \cite{CAHN,HT,SH}.
In this paper, we consider a generalized version of the $2$-center problem
called \textit{proximity connected} $2$-center (PCTC) problem. In this problem,
we are also given a parameter $\delta\geq 0$ and we have the additional
constraint that the distance between the centers of the disks should be at most
$\delta$. Note that when $\delta=0$, the PCTC problem is reduced to the
$1$-center(minimum enclosing disk) problem and when $\delta$ tends to infinity,
it is reduced to the $2$-center problem. The PCTC problem first appeared in the
context of wireless networks in 1992 \cite{ACN0}, but obtaining a nontrivial
deterministic algorithm for the problem remained open. In this paper, we
resolve this open problem by providing a deterministic $O(n^2\log n)$ time
algorithm for the problem.


title: Dynamic Point Cloud Denoising via Gradient Fields

abstract:
  3D dynamic point clouds provide a discrete representation of real-world
objects or scenes in motion, which have been widely applied in immersive
telepresence, autonomous driving, surveillance, etc. However, point clouds
acquired from sensors are usually perturbed by noise, which affects downstream
tasks such as surface reconstruction and analysis. Although many efforts have
been made for static point cloud denoising, dynamic point cloud denoising
remains under-explored. In this paper, we propose a novel gradient-field-based
dynamic point cloud denoising method, exploiting the temporal correspondence
via the estimation of gradient fields -- a fundamental problem in dynamic point
cloud processing and analysis. The gradient field is the gradient of the
log-probability function of the noisy point cloud, based on which we perform
gradient ascent so as to converge each point to the underlying clean surface.
We estimate the gradient of each surface patch and exploit the temporal
correspondence, where the temporally corresponding patches are searched
leveraging on rigid motion in classical mechanics. In particular, we treat each
patch as a rigid object, which moves in the gradient field of an adjacent frame
via force until reaching a balanced state, i.e., when the sum of gradients over
the patch reaches 0. Since the gradient would be smaller when the point is
closer to the underlying surface, the balanced patch would fit the underlying
surface well, thus leading to the temporal correspondence. Finally, the
position of each point in the patch is updated along the direction of the
gradient averaged from corresponding patches in adjacent frames. Experimental
results demonstrate that the proposed model outperforms state-of-the-art
methods under both synthetic noise and simulated real-world noise.