title:
Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge
  Graph Completion

abstract:
  Multimodal Knowledge Graphs (MKGs), which organize visual-text factual
knowledge, have recently been successfully applied to tasks such as information
retrieval, question answering, and recommendation system. Since most MKGs are
far from complete, extensive knowledge graph completion studies have been
proposed focusing on the multimodal entity, relation extraction and link
prediction. However, different tasks and modalities require changes to the
model architecture, and not all images/objects are relevant to text input,
which hinders the applicability to diverse real-world scenarios. In this paper,
we propose a hybrid transformer with multi-level fusion to address those
issues. Specifically, we leverage a hybrid transformer architecture with
unified input-output for diverse multimodal knowledge graph completion tasks.
Moreover, we propose multi-level fusion, which integrates visual and text
representation via coarse-grained prefix-guided interaction and fine-grained
correlation-aware fusion modules. We conduct extensive experiments to validate
that our MKGformer can obtain SOTA performance on four datasets of multimodal
link prediction, multimodal RE, and multimodal NER. Code is available in
https://github.com/zjunlp/MKGformer.


title:
A variational approach for linearly dependent moving bases in quantum
  dynamics: application to Gaussian functions

abstract:
  In this paper, we present a variational treatment of the linear dependence
for a non-orthogonal time-dependent basis set in solving the Schr\"odinger
equation. The method is based on: i) the definition of a linearly independent
working space, and ii) a variational construction of the propagator over finite
time-steps. The second point allows the method to properly account for changes
in the dimensionality of the working space along the time evolution. In
particular, the time evolution is represented by a semi-unitary transformation.
Tests are done on a quartic double-well potential with Gaussian basis function
whose centers evolve according to classical equations of motion. We show that
the resulting dynamics converges to the exact one and is unitary by
construction.


title:
FedSPLIT: One-Shot Federated Recommendation System Based on Non-negative
  Joint Matrix Factorization and Knowledge Distillation

abstract:
  Non-negative matrix factorization (NMF) with missing-value completion is a
well-known effective Collaborative Filtering (CF) method used to provide
personalized user recommendations. However, traditional CF relies on the
privacy-invasive collection of users' explicit and implicit feedback to build a
central recommender model. One-shot federated learning has recently emerged as
a method to mitigate the privacy problem while addressing the traditional
communication bottleneck of federated learning. In this paper, we present the
first unsupervised one-shot federated CF implementation, named FedSPLIT, based
on NMF joint factorization. In our solution, the clients first apply local CF
in-parallel to build distinct client-specific recommenders. Then, the
privacy-preserving local item patterns and biases from each client are shared
with the processor to perform joint factorization in order to extract the
global item patterns. Extracted patterns are then aggregated to each client to
build the local models via knowledge distillation. In our experiments, we
demonstrate the feasibility of our approach with standard recommendation
datasets. FedSPLIT can obtain similar results than the state of the art (and
even outperform it in certain situations) with a substantial decrease in the
number of communications.


title: GitRank: A Framework to Rank GitHub Repositories

abstract:
  Open-source repositories provide wealth of information and are increasingly
being used to build artificial intelligence (AI) based systems to solve
problems in software engineering. Open-source repositories could be of varying
quality levels, and bad-quality repositories could degrade performance of these
systems. Evaluating quality of open-source repositories, which is not available
directly on code hosting sites such as GitHub, is thus important. In this
hackathon, we utilize known code quality measures and GrimoireLab toolkit to
implement a framework, named GitRank, to rank open-source repositories on three
different criteria. We discuss our findings and preliminary evaluation in this
hackathon report.


title:
ShoeRinsics: Shoeprint Prediction for Forensics with Intrinsic
  Decomposition

abstract:
  Shoe tread impressions are one of the most common types of evidence left at
crime scenes. However, the utility of such evidence is limited by the lack of
databases of footwear impression patterns that cover the huge and growing
number of distinct shoe models. We propose to address this gap by leveraging
shoe tread photographs collected by online retailers. The core challenge is to
predict the impression pattern from the shoe photograph since ground-truth
impressions or 3D shapes of tread patterns are not available. We develop a
model that performs intrinsic image decomposition (predicting depth, normal,
albedo, and lighting) from a single tread photo. Our approach, which we term
ShoeRinsics, combines domain adaptation and re-rendering losses in order to
leverage a mix of fully supervised synthetic data and unsupervised retail image
data. To validate model performance, we also collected a set of paired
shoe-sole images and corresponding prints, and define a benchmarking protocol
to quantify the accuracy of predicted impressions. On this benchmark,
ShoeRinsics outperforms existing methods for depth prediction and
synthetic-to-real domain adaptation.


title: The Category of Hypergroups as a Hyper (quasi)Abelian Category

abstract:
  In this paper we study categorical properties of the category of (abelian)
hypergroups that leads to the notion of hyper-quasipreadditive and
hyper-quasiabelian categories. Our goal is to create a path towards a general
theory of homological algebra for hyperalgebras. This is a first attempt to
achieve this goal. We hope to improve the definitions and results, and provide
more examples soon.


title:
Inverse Tritium Beta Decay with Relic Neutrinos, Solar Neutrinos, and a
  51Cr Source

abstract:
  The inverse tritium beta decay (ITBD) reaction, $\nu_e + ^3$H $\to e^- +
^3$He, is a promising experimental tool for observing relic neutrinos created
in the early Universe. This reaction has been selected by the PTOLEMY
experiment for the search of relic neutrinos. Despite its potential, the ITBD
reaction induced by any sources of neutrinos has yet to be observed. We show
that an intense $^{51}$Cr radioactive neutrino source is suitable for observing
the ITBD reaction for the first time. As the Sun is another source of intense
electron neutrinos, we also examine the ITBD reaction rate from solar
neutrinos. Based on our recent studies on the evolution of the helicity of
relic neutrinos, we further present the ITBD rate for capturing relic neutrinos
as a function of neutrino mass hierarchy, the Dirac versus Majorana nature of
neutrino, and the mass of the lightest neutrino.


title:
KenSwQuAD -- A Question Answering Dataset for Swahili Low Resource
  Language

abstract:
  This research developed a Kencorpus Swahili Question Answering Dataset
KenSwQuAD from raw data of Swahili language, which is a low resource language
predominantly spoken in Eastern African and also has speakers in other parts of
the world. Question Answering datasets are important for machine comprehension
of natural language processing tasks such as internet search and dialog
systems. However, before such machine learning systems can perform these tasks,
they need training data such as the gold standard Question Answering (QA) set
that is developed in this research. The research engaged annotators to
formulate question answer pairs from Swahili texts that had been collected by
the Kencorpus project, a Kenyan languages corpus that collected data from three
Kenyan languages. The total Swahili data collection had 2,585 texts, out of
which we annotated 1,445 story texts with at least 5 QA pairs each, resulting
into a final dataset of 7,526 QA pairs. A quality assurance set of 12.5% of the
annotated texts was subjected to re-evaluation by different annotators who
confirmed that the QA pairs were all correctly annotated. A proof of concept on
applying the set to machine learning on the question answering task confirmed
that the dataset can be used for such practical tasks. The research therefore
developed KenSwQuAD, a question-answer dataset for Swahili that is useful to
the natural language processing community who need training and gold standard
sets for their machine learning applications. The research also contributed to
the resourcing of the Swahili language which is important for communication
around the globe. Updating this set and providing similar sets for other low
resource languages is an important research area that is worthy of further
research.


title:
Evaluation of the relative performance of the subflattenings method for
  phylogenetic inference

abstract:
  The algebraic properties of flattenings and subflattenings provide direct
methods for identifying edges in the true phylogeny -- and by extension the
complete tree -- using pattern counts from a sequence alignment. The relatively
small number of possible internal edges among a set of taxa (compared to the
number of binary trees) makes these methods attractive, however more could be
done to evaluate their effectiveness for inferring phylogenetic trees. This is
the case particularly for subflattenings, and our work makes progress in this
area. We introduce software for constructing and evaluating subflattenings for
splits, utilising a number of methods to make computing subflattenings more
tractable. We then present the results of simulations we have performed in
order to compare the effectiveness of subflattenings to that of flattenings in
terms of split score distributions, and susceptibility to possible biases. We
find that subflattenings perform similarly to flattenings in terms of the
distribution of split scores on the trees we examined, but may be less affected
by bias arising from both split size/balance and long branch attraction. These
insights are useful for developing effective algorithms to utilise these tools
for the purpose of inferring phylogenetic trees.


title: Topological strings and Wilson loops

abstract:
  We propose the refined topological string correspondence to the expectation
values of half-BPS Wilson loop operators in 5d $\mathcal{N}=1$ gauge theory
partition function on the Omega-deformed background
$\mathbb{R}^4_{\epsilon_{1,2}}\times S^1$. We provide the refined topological
vertex method and the refined holomorphic anomaly equation method in the
topological string theory, from which we have exact computations on the 5d
Wilson loops partition functions in both A- and B-models. Finally, with the
exact results we have in B-model, we recover the quantum periods of local
$\mathbb{P}^1\times\mathbb{P}^1$ model and local $\mathbb{P}^2$ model in the
study of quantum geometry and we further give a refined generalization of
A-period.


title:
Time-multiplexed Neural Holography: A flexible framework for holographic
  near-eye displays with fast heavily-quantized spatial light modulators

abstract:
  Holographic near-eye displays offer unprecedented capabilities for virtual
and augmented reality systems, including perceptually important focus cues.
Although artificial intelligence--driven algorithms for computer-generated
holography (CGH) have recently made much progress in improving the image
quality and synthesis efficiency of holograms, these algorithms are not
directly applicable to emerging phase-only spatial light modulators (SLM) that
are extremely fast but offer phase control with very limited precision. The
speed of these SLMs offers time multiplexing capabilities, essentially enabling
partially-coherent holographic display modes. Here we report advances in
camera-calibrated wave propagation models for these types of holographic
near-eye displays and we develop a CGH framework that robustly optimizes the
heavily quantized phase patterns of fast SLMs. Our framework is flexible in
supporting runtime supervision with different types of content, including 2D
and 2.5D RGBD images, 3D focal stacks, and 4D light fields. Using our
framework, we demonstrate state-of-the-art results for all of these scenarios
in simulation and experiment.

categories: cs.GR cs.AI physics.comp-ph physics.optics


title:
Peeling of linearly elastic sheets using complex fluids at low Reynolds
  numbers

abstract:
  We investigate the transient, fluid structure interaction (FSI) of a
non-Newtonian fluid peeling two linearly elastic sheets at low Reynolds
numbers. Two different non-Newtonian fluids are considered; a simplified sPTT
model, and an inelastic fluid with shear thinning viscosity (generalized
Newtonian fluid). In the limit of small gap between the sheets, we invoke a
lubrication approximation and numerically solve for the gap height between the
two sheets during the start-up of a pressure-controlled flow. What we observe
is that for an impulse pressure applied to the sheet inlet, the peeling front
moves diffusively toward the end of the sheet when the fluid is Newtonian.
However, when one examines a complex fluid with shear thinning, the propagation
front moves sub-diffusively in time , but ultimately reaches the end faster due
to an order of magnitude larger pre-factor for the propagation speed. We
provide scaling analyses and similarity solutions to delineate several regimes
of peeling based on the sheet elasticity, Wi number (for sPTT fluid), and shear
thinning exponent (for generalized Newtonian fluid). To conclude, this study
aims to afford to the experimentalist a system of knowledge to delineate the
peeling characteristics of a certain class of complex fluids.


title:
When gold is not enough: platinum standard of quantum chemistry with
  $N^7$ cost

abstract:
  In this paper we extend the rank-reduced coupled-cluster formalism to the
calculation of non-iterative energy corrections due to quadruple excitations.
There are two major components of the proposed formalism. The first is an
approximate compression of the quadruple excitation amplitudes using the Tucker
format. The second is a modified functional used for evaluation of the
corrections which gives exactly the same results for the exact amplitudes, but
is less susceptible to errors resulting from the aforementioned compression. We
show, both theoretically and numerically, that the computational cost of the
proposed method scales as the seventh power of the system size. Using reference
results for a set of small molecules, the method is calibrated to deliver
relative accuracy of a few percent in energy corrections. To illustrate the
potential of the theory we calculate the isomerization energy of
\emph{ortho}/\emph{meta} benzyne (C$_6$H$_4$) and the barrier height for the
Cope rearrangement in bullvalene (C$_{10}$H$_{10}$). The method retains a
near-black-box nature of the conventional coupled-cluster formalism and depends
on only one additional parameter that controls the accuracy.


title:
PREME: Preference-based Meeting Exploration through an Interactive
  Questionnaire

abstract:
  The recent increase in the volume of online meetings necessitates automated
tools for managing and organizing the material, especially when an attendee has
missed the discussion and needs assistance in quickly exploring it. In this
work, we propose a novel end-to-end framework for generating interactive
questionnaires for preference-based meeting exploration. As a result, users are
supplied with a list of suggested questions reflecting their preferences. Since
the task is new, we introduce an automatic evaluation strategy. Namely, it
measures how much the generated questions via questionnaire are answerable to
ensure factual correctness and covers the source meeting for the depth of
possible exploration.


title:
A Bayesian Detect to Track System for Robust Visual Object Tracking and
  Semi-Supervised Model Learning

abstract:
  Object tracking is one of the fundamental problems in visual recognition
tasks and has achieved significant improvements in recent years. The
achievements often come with the price of enormous hardware consumption and
expensive labor effort for consecutive labeling. A missing ingredient for
robust tracking is achieving performance with minimal modification on network
structure and semi-supervised learning intermittent labeled frames. In this
paper, we ad-dress these problems in a Bayesian tracking and detection
framework parameterized by neural network outputs. In our framework, the
tracking and detection process is formulated in a probabilistic way as
multi-objects dynamics and network detection uncertainties. With our
formulation, we propose a particle filter-based approximate sampling algorithm
for tracking object state estimation. Based on our particle filter inference
algorithm, a semi-supervised learn-ing algorithm is utilized for learning
tracking network on intermittent labeled frames by variational inference. In
our experiments, we provide both mAP and probability-based detection
measurements for comparison between our algorithm with non-Bayesian solutions.
We also train a semi-supervised tracking network on M2Cai16-Tool-Locations
Dataset and compare our results with supervised learning on fully labeled
frames.


title: Legendre-Teege Reciprocity

abstract:
  Legendre published the first attempted proof of the law of Quadratic
Reciprocity. But, in its final form (1797), it had a gap. Some 125 years later
Herman Teege published the first rigorous proof of the unproven hypothesis
which formed that gap. Then, 48 years later Kenneth Rogers published a second
(but implicit) proof. These proofs lifted Legendre's attempt to the list of
complete proofs. No detailed exposition of these proofs appears in the
literature. Our paper fills that gap.


title:
Overlooked Work and Heat of Intervention and the Fate of Information
  Principles of Szilard and Landauer

abstract:
  We show that any external intervention (insertion or removal of a partition)
that destroys the equilibrium or brings it in a system always requires work and
heat to ensure that the first law is obeyed, a fact that has been completely
overlooked in the literature. As a consequence, there is no second law
violation. We discuss the ramifications of our finding for information
principles of Szilard and Landauer and show that no information entropy is
needed. The relevance of this result for Maxwell's demon is also considered.


title: The composition complexity of majority

abstract:
  We study the complexity of computing majority as a composition of local
functions: \[ \text{Maj}_n = h(g_1,\ldots,g_m), \] where each $g_j :\{0,1\}^{n}
\to \{0,1\}$ is an arbitrary function that queries only $k \ll n$ variables and
$h : \{0,1\}^m \to \{0,1\}$ is an arbitrary combining function. We prove an
optimal lower bound of \[ m \ge \Omega\left( \frac{n}{k} \log k \right) \] on
the number of functions needed, which is a factor $\Omega(\log k)$ larger than
the ideal $m = n/k$. We call this factor the composition overhead; previously,
no superconstant lower bounds on it were known for majority.
  Our lower bound recovers, as a corollary and via an entirely different proof,
the best known lower bound for bounded-width branching programs for majority
(Alon and Maass '86, Babai et al. '90). It is also the first step in a plan
that we propose for breaking a longstanding barrier in lower bounds for
small-depth boolean circuits.
  Novel aspects of our proof include sharp bounds on the information lost as
computation flows through the inner functions $g_j$, and the bootstrapping of
lower bounds for a multi-output function (Hamming weight) into lower bounds for
a single-output one (majority).


title:
Response Component Analysis for Sea State Estimation Using Artificial
  Neural Networks and Vessel Response Spectral Data

abstract:
  The use of the `ship as a wave buoy analogy' (SAWB) provides a novel means to
estimate sea states, where relationships are established between causal wave
properties and vessel motion response information. This study focuses on a
model-free machine learning approach to SAWB-based sea state estimation (SSE),
using neural networks (NNs) to map vessel response spectral data to statistical
wave properties.
  Results showed a strong correlation between heave responses and significant
wave height estimates, whilst the accuracy of mean wave period and wave heading
predictions were observed to improve considerably when data from multiple
vessel degrees of freedom (DOFs) was utilized. Overall, 3-DOF (heave, pitch and
roll) NNs for SSE were shown to perform well when compared to existing SSE
approaches that use similar simulation setups. Given the information-dense
statistical representation of vessel motion responses in spectral form, as well
as the ability of NNs to effectively model complex relationships between
variables, the designed SSE method shows promise for future adaptation to
mobile SSE systems using the SAWB approach.


title: Uncertainty-Based Non-Parametric Active Peak Detection

abstract:
  Active, non-parametric peak detection is considered. As a use case, active
source localization is examined and an uncertainty-based sampling scheme
algorithm to effectively localize the peak from a few energy measurements is
designed. It is shown that under very mild conditions, the source localization
error with $m$ actively chosen energy measurements scales as $O(\log^2 m/m)$.
Numerically, it is shown that in low-sample regimes, the proposed method enjoys
superior performance on several types of data and outperforms the
state-of-the-art passive source localization approaches and in the low sample
regime, can outperform greedy methods as well.


title: The bilayer Hubbard model: analysis based on the fermionic sign problem

abstract:
  The bilayer Hubbard model describes the antiferromagnet to spin singlet
transition and, potentially, aspects of the physics of unconventional
superconductors. Despite these important applications, significant aspects of
its `phase diagram' in the interplane hopping $t_\perp$ vs. on-site interaction
$U$ parameter space, at half-filling, are largely in disagreement. Here we
provide a novel analysis making use of the average sign of weights over the
course of the importance sampling in quantum Monte Carlo simulations to resolve
several central open questions. Specifically, this metric of the weights
clarifies the finite-sized metallic regimes at small $U$. Furthermore, at
strong interactions, it points to the existence of a crossover from a
correlated to uncorrelated band-insulator not yet explored in a variety of
existing, unbiased numerical methods. Our work demonstrates the versatility of
using properties of the weights in quantum Monte Carlo simulations to reveal
important physical characteristics of the models under study.


title:
Regularity criteria and Liouville Theorem for 3D inhomogeneous
  Navier-Stokes flows with vacuum

abstract:
  In this paper, we investigate the 3D inhomogeneous Navier-Stokes flows with
vacuum, and obtain regularity criteria and Liouville type theorems in the
Lorentz space if a smooth solution $(\rho, \mathbf{u})$ satisfies suitable
conditions.


title:
Mg$_2$Si is the new black: introducing a black silicide with $>$95%
  average absorption at 200-1800 nm wavelengths

abstract:
  Textured silicon surface structures, in particular black silicon (b-Si), open
up possibilities for Si-based solar cells and photodetectors to be extremely
thin and highly sensitive owing to perfect light-trapping and anti-reflection
properties. However, near-infrared (NIR) performance of bare b-Si is limited by
Si band gap of 1.12 eV or 1100 nm. This work reports a simple method to
increase NIR absorption of b-Si by $in$ $vacuo$ silicidation with magnesium.
Obtained Mg$_2$Si/b-Si heterostructure has a complex geometry where b-Si
nanocones are covered by Mg$_2$Si shells and crowned with flake-like Mg$_2$Si
hexagons. Mg$_2$Si formation atop b-Si resulted in 5-fold lower reflectivity
and optical absorption to be no lower than 88\% over 200-1800 nm spectral
range. More importantly, Mg$_2$Si/b-Si heterostructure is more adjusted to
match AM-1.5 solar spectrum with theoretically higher photogenerated current
density. The maximal advantage is demonstrated in the NIR region compared to
bare b-Si in full accordance with one's expectations about NIR sensitive narrow
band gap ($\sim$0.75 eV) semiconductor with high absorption coefficient, which
is Mg$_2$Si. Results of optical simulation confirmed the superiority of
Mg$_2$Si/b-Si NIR performance. Therefore, this new wide-band optical absorber
called black silicide proved rather competitive alongside state-of-the-art
approaches to extend b-Si spectral blackness.


title:
A characteristic-spectral-mixed scheme for six-dimensional
  Wigner-Coulomb dynamics

abstract:
  Numerical resolution for 6-D Wigner dynamics under the Coulomb potential
faces with the combined challenges of high dimensionality, nonlocality,
oscillation and singularity. In particular, the extremely huge memory storage
of 6-D grids hinders the usage of all existing deterministic numerical scheme,
which is well-known as the curse of dimensionality. To surmount these
difficulties, we propose a massively parallel solver, termed the
CHAracteristic-Spectral-Mixed (CHASM) scheme, by fully exploiting two distinct
features of the Wigner equation: Locality of spatial advection and nonlocality
of quantum interaction. Our scheme utilizes the local cubic B-spline basis to
interpolate the local spatial advection. The key is to use a perfectly matched
boundary condition to give a closure of spline coefficients, so that
distributed pieces can recover the global one as accurately as possible owing
to the rapid decay of wavelet basis in the dual space, and communication costs
are significantly reduced. To resolve the nonlocal pseudodifferential operator
with weakly singular symbol, CHASM further adopts the truncated kernel method
to attain a highly efficient approximation. Several typical experiments
including the quantum harmonic oscillator and Hydrogen 1s state demonstrate the
accuracy and efficiency of CHASM. The non-equilibrium electron-proton couplings
are also clearly displayed and reveal the uncertainty principle and quantum
tunneling in phase space. Finally, the scalability of CHASM up to 16000 cores
is presented.


title: Topological Langmuir-cyclotron wave

abstract:
  A theoretical framework is developed to describe the Topological
Langmuir-Cyclotron Wave (TLCW), a recently identified topological surface
excitation in magnetized plasmas. As a topological wave, the TLCW propagates
unidirectionally without scattering in complex boundaries. The TLCW is studied
theoretically as a spectral flow of the Hamiltonian
Pseudo-Differential-Operator (PDO) $\hat{H}$ for waves in an inhomogeneous
plasma. The semi-classical parameter of the Weyl quantization for plasma waves
is identified to be the ratio between electron gyro-radius and the
inhomogeneity scale length of the system. Hermitian eigenmode bundles of the
bulk Hamiltonian symbol H for plasma waves are formally defined. Because
momentum space in classical continuous media is contractible in general, the
topology of the eigenmode bundles over momentum space is trivial. This is in
stark contrast to condensed matters. Nontrivial topology of the eigenmode
bundles in classical continuous media only exists over phase space. A boundary
isomorphism theorem is established to facilitate the calculation of Chern
numbers of eigenmode bundles over non-contractible manifolds in phase space. It
also defines a topological charge of an isolated Weyl point in phase space
without adopting any connection. Using these algebraic topological techniques
and an index theorem formulated by Faure, it is rigorously proven that the
nontrivial topology at the Weyl point of the Langmuir wave-cyclotron wave
resonance generates the TLCW as a spectral flow. It is shown that the TLCW can
be faithfully modeled by a tilted Dirac cone in phase space. An analytical
solution of the entire spectrum of the PDO of a generic tilted phase space
Dirac cone, including its spectral flow, is given. The spectral flow index of a
tilted Dirac cone is one, and its mode structure is a shifted Gaussian
function.

categories: physics.plasm-ph cond-mat.mtrl-sci math-ph math.MP physics.space-ph


title:
Ranks of $RO(G)$-graded stable homotopy groups of spheres for finite
  groups $G$

abstract:
  We describe the distribution of infinite groups in the $RO(G)$-graded stable
homotopy groups of spheres for a finite group $G$.


title: Supervisor Obfuscation Against Covert Actuator Attackers

abstract:
  This work investigates the problem of synthesizing obfuscated supervisors
against covert actuator attackers. For a non-resilient supervisor S, for which
there exist some covert actuator attackers that are capable of inflicting
damage, we propose an algorithm to compute all the obfuscated supervisors, with
the requirements that: 1) any obfuscated supervisor S' is resilient against any
covert actuator attacker, and 2) the original closed-behavior of the
closed-loop system under S is preserved, that is, any obfuscated supervisor S'
is control equivalent to the original non-resilient supervisor S. We prove that
the designed algorithm to synthesize obfuscated supervisors against covert
actuator attack is sound and complete.


title:
An implementation of Galactic white dwarf binary data analysis for
  MLDC-3.1

abstract:
  The space-borne gravitational wave detectors will observe a large population
of double white dwarf binaries in the Milky Way. However, the search for double
white dwarfs in the gravitational wave data will be time-consuming due to the
large number of templates involved and antenna response calculation. In this
paper, we implement an iterative combinatorial algorithm to search for double
white dwarfs in MLDC-3.1 data. To quickly determine the rough parameters of the
target sources, the following algorithms are adopted in a coarse search
process: (1) using the downsampling method to reduce the number of original
data points; (2) using the undersampling method to speed up the generation of a
single waveform template; (3) using the stochastic template bank method to
quickly construct the waveform template bank while achieving high coverage of
the parameter space; (4) Combining the FFT acceleration algorithm with the
stochastic template bank to reduce the calculation time of a single template. A
fine search process is used to further determine the parameters of the signals
based on the coarse search, for which we adopt the Particle Swarm Optimization.
Finally, we detected $\mathcal{O}(10^4)$ double white dwarf signals, validating
the feasibility of our method.


title:
Hyper-viscoelastic 3D response of axons subjected to repeated tensile
  loads in brain white matter

abstract:
  A novel finite element method (FEM) is developed to study mechanical response
of axons embedded in extra cellular matrix (ECM) when subjected to harmonic
uniaxial stretch under purely non-affine kinematic boundary conditions. The
proposed modeling approach combines hyper-elastic (such as Ogden model) and
time/frequency domain viscoelastic constitutive models to evaluate the effect
of parametrically varying oligodendrocyte-axon tethering under harmonic stretch
at 50Hz. A hybrid hyper-viscoelastic material (HVE) model enabled the analysis
of repeated uniaxial load on stress propagation and damage accumulation in
white matter.
  In the proposed FEM, oligodendrocyte connections to axons are depicted via a
spring-dashpot model. This tethering technique facilitates contact definition
at various locations, parameterizes connection points and varies stiffness of
connection hubs. Results from a home-grown FE submodel configuration of a
single oligodendrocyte tethered to axons at various locations are presented.
Root mean square deviation (RMSD) are computed between stress-strain plots to
depict trends in mechanical response. Steady-state dynamic (SSD) simulations
show stress relaxation in axons. Gradual axonal softening under repetitive
loads is illustrated employing Prony series - HVE models. Representative
von-Mises stress plots indicate that undulated axons experience bending
stresses along their tortuous path, suggesting greater susceptibility to damage
accumulation and fatigue failure due to repeated strains.


title:
Searching for velocity-dependent dark matter annihilation signals from
  extragalactic halos

abstract:
  We consider gamma-ray signals of dark matter annihilation in extragalactic
halos in the case where dark matter annihilates from a $p$-wave or $d$-wave
state. In these scenarios, signals from extragalactic halos are enhanced
relative to other targets, such as the Galactic Center or dwarf spheroidal
galaxies, because the typical relative speed of the dark matter is larger in
extragalactic halos. We perform a mock data analysis of gamma rays produced by
dark matter annihilation in halos detected by the Sloan Digital Sky Survey. We
include a model for uncorrelated galactic and extragalactic gamma ray
backgrounds, as well as a simple model for backgrounds due to astrophysical
processes in the extragalactic halos detected by the survey. We find that, for
models which are still allowed by other gamma ray searches, searches of
extragalactic halos with the current Fermi exposure can produce evidence for
dark matter annihilation, though it is difficult to distinguish the $p$-wave
and $d$-wave scenarios. With a factor $10\times$ larger exposure, though,
discrimination of the velocity-dependence is possible.


title: Ramsey Envelope Modulation in NV Diamond Magnetometry

abstract:
  Nitrogen-vacancy (NV) spin ensembles in diamond provide an advanced magnetic
sensing platform, with applications in both the physical and life sciences. The
development of isotopically engineered $^{15}$NV diamond offers advantages over
naturally occurring $^{14}$NV for magnetometry, due to its simpler hyperfine
structure. However, for sensing modalities requiring a bias magnetic field not
aligned with the sensing NV axis, the absence of a quadrupole moment in the
$^{15}$N nuclear spin leads to pronounced envelope modulation effects in
time-dependent measurements of $^{15}$NV spin evolution. While such behavior in
spin echo experiments are well studied, analogous effects in Ramsey
measurements and the implications for magnetometry remain under-explored. Here,
we derive the modulated $^{15}$NV Ramsey response to a misaligned bias field,
using a simple vector description of the effective magnetic field on the
nuclear spin. The predicted modulation properties are then compared to
experimental results, revealing significant magnetic sensitivity loss if
unaddressed. We demonstrate that double-quantum coherences of the NV $S=1$
electronic spin states dramatically suppress these envelope modulations, while
additionally proving resilient to other parasitic effects such as strain
heterogeneity and temperature shifts.


title:
Interactive Grounded Language Understanding in a Collaborative
  Environment: IGLU 2021

abstract:
  Human intelligence has the remarkable ability to quickly adapt to new tasks
and environments. Starting from a very young age, humans acquire new skills and
learn how to solve new tasks either by imitating the behavior of others or by
following provided natural language instructions. To facilitate research in
this direction, we propose \emph{IGLU: Interactive Grounded Language
Understanding in a Collaborative Environment}.
  The primary goal of the competition is to approach the problem of how to
build interactive agents that learn to solve a task while provided with
grounded natural language instructions in a collaborative environment.
Understanding the complexity of the challenge, we split it into sub-tasks to
make it feasible for participants.


title:
Artificial Intelligence and Structural Injustice: Foundations for
  Equity, Values, and Responsibility

abstract:
  This chapter argues for a structural injustice approach to the governance of
AI. Structural injustice has an analytical and an evaluative component. The
analytical component consists of structural explanations that are well-known in
the social sciences. The evaluative component is a theory of justice.
Structural injustice is a powerful conceptual tool that allows researchers and
practitioners to identify, articulate, and perhaps even anticipate, AI biases.
The chapter begins with an example of racial bias in AI that arises from
structural injustice. The chapter then presents the concept of structural
injustice as introduced by the philosopher Iris Marion Young. The chapter
moreover argues that structural injustice is well suited as an approach to the
governance of AI and compares this approach to alternative approaches that
start from analyses of harms and benefits or from value statements. The chapter
suggests that structural injustice provides methodological and normative
foundations for the values and concerns of Diversity, Equity, and Inclusion.
The chapter closes with an outlook onto the idea of structure and on
responsibility. The idea of a structure is central to justice. An open
theoretical research question is to what extent AI is itself part of the
structure of society. Finally, the practice of responsibility is central to
structural injustice. Even if they cannot be held responsible for the existence
of structural injustice, every individual and every organization has some
responsibility to address structural injustice going forward.


title: Synchronization and clustering in complex quadratic networks

abstract:
  In continuation of prior work, we investigate ties between a network's
connectivity and ensemble dynamics. This relationship is notoriously difficult
to approach mathematically in natural, complex networks. In our work, we aim to
understand it in a canonical framework (using complex quadratic node dynamics,
coupled in networks which we call CQNs).
  After previously defining extensions of the Mandelbrot and Julia sets for
networks, we currently focus on the behavior of the node-wise projections of
these sets, and on defining and analyzing the phenomena of node clustering and
synchronization. We investigate the mechanisms that lead to nodes exhibiting
identical or different Mandelbrot set. We propose that clustering is strongly
determined by the network connectivity patterns, with the geometry of these
clusters further controlled by the connection weights.
  Synchronization and clustering are well-studied in the context of networks of
oscillators (such as neural networks). Understanding the similarities to how
these concepts apply to CQNs contributes to our understanding of universal
principles in dynamic networks, and may help extend theoretical results to
natural, complex systems.


title: Orbital integrals and normalizations of measures

abstract:
  This note provides an informal introduction, with examples, to some technical
aspects of the re-normalization of measures on orbital integrals used in the
work of Langlands, Frenkel-Langlands-Ng\^o, and Altug on Beyond Endoscopy. In
particular, we survey different relevant measures on algebraic tori and explain
the connection with the Tamagawa numbers. We work out the example of
$\mathrm{GL}_2$ in complete detail. The Appendix by Matthew Koster illustrates,
for the Lie algebras $\mathfrak{sl}_2$ and $\mathfrak{so}_3$, the relation
between the so-called geometric measure on the orbits and Kirillov's measure on
co-adjoint orbits in the linear dual of the Lie algebra.


title: Robust Conversational Agents against Imperceptible Toxicity Triggers

abstract:
  Warning: this paper contains content that maybe offensive or upsetting.
Recent research in Natural Language Processing (NLP) has advanced the
development of various toxicity detection models with the intention of
identifying and mitigating toxic language from existing systems. Despite the
abundance of research in this area, less attention has been given to
adversarial attacks that force the system to generate toxic language and the
defense against them. Existing work to generate such attacks is either based on
human-generated attacks which is costly and not scalable or, in case of
automatic attacks, the attack vector does not conform to human-like language,
which can be detected using a language model loss. In this work, we propose
attacks against conversational agents that are imperceptible, i.e., they fit
the conversation in terms of coherency, relevancy, and fluency, while they are
effective and scalable, i.e., they can automatically trigger the system into
generating toxic language. We then propose a defense mechanism against such
attacks which not only mitigates the attack but also attempts to maintain the
conversational flow. Through automatic and human evaluations, we show that our
defense is effective at avoiding toxic language generation even against
imperceptible toxicity triggers while the generated language fits the
conversation in terms of coherency and relevancy. Lastly, we establish the
generalizability of such a defense mechanism on language generation models
beyond conversational agents.


title: Optimising Equal Opportunity Fairness in Model Training

abstract:
  Real-world datasets often encode stereotypes and societal biases. Such biases
can be implicitly captured by trained models, leading to biased predictions and
exacerbating existing societal preconceptions. Existing debiasing methods, such
as adversarial training and removing protected information from
representations, have been shown to reduce bias. However, a disconnect between
fairness criteria and training objectives makes it difficult to reason
theoretically about the effectiveness of different techniques. In this work, we
propose two novel training objectives which directly optimise for the
widely-used criterion of {\it equal opportunity}, and show that they are
effective in reducing bias while maintaining high performance over two
classification tasks.


title: Quantized mass-energy effects in an Unruh-deWitt detector

abstract:
  A simple but powerful particle detector model consists of a two-level system
coupled to a field, where the detected particles are the field excitations.
This is known as the Unruh-deWitt detector. Research using this model has often
focused on either a completely classical description of the external degrees of
freedom of the detector, or a full field-theoretic treatment, where the
detector itself is described as a field. Recently there has been much interest
in quantum aspects of the detector's center of mass -- either described as
moving in superposition along semiclassical trajectories, or dynamically
evolving under a non-relativistic Hamiltonian. However, the processes of
interest -- the absorption or emission of field particles -- necessarily change
the detector's rest mass by the amount of energy of the absorbed or emitted
field quanta. Neither of the above models can capture such effects. Here we
incorporate the quantization of the detector's mass-energy into the
Unruh-deWitt model. We show that internal energy changes due to emission or
absorption are relevant even in the lowest energy limit. Specifically,
corrections to transition rates due to the detector's mass changing cannot be
ignored unless the entire center of mass dynamics is also ignored. Our results
imply that one cannot have a consistent model of the Unruh-deWitt detector as a
massive particle without including the mass-energy equivalence.


title:
Large payload bidirectional quantum secure direct communication without
  information leakage

abstract:
  A large payload bidirectional quantum secure direct communication (BQSDC)
protocol without information leakage is proposed, which is based on
entanglement swapping between any two GHZ states. Two remote authorized
parties, Alice and Bob, can safely exchange their individual secret messages
without worrying about the information leakage problem. Our protocol uses a
shared secret GHZ state to overcome the information leakage problem. The shared
secret GHZ state plays two roles in the bidirectional communication process: on
one hand, it lets Bob know the prepared initial state; on the other hand, it is
used for encoding Bob's secret messages. Moreover, our protocol can transmit
six bits of secret messages per round communication. Compared with those
previous BQSDC protocols, the advantage of our protocol lies in having the
following two characters simultaneously: on one hand, it overcomes the
information leakage problem; one the other hand, its capacity is as high as six
bits per round communication.


title:
Exclusive vector meson productions with the analytical solution of
  Balitsky-Kovchegov Equation

abstract:
  Exclusive vector meson production is an excellent probe for describing the
structure of proton. In this paper, based on dipole model, the differential
cross sections, total cross sections and the ratios of the longitudinal to
transverse cross sections of $J/\psi$ and $\rho^0$ productions are calculated
with the analytical solution of Balitsky-Kovchegov (BK) equation. In addition,
we also consider the influences of two meson wave function models on the
results. Our predictions, which are little sensitive to meson wave functions,
agree with the experimental data. The analytical solution of BK equation is
reliable for description of exclusive vector meson production in a certain
range of $Q^2$.


title: Compressive Ptychography using Deep Image and Generative Priors

abstract:
  Ptychography is a well-established coherent diffraction imaging technique
that enables non-invasive imaging of samples at a nanometer scale. It has been
extensively used in various areas such as the defense industry or materials
science. One major limitation of ptychography is the long data acquisition time
due to mechanical scanning of the sample; therefore, approaches to reduce the
scan points are highly desired. However, reconstructions with less number of
scan points lead to imaging artifacts and significant distortions, hindering a
quantitative evaluation of the results. To address this bottleneck, we propose
a generative model combining deep image priors with deep generative priors. The
self-training approach optimizes the deep generative neural network to create a
solution for a given dataset. We complement our approach with a prior acquired
from a previously trained discriminator network to avoid a possible divergence
from the desired output caused by the noise in the measurements. We also
suggest using the total variation as a complementary before combat artifacts
due to measurement noise. We analyze our approach with numerical experiments
through different probe overlap percentages and varying noise levels. We also
demonstrate improved reconstruction accuracy compared to the state-of-the-art
method and discuss the advantages and disadvantages of our approach.


title:
A kernel-based meshless conservative Galerkin method for solving
  Hamiltonian wave equations

abstract:
  We propose a meshless conservative Galerkin method for solving Hamiltonian
wave equations. We first discretize the equation in space using radial basis
functions in a Galerkin-type formulation. Differ from the traditional RBF
Galerkin method that directly uses nonlinear functions in its weak form, our
method employs appropriate projection operators in the construction of the
Galerkin equation, which will be shown to conserve global energies. Moreover,
we provide a complete error analysis to the proposed discretization. We further
derive the fully discretized solution by a second order average vector field
scheme. We prove that the fully discretized solution preserved the discretized
energy exactly. Finally, we provide some numerical examples to demonstrate the
accuracy and the energy conservation.


title: Spot-adaptive Knowledge Distillation

abstract:
  Knowledge distillation (KD) has become a well established paradigm for
compressing deep neural networks. The typical way of conducting knowledge
distillation is to train the student network under the supervision of the
teacher network to harness the knowledge at one or multiple spots (i.e.,
layers) in the teacher network. The distillation spots, once specified, will
not change for all the training samples, throughout the whole distillation
process. In this work, we argue that distillation spots should be adaptive to
training samples and distillation epochs. We thus propose a new distillation
strategy, termed spot-adaptive KD (SAKD), to adaptively determine the
distillation spots in the teacher network per sample, at every training
iteration during the whole distillation period. As SAKD actually focuses on
"where to distill" instead of "what to distill" that is widely investigated by
most existing works, it can be seamlessly integrated into existing distillation
methods to further improve their performance. Extensive experiments with 10
state-of-the-art distillers are conducted to demonstrate the effectiveness of
SAKD for improving their distillation performance, under both homogeneous and
heterogeneous distillation settings. Code is available at
https://github.com/zju-vipa/spot-adaptive-pytorch


title:
Ahlfors-David regularity of intrinsically quasi-symmetric sections in
  metric spaces

abstract:
  We introduce a definition of intrinsically quasi-symmetric sections in metric
spaces and we prove the Ahlfors-David regularity for this class of sections. We
follow a recent result by Le Donne and the author where we generalize the
notion of intrinsically Lipschitz graphs in the sense of Franchi, Serapioni and
Serra Cassano. We do this by focusing our attention on the graph property
instead of the map one.


title: Information leakage resistant quantum dialogue against collective noise

abstract:
  In this paper, two information leakage resistant quantum dialogue (QD)
protocols over a collective-noise channel are proposed. Decoherence-free
subspace (DFS) is used to erase the influence from two kinds of collective
noise, i.e., collective-dephasing noise and collective-rotation noise, where
each logical qubit is composed of two physical qubits and free from noise. In
each of the two proposed protocols, the secret messages are encoded on the
initial logical qubits via two composite unitary operations. Moreover, the
single-photon measurements rather than the Bell-state measurements or the more
complicated measurements are needed for decoding, making the two proposed
protocols easier to implement. The initial state of each logical qubit is
privately shared between the two authenticated users through the direct
transmission of its auxiliary counterpart. Consequently, the information
leakage problem is avoided in the two proposed protocols. Moreover, the
detailed security analysis also shows that Eve's several famous active attacks
can be effectively overcome, such as the Trojan horse attack, the
intercept-resend attack, the measure-resend attack, the entangle-measure attack
and the correlation-elicitation (CE) attack.


title:
Quasi-periodic behaviour in the $\gamma$-ray light curve of the blazar
  PKS 0405-385

abstract:
  We analyze the quasi-periodic oscillation (QPO) of the historical light curve
of FSRQs PKS 0405-385 detected by the Fermi LAT from August 2008 to November
2021. To identify and determine the QPO signal of PKS 0405-385 in the
$\gamma$-ray light curve, we use four time series analysis techniques based on
frequency and time domains, i.e., the Lomb-Scargle periodogram (LSP), the
weighted wavelet z-transform (WWZ), the REDFIT and the epoch folding. The
results show that PKS 0405-385 has a quasi-periodic behavior of $\sim$2.8 yr
with the significance of $\sim$4.3$\sigma$ in Fermi long-term monitoring.
Remarkably, we also performed QPO analysis in the G-band light curve observed
from October 2014 to October 2021 using LSP and WWZ technology, and the results
($\sim$4$\sigma$ of significance) are consistent with the periodic detection in
$\gamma$-ray. This may imply that the optical emission is radiated by an
electron population same as the \gr\ emission. In discussing the possible
mechanism of quasi-periodic behavior, either the helical motion within a jet or
the supermassive black hole binary system provides a viable explanation for the
QPO of 2.8 yr, and the relevant parameters have been estimated.


title: Intrinsically Lipschitz graphs on semidirect products of groups

abstract:
  In the metric spaces, we give some equivalent condition of intrinsically
Lipschitz maps introduce by Franchi, Serapioni and Serra Cassano in
subRiemannian Carnot groups. Unlike what happens in the Carnot groups, in our
context intrinsic dilation do not exist but we can prove the same results using
the Lipschitz property of the projection maps.


title: A note about Intrinsically Lipschitz constants

abstract:
  Recently, Le Donne and the author introduce a notion of intrinsically
Lipschitz graphs in metric spaces. The idea of this paper is to investigate
about the properties of the intrinsically Lipschitz constants. More precisely,
we give the Leibniz formula and the product formula for the intrinsic slope.


title:
Enhanced optoelectronic performance and photogating effect in
  quasi-one-dimensional BiSeI wires

abstract:
  Quasi-one-dimensional (quasi-1D) materials are a newly arising topic in
low-dimensional researches. As a result of reduced dimensionality and enhanced
anisotropy, the quasi-1D structure gives rise to novel properties and promising
applications such as photodetectors. However, it remains an open question
whether performance crossover will occur when the channel material is
downsized. Here we report on the fabrication and testing of photodetectors
based on exfoliated quasi-1D BiSeI thin wires. Compared with the device on bulk
crystal, a significantly enhanced photoresponse is observed, which is
manifested by a series of performance parameters, including ultrahigh
responsivity (7 x 10$^4$ A W$^{-1}$), specific detectivity (2.5 x 10$^{14}$
Jones) and external quantum efficiency (1.8 x 10$^7$%) when $V_{\textrm {ds}}$
= 3 V, $\lambda$ = 515 nm and $P$ = 0.01 mW cm$^{-2}$. The conventional
photoconductive effect is unlikely to account for such a superior
photoresponse, which is ultimately understood in terms of the increased
specific surface area and the photogating effect caused by trapping states.
This work provides a perspective for the modulation of optoelectronic
properties and performance in quasi-1D materials.


title: Dangling-Aware Entity Alignment with Mixed High-Order Proximities

abstract:
  We study dangling-aware entity alignment in knowledge graphs (KGs), which is
an underexplored but important problem. As different KGs are naturally
constructed by different sets of entities, a KG commonly contains some dangling
entities that cannot find counterparts in other KGs. Therefore, dangling-aware
entity alignment is more realistic than the conventional entity alignment where
prior studies simply ignore dangling entities. We propose a framework using
mixed high-order proximities on dangling-aware entity alignment. Our framework
utilizes both the local high-order proximity in a nearest neighbor subgraph and
the global high-order proximity in an embedding space for both dangling
detection and entity alignment. Extensive experiments with two evaluation
settings shows that our framework more precisely detects dangling entities, and
better aligns matchable entities. Further investigations demonstrate that our
framework can mitigate the hubness problem on dangling-aware entity alignment.


title:
Measurement of absolute radius, refractive index and dispersion of a
  long cylinder

abstract:
  Long cylinders, such as optical fibers, are some of the most widely used
photonic devices. The radius and refractive index of these fibers are therefore
fundamentally important parameters in determining their performance. We have
developed a method to determine the absolute radius, refractive index, and
chromatic dispersion of a long cylinder using only the resonance wavelengths of
the whispering gallery modes around its circumference for two different
polarizations. Since this method only requires the measurement of resonance
wavelengths, it is non-destructive and it can be performed using standard
equipment. As a proof-of-concept, we demonstrate the method on a $125\mu m$
optical fiber and an $80\mu m$ borosilicate capillary fiber with thick walls,
obtaining values for the diameter and the refractive index with an accuracy of
1 nm and 0.5%, respectively.


title:
Analysis of ballistic resonance in the {\alpha}-Fermi-Pasta-Ulam-Tsingou
  model

abstract:
  Ballistic resonance phenomena is elucidated in the one-dimensional
alpha-Fermi-Pasta-Ulam-Tsingou(FPUT) model using an approach of computing
thermal response functions. The existence of periodic oscillations in spatially
sinusoidal temperature profiles seen in previous studies is confirmed. However,
the results obtained using response functions enable a more complete
understanding. In particular, it is shown that ballistic resonance involves
beats between normal modes which tend to reinforce in a one-dimensional chain.
Anharmonic scattering acts to destroy phase coherence across the statistical
ensemble, and with increasing anharmonicity, transport is driven towards the
diffusive regime. These results provide additional insight into anomalous heat
transport in low-dimensional systems. Normal-mode scattering is also explored
using time correlation functions. Interestingly, these calculations, in
addition to demonstrating loss of phase coherence across an ensemble of
simulations, appear to show evidence of so-called q-breathers in conditions of
strong anharmonicity. Finally, we described how the approach outlined here
could be developed to include quantum statistics and also also first-principles
estimates of phonon scattering rates to elucidate second sound and ballistic
transport in realistic materials at low temperatures.


title:
Evolution of global polarization in relativistic heavy-ion collisions
  within a perturbative approach

abstract:
  Extremely large angular orbital momentum can be produced in non-central
heavy-ion collisions, leading to a strong transverse polarization of partons
that scatter through the quark-gluon plasma (QGP) due to spin-orbital coupling.
We develop a perturbative approach to describe the formation and spacetime
evolution of quark polarization inside the QGP. Polarization from both the
initial hard scatterings and interactions with the QGP have been consistently
described using the quark-potential scattering approach, which has been coupled
to realistic initial condition calculation and the subsequent (3+1)-dimensional
viscous hydrodynamic simulation of the QGP for the first time. Within this
improved approach, we have found that different spacetime-rapidity-dependent
initial energy density distributions generate different time evolution profiles
of the longitudinal flow velocity gradient of the QGP, which further lead to an
approximately 15% difference in the final polarization of quarks collected on
the hadronization hypersurface of the QGP. Therefore, in addition to the
collective flow coefficients, the hyperon polarization may serve as a novel
tool to help constrain the initial condition of the hot nuclear matter created
in high-energy nuclear collisions.


title:
Dynamic Bayesian Network Auxiliary ABC-SMC for Hybrid Model Bayesian
  Inference to Accelerate Biomanufacturing Process Mechanism Learning and
  Robust Control

abstract:
  Driven by the critical needs of biomanufacturing 4.0, we present a
probabilistic knowledge graph hybrid model characterizing complex
spatial-temporal causal interdependencies of underlying bioprocessing
mechanisms. It can faithfully capture the important properties, including
nonlinear reactions, partially observed state, and nonstationary dynamics.
Given limited process observations, we derive a posterior distribution
quantifying model uncertainty, which can facilitate mechanism learning and
support robust process control. To avoid evaluation of intractable likelihood,
Approximate Bayesian Computation sampling with Sequential Monte Carlo (ABC-SMC)
is developed to approximate the posterior distribution. Given high stochastic
and model uncertainties, it is computationally expensive to match process
output trajectories. Therefore, we propose a linear Gaussian dynamic Bayesian
network (LG-DBN) auxiliary likelihood-based ABC-SMC algorithm. Through matching
observed and simulated summary statistics, the proposed approach can
dramatically reduce the computation cost and accelerate the posterior
approximation convergence.


title: Relational Representation Learning in Visually-Rich Documents

abstract:
  Relational understanding is critical for a number of visually-rich documents
(VRDs) understanding tasks. Through multi-modal pre-training, recent studies
provide comprehensive contextual representations and exploit them as prior
knowledge for downstream tasks. In spite of their impressive results, we
observe that the widespread relational hints (e.g., relation of key/value
fields on receipts) built upon contextual knowledge are not excavated yet. To
mitigate this gap, we propose DocReL, a Document Relational Representation
Learning framework. The major challenge of DocReL roots in the variety of
relations. From the simplest pairwise relation to the complex global structure,
it is infeasible to conduct supervised training due to the definition of
relation varies and even conflicts in different tasks. To deal with the
unpredictable definition of relations, we propose a novel contrastive learning
task named Relational Consistency Modeling (RCM), which harnesses the fact that
existing relations should be consistent in differently augmented positive
views. RCM provides relational representations which are more compatible to the
urgent need of downstream tasks, even without any knowledge about the exact
definition of relation. DocReL achieves better performance on a wide variety of
VRD relational understanding tasks, including table structure recognition, key
information extraction and reading order detection.


title:
Ferromagnetic MnBi4Te7 obtained with low concentration Sb doping: A
  promising platform for exploring topological quantum states

abstract:
  The tuning of magnetic phase, chemical potential, and structure is crucial to
observe diverse exotic topological quantum states in $MnBi_2Te_4(Bi_2Te_3)_m$
(m = 0, 1, 2, & 3). Here we show a ferromagnetic (FM) phase with a chiral
crystal structure in $Mn(Bi_{1-x}Sb_x)_4Te_7$, obtained via tuning the growth
conditions and Sb concentration. Unlike previously reported
$Mn(Bi_{1-x}Sb_x)_4Te_7$, which exhibits FM transitions only at high Sb doping
levels, our samples show FM transitions ($T_C$ = 13.5 K) at 15%-27% doping
levels. Furthermore, our single crystal x-ray diffraction structure refinements
find Sb doping leads to a chiral structure with the space group of P3,
contrasted with the centrosymmetric P-3m1 crystal structure of the parent
compound $MnBi_4Te_7$. Through ARPES measurements, we also demonstrated that
the non-trivial band topology is preserved in the Sb-doped FM samples. Given
that the non-trivial band topology of this system remains robust for low Sb
doping levels, our success in making FM $Mn(Bi_{1-x}Sb_x)_4Te_7$ with $x$ =
0.15, 0.175, 0.2 & 0.27 paves the way for realizing the predicted topological
quantum states such as axion insulator and Weyl semimetals. Additionally, we
also observed magnetic glassy behavior in both antiferromagnetic $MnBi_4Te_7$
and FM $Mn(Bi_{1-x}Sb_x)_4Te_7$ samples, which we believe originates from
cluster spin glass phases coexisting with long-range AFM/FM orders. We have
also discussed how the antisite Mn ions impact the interlayer magnetic coupling
and how FM interlayer coupling is stabilized in this system.


title: Surface Reconstruction from Point Clouds: A Survey and a Benchmark

abstract:
  Reconstruction of a continuous surface of two-dimensional manifold from its
raw, discrete point cloud observation is a long-standing problem. The problem
is technically ill-posed, and becomes more difficult considering that various
sensing imperfections would appear in the point clouds obtained by practical
depth scanning. In literature, a rich set of methods has been proposed, and
reviews of existing methods are also provided. However, existing reviews are
short of thorough investigations on a common benchmark. The present paper aims
to review and benchmark existing methods in the new era of deep learning
surface reconstruction. To this end, we contribute a large-scale benchmarking
dataset consisting of both synthetic and real-scanned data; the benchmark
includes object- and scene-level surfaces and takes into account various
sensing imperfections that are commonly encountered in practical depth
scanning. We conduct thorough empirical studies by comparing existing methods
on the constructed benchmark, and pay special attention on robustness of
existing methods against various scanning imperfections; we also study how
different methods generalize in terms of reconstructing complex surface shapes.
Our studies help identify the best conditions under which different methods
work, and suggest some empirical findings. For example, while deep learning
methods are increasingly popular, our systematic studies suggest that,
surprisingly, a few classical methods perform even better in terms of both
robustness and generalization; our studies also suggest that the practical
challenges of misalignment of point sets from multi-view scanning, missing of
surface points, and point outliers remain unsolved by all the existing surface
reconstruction methods. We expect that the benchmark and our studies would be
valuable both for practitioners and as a guidance for new innovations in future
research.


title: Subverting Fair Image Search with Generative Adversarial Perturbations

abstract:
  In this work we explore the intersection fairness and robustness in the
context of ranking: \textit{when a ranking model has been calibrated to achieve
some definition of fairness, is it possible for an external adversary to make
the ranking model behave unfairly without having access to the model or
training data?} To investigate this question, we present a case study in which
we develop and then attack a state-of-the-art, fairness-aware image search
engine using images that have been maliciously modified using a Generative
Adversarial Perturbation (GAP) model. These perturbations attempt to cause the
fair re-ranking algorithm to unfairly boost the rank of images containing
people from an adversary-selected subpopulation.
  We present results from extensive experiments demonstrating that our attacks
can successfully confer significant unfair advantage to people from the
majority class relative to fairly-ranked baseline search results. We
demonstrate that our attacks are robust across a number of variables, that they
have close to zero impact on the relevance of search results, and that they
succeed under a strict threat model. Our findings highlight the danger of
deploying fair machine learning algorithms in-the-wild when (1) the data
necessary to achieve fairness may be adversarially manipulated, and (2) the
models themselves are not robust against attacks.


title: Variance-Gamma (VG) model: Fractional Fourier Transform (FRFT)

abstract:
  The paper examines the Fractional Fourier Transform (FRFT) based technique as
a tool for obtaining the probability density function and its derivatives, and
mainly for fitting stochastic model with the fundamental probabilistic
relationships of infinite divisibility. The probability density functions are
computed, and the distributional proprieties are reviewed for Variance-Gamma
(VG) model. The VG model has been increasingly used as an alternative to the
Classical Lognormal Model (CLM) in modelling asset prices. The VG model was
estimated by the FRFT. The data comes from the SPY ETF historical data. The
Kolmogorov-Smirnov (KS) goodness-of-fit shows that the VG model fits the
cumulative distribution of the sample data better than the CLM. The best VG
model comes from the FRFT estimation.


title:
Characterizing ultra-narrow momentum of atoms by standing-wave
  light-pulse sequences

abstract:
  We propose a method to characterize the ultra-narrow momentum distribution of
atomic gases by employing a standing-wave light-pulse sequences beam-splitter.
The mechanism of beam splitting is analyzed in detail, and the influence of a
finite-width momentum distribution on the population of each diffraction order
is given. The temperature of ultracold atomic gases can be calibrated by
measuring the ratio of population in different diffraction orders after double
standing-wave light-pulses. We obtain analytical expressions for two typical
cases, and demonstrate phase space evolution in the whole process by using the
Wigner function. This method is valid for both classical atomic gas and
Bose-Einstein condensates, and it is suited for temperature measurement on the
space ultra-cold atomic physics platform, in which the ultra-narrow momentum
distribution of atomic gas is on the order of 100pK or even lower.


title: Channel-Adaptive Wireless Image Transmission with OFDM

abstract:
  We present a learning-based channel-adaptive joint source and channel coding
(CA-JSCC) scheme for wireless image transmission over multipath fading
channels. The proposed method is an end-to-end autoencoder architecture with a
dual-attention mechanism employing orthogonal frequency division multiplexing
(OFDM) transmission. Unlike the previous works, our approach is adaptive to
channel-gain and noise-power variations by exploiting the estimated channel
state information (CSI). Specifically, with the proposed dual-attention
mechanism, our model can learn to map the features and allocate
transmission-power resources judiciously based on the estimated CSI. Extensive
numerical experiments verify that CA-JSCC achieves state-of-the-art performance
among existing JSCC schemes. In addition, CA-JSCC is robust to varying channel
conditions and can better exploit the limited channel resources by transmitting
critical features over better subchannels.


title:
SMC and MPC based composite control for a constrained second-order
  nonlinear system with external disturbances

abstract:
  The paper proposes a novel strucuture of composite control consisting of
sliding mode control (SMC) and model predictive control (MPC) for a constrained
second-order nonlinear system with external disturbances. Based on linear
sliding mode surface, the control law is first generated by MPC problem to
drive the system state to a certain region, and then is switched to SMC which
can promise the input-to state stable (ISS). The recursive feasibility of MPC
and closed-loop stability is guaranteed, while the whole process behaves
robustness and constraints satisfaction. Finally, a simulation case is provided
to demonstate the effectiveness of the composite control.


title: Fault tolerant authenticated quantum dialogue using logical Bell states

abstract:
  Two fault tolerant authenticated quantum dialogue (AQD) protocols are
proposed in this paper by employing logical Bell states as the quantum
resource, which combat the collective-dephasing noise and the
collective-rotation noise, respectively. The two proposed protocols each can
accomplish the mutual identity authentication and the dialogue between two
participants simultaneously and securely over one kind of collective noise
channels. In each of two proposed protocols, the information transmitted
through the classical channel is assumed to be eavesdroppable and modifiable.
The key for choosing the measurement bases of sample logical qubits is
pre-shared privately between two participants. The Bell state measurements
rather than the four-qubit joint measurements are adopted for decoding. The two
participants share the initial states of message logical Bell states with
resort to the direct transmission of auxiliary logical Bell states so that the
information leakage problem is avoided. The impersonation attack, the
man-in-the-middle attack, the modification attack and the Trojan horse attacks
from Eve all are detectable.


title:
$\Lambda/{\bar \Lambda}$ Polarization and Splitting Induced by Rotation
  and Magnetic Field

abstract:
  The global polarization of $\Lambda/{\bar \Lambda}$ and the splitting of
${\bar \Lambda}-\Lambda$ polarization induced by rotation and magnetic field
has been investigated in a dynamical quark model by taking into account the
axial vector interaction and the anomalous magnetic moment of quarks. It is
found that the rotation leads to the spin polarization of quarks and
anti-quarks with the same sign, while the magnetic field to opposite sign,
which corresponds to the ${\bar \Lambda}-\Lambda$ polarization splitting. The
combination of the two effects leads to perfect agreement with experiment data.
Quantitatively, the axial vector spin polarization contributes 30$\%$ of the
global polarization and the anomalous magnetic moment of quarks contributes
40$\%$ to the splitting of ${\bar \Lambda}-\Lambda$ polarization. However, at
$\sqrt{s_{NN}} \leq 7.7 \text{GeV}$, it still remains a challenge to reach
enough magnitude of the magnetic field at freeze-out.


title:
Towards Real-time Traffic Sign and Traffic Light Detection on Embedded
  Systems

abstract:
  Recent work done on traffic sign and traffic light detection focus on
improving detection accuracy in complex scenarios, yet many fail to deliver
real-time performance, specifically with limited computational resources. In
this work, we propose a simple deep learning based end-to-end detection
framework, which effectively tackles challenges inherent to traffic sign and
traffic light detection such as small size, large number of classes and complex
road scenarios. We optimize the detection models using TensorRT and integrate
with Robot Operating System to deploy on an Nvidia Jetson AGX Xavier as our
embedded device. The overall system achieves a high inference speed of 63
frames per second, demonstrating the capability of our system to perform in
real-time. Furthermore, we introduce CeyRo, which is the first ever large-scale
traffic sign and traffic light detection dataset for the Sri Lankan context.
Our dataset consists of 7984 total images with 10176 traffic sign and traffic
light instances covering 70 traffic sign and 5 traffic light classes. The
images have a high resolution of 1920 x 1080 and capture a wide range of
challenging road scenarios with different weather and lighting conditions. Our
work is publicly available at https://github.com/oshadajay/CeyRo.


title:
Quantum Semantic Communications: An Unexplored Avenue for Contextual
  Networking

abstract:
  Future communication systems (6G and beyond) will witness a paradigm shift
from communication-intensive systems towards intelligent computing-intensive
architectures. A key research area that enables this transition is semantic
communications, whereby the communication process conveys the meaning of
messages instead of being a mere reconstruction process of raw, naive data
bits. In this paper, a novel quantum semantic communications (QSC) framework is
proposed to develop reasoning-based future communication systems with quantum
semantic representations that are characterized with minimalism, efficiency,
and accuracy. In particular, the concepts of quantum embedding and
high-dimensional Hilbert spaces are exploited so as to extract the meaning of
classical data. Moreover, in order to equip our approach with minimalism and
efficiency, an unsupervised quantum machine learning (QML) technique, namely,
quantum clustering is employed. Quantum clustering enables extraction of
contextual information and distinct characterization of the semantics of the
message to be conveyed. Subsequently, to successfully transmit the constructed
semantic representations, quantum communication links are used to transfer the
quantum states. This new QSC framework exploits unique quantum principles such
as the minimalism of entangled photons, quantum-semantic entropy of noise, and
quantum fidelity. Simulation results show that the proposed framework can save
around 85\% of quantum communication resources, i.e., entangled photons,
compared to semantic-agnostic quantum communication schemes. Results also show
the benefits of increasing the number of dimensions on the expressivity of the
semantic representations.


title: Selecting and Modelling Remnant AGNs with Limited Spectral Coverage

abstract:
  Quantifying the energetics and lifetimes of remnant radio-loud active
galactic nuclei (AGNs) is much more challenging than for active sources due to
the added complexity of accurately determining the time since the central black
hole switched off. Independent spectral modelling of remnant lobes enables the
derivation of the remnant ratio, $R_\mathrm{rem}$, (i.e. `off-time/source
age'), thus reducing this complexity back to that of active sources however,
the requirement of high-frequency ($\gtrsim5\,$GHz) coverage makes the
application of this technique over large-area radio surveys difficult. In this
work we propose a new method, which relies on the observed brightness of
backflow of Fanaroff-Riley type~II lobes, combined with the \emph{Radio AGN in
Semi-Analytic Environments} (RAiSE) code, to measure the duration of the
remnant phase. Sensitive radio observations of the remnant radio galaxy
J2253-34 are obtained to provide a robust comparison of this technique with the
canonical spectral analysis and modelling methods. We find that the remnant
lifetimes modelled by each method are consistent; spectral modelling yields
$R_\mathrm{rem} = 0.23\pm0.02$, compared to $R_\mathrm{rem} = 0.26\pm0.02$ from
our new method. We examine the viability of applying our proposed technique to
low-frequency radio surveys using mock radio source populations, and examine
whether the technique is sensitive to any intrinsic properties of radio AGNs.
Our results show that the technique can be used to robustly classify active and
remnant populations, with the most confident predictions for the remnant ratio,
and thus off-time, in the longest-lived radio sources ($>50~$Myr) and those at
higher redshifts ($z > 0.1$).


title:
A Landau-Devonshire Analysis of Strain Effects on Ferroelectric
  Al1-xScxN

abstract:
  We present a thermodynamic analysis of the recently discovered nitride
ferroelectric materials using the classic Landau-Devonshire approach. The
electrostrictive and dielectric stiffness coefficients of Al1-xScxN with
wurtzite structure (6mm) are determined using a free energy density function
assuming a hexagonal parent phase (6/mmm), with the first order phase
transition based on the dielectric stiffness relationships. The results of this
analysis show that the strain sensitivity of the energy barrier is one order of
magnitude larger than that of the spontaneous polarization in these novel
wurtzite ferroelectrics, yet both are less sensitive to strain compared to
classic perovskite ferroelectrics. These analysis results reported here explain
experimentally reported sensitivity of coercive field to elastic strain/stress
in Al1-xScxN films, and would enable further thermodynamic analysis via phase
field simulation and related methods.


title: Fast Left Kan Extensions Using The Chase

abstract:
  We show how computation of left Kan extensions can be reduced to computation
of free models of cartesian (finite-limit) theories. We discuss how the
standard and parallel chase compute weakly free models of regular theories and
free models of cartesian theories, and compare the concept of "free model" with
a similar concept from database theory known as "universal model". We prove
that, as algorithms for computing finite free models of cartesian theories, the
standard and parallel chase are complete under fairness assumptions. Finally,
we describe an optimized implementation of the parallel chase specialized to
left Kan extensions that achieves an order of magnitude improvement in our
performance benchmarks compared to the next fastest left Kan extension
algorithm we are aware of.


title:
Cooperative Reflection and Synchronization Design for Distributed
  Multiple-RIS Communications

abstract:
  To reap the promised gain achieved by distributed reconfigurable intelligent
surfaces (RISs)-enhanced communications in a wireless network, timing
synchronization among these metasurfaces is an essential prerequisite in
practice. This paper proposes a unified framework for the joint estimation of
the unknown timing offsets and the RIS channel parameters, as well as the
design of cooperative reflection and synchronization algorithm for the
distributed multiple-RIS communication. Considering that RIS is usually a
passive device with limited capability of signal processing, the individual
timing offset and channel gains of each hop of the RIS links cannot be directly
estimated. To make the estimation tractable, we propose to estimate the
cascaded channels and timing offsets jointly by deriving a maximum likelihood
estimator. Furthermore, we theoretically characterize the Cramer-Rao lower
bound (CRLB) to evaluate the accuracy of this estimator. By using the proposed
estimator and the derived CRLBs, an efficient resynchronization algorithm is
devised jointly at the RISs and the destination to compensate the multiple
timing offsets. Based on the majorization-minimization framework, the proposed
algorithm admits semi-closed and closed form solutions for the RIS reflection
matrices and the timing offset equalizer, respectively. Simulation results
verify that our theoretical analysis well matches the numerical tests and
validate the effectiveness of the proposed resynchronization algorithm.


title:
Ultra-Reliable Distributed Cloud Network Control with End-to-End Latency
  Constraints

abstract:
  We are entering a rapidly unfolding future driven by the delivery of
real-time computation services, such as industrial automation and augmented
reality, collectively referred to as AgI services, over highly distributed
cloud/edge computing networks. The interaction intensive nature of AgI services
is accelerating the need for networking solutions that provide strict latency
guarantees. In contrast to most existing studies that can only characterize
average delay performance, we focus on the critical goal of delivering AgI
services ahead of corresponding deadlines on a per-packet basis, while
minimizing overall cloud network operational cost. To this end, we design a
novel queuing system able to track data packets' lifetime and formalize the
delay-constrained least-cost dynamic network control problem. To address this
challenging problem, we first study the setting with average capacity (or
resource budget) constraints, for which we characterize the delay-constrained
stability region and design a near-optimal control policy leveraging Lyapunov
optimization theory on an equivalent virtual network. Guided by the same
principle, we tackle the peak capacity constrained scenario by developing the
reliable cloud network control (RCNC) algorithm, which employs a two-way
optimization method to make actual and virtual network flow solutions converge
in an iterative manner. Extensive numerical results show the superior
performance of the proposed control policy compared with the state-of-the-art
cloud network control algorithm, and the value of guaranteeing strict
end-to-end deadlines for the delivery of next-generation AgI services.


title:
ActorRL: A Novel Distributed Reinforcement Learning for Autonomous
  Intersection Management

abstract:
  As an emerging tendency of future transportation, Connected Autonomous
Vehicle (CAV) has the potential to improve traffic capacity and safety at
intersections. In autonomous intersection management (AIM), distributed
scheduling algorithm formulates the interactions among traffic participants as
multi-agent problem with information exchange and behavioral cooperation. Deep
Reinforcement Learning (DRL), as an approach obtaining satisfying performance
in many domains, has been brought in AIM recently. Attempts to overcome the
challenges of curse of dimensionality and instability in multi-agent DRL, we
propose a novel DRL framework for AIM problem, ActorRL, where actor allocation
mechanism attaches multiple roles with different personalities to CAVs under
global observation, including radical actor, conservative actor, safety-first
actor, etc. The actor shares behavioral policies with collective memories from
CAVs it is assigned to, playing the role of "navigator" at AIM. In experiments,
we compares the proposed method with several widely used scheduling methods and
distributed DRL without actor allocation, the results shows better performance
over benchmarks.


title:
Optimal control for Hamiltonian parameter estimation in non-commuting
  and bipartite quantum dynamics

abstract:
  The ability to characterise a Hamiltonian with high precision is crucial for
the implementation of quantum technologies. In addition to the well-developed
approaches utilising optimal probe states and optimal measurements, the method
of optimal control can be used to identify time-dependent pulses applied to the
system to achieve higher precision, especially in the presence of noise. Here,
we extend optimally controlled estimation schemes for single qubits to
non-commuting dynamics as well as two interacting qubits, demonstrating
improvements in terms of maximal precision, time-stability, as well as
robustness over uncontrolled protocols.


title:
Hypothesis Testing in Sequentially Sampled Data: AdapRT to Maximize
  Power Beyond iid Sampling

abstract:
  Testing whether a variable of interest affects the outcome is one of the most
fundamental problems in statistics. To tackle this problem, the conditional
randomization test (CRT) is a design-based method that is widely used to test
the independence of a variable of interest (X) with an outcome (Y) holding some
controls (Z) fixed. The CRT relies solely on the random iid sampling of (X,Z)
to produce exact finite-sample p-values that are constructed using any test
statistic. We propose a new method, the adaptive randomization test (AdapRT),
that similarly tackles the independence problem but allows the data to be
sequentially sampled. Like the CRT, the AdapRT relies solely on knowing the
(adaptive) sampling distribution of (X,Z). In this paper, we additionally show
the significant power increase by adaptively sampling in two illustrative
settings. We first showcase the AdapRT in a particular multi-arm bandit problem
known as the normal-mean model. Under this setting, we theoretically
characterize the powers of both the iid sampling scheme and the AdapRT and
empirically find that the AdapRT can uniformly outperform the typical uniform
iid sampling scheme that pulls all arms with equal probability. We also
surprisingly find that the AdapRT can be more powerful than even the oracle iid
sampling scheme when the signal is relatively strong. We believe that the
proposed adaptive procedure is successful mainly because it stabilizes arms
that may initially look like "fake" signal. We additionally showcase the AdapRT
to a popular factorial survey design setting known as conjoint analysis and
find similar results through both simulations and application. Lastly, we also
provide a power analysis pipeline for practitioners to diagnosis the
effectiveness of their proposed adaptive procedures and apply the pipeline to
the two aforementioned settings.


title: Generalized Covariant Entropy Bound in Lanczos-Lovelock Gravity

abstract:
  In this paper, we investigate the generalized covariant entropy bound in the
theory where the Einstein gravity is perturbed by the higher-order Lovelock
terms. After replacing the Bekenstein-Hawking entropy with the Jacobson-Myers
entropy and introducing two reasonable physical assumptions, we showed that the
corresponding generalized covariant entropy bound is satisfied under a
higher-order approximation of the perturbation from the higher-order Lovelock
terms. Our result implies that the Jacobson-Myers entropy strictly obeys the
entropy bound under the perturbation level, and the generalized second law of
Lanczos-Lovelock gravity is also satisfied when the Einstein gravity is
perturbed by the higher-order Lovelock terms.


title:
A Unified Algorithm for Penalized Convolution Smoothed Quantile
  Regression

abstract:
  Penalized quantile regression (QR) is widely used for studying the
relationship between a response variable and a set of predictors under data
heterogeneity in high-dimensional settings. Compared to penalized least
squares, scalable algorithms for fitting penalized QR are lacking due to the
non-differentiable piecewise linear loss function. To overcome the lack of
smoothness, a recently proposed convolution-type smoothed method brings an
interesting tradeoff between statistical accuracy and computational efficiency
for both standard and penalized quantile regressions. In this paper, we propose
a unified algorithm for fitting penalized convolution smoothed quantile
regression with various commonly used convex penalties, accompanied by an
R-language package conquer available from the Comprehensive R Archive Network.
We perform extensive numerical studies to demonstrate the superior performance
of the proposed algorithm over existing methods in both statistical and
computational aspects. We further exemplify the proposed algorithm by fitting a
fused lasso additive QR model on the world happiness data.


title:
Eikonal quasinormal modes and photon orbits of deformed Schwarzschild
  black holes

abstract:
  The geometric optics approximation provides an interpretation for eikonal
correspondence that, in black-hole-containing spacetimes, connects
high-frequency black hole quasinormal modes with closed photon orbits around
said black hole. This correspondence has been identified explicitly for
Schwarzschild, Reissner-Nordstr\"om, Kerr, and Kerr-Newman black holes,
violation of which can be a potential hint toward physics beyond General
Relativity. Notably, the aforementioned black hole spacetimes have sufficient
symmetries such that both the geodesic equations and the master wave equations
are separable. The identification of the correspondence seems largely relies on
these symmetries. One naturally asks how the eikonal correspondence would
appear if the spacetime is less symmetric. As a pioneering work in this
direction, we consider in this paper a deformed Schwarzschild spacetime
retaining only axisymmetry and stationarity. We show that up to the first order
of spacetime deformations, the eikonal correspondence manifests through the
definition of the \textit{averaged} radius of trapped photon orbits along their
one period. This averaged radius overlaps the potential peak in the master wave
equation, which can be defined up to the first order of spacetime deformations,
allowing the explicit identification of the eikonal correspondence.


title: Robust quantum control for the manipulation of solid-state spins

abstract:
  Robust and high-fidelity control of electron spins in solids is the
cornerstone for facilitating applications of solid-state spins in quantum
information processing and quantum sensing. However, precise control of spin
systems is always challenging due to the presence of a variety of noises
originating from the environment and control fields. Herein, noise-resilient
quantum gates, designed with robust optimal control (ROC) algorithms, are
demonstrated experimentally with nitrogen-vacancy (NV) centers in diamond to
realize tailored robustness against detunings and Rabi errors simultaneously.
In the presence of both 10% off-resonant detuning and deviation of a Rabi
frequency, we achieve an average single-qubit gate fidelity of up to 99.97%.
Our experiments also show that, ROCbased multipulse quantum sensing sequences
can suppress spurious responses resulting from finite widths and imperfections
of microwave pulses, which provides an efficient strategy for enhancing the
performance of existing multipulse quantum sensing sequences.


title: FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs

abstract:
  Few-shot graph classification aims at predicting classes for graphs, given
limited labeled graphs for each class. To tackle the bottleneck of label
scarcity, recent works propose to incorporate few-shot learning frameworks for
fast adaptations to graph classes with limited labeled graphs. Specifically,
these works propose to accumulate meta-knowledge across diverse meta-training
tasks, and then generalize such meta-knowledge to the target task with a
disjoint label set. However, existing methods generally ignore task
correlations among meta-training tasks while treating them independently.
Nevertheless, such task correlations can advance the model generalization to
the target task for better classification performance. On the other hand, it
remains non-trivial to utilize task correlations due to the complex components
in a large number of meta-training tasks. To deal with this, we propose a novel
few-shot learning framework FAITH that captures task correlations via
constructing a hierarchical task graph at different granularities. Then we
further design a loss-based sampling strategy to select tasks with more
correlated classes. Moreover, a task-specific classifier is proposed to utilize
the learned task correlations for few-shot classification. Extensive
experiments on four prevalent few-shot graph classification datasets
demonstrate the superiority of FAITH over other state-of-the-art baselines.


title:
Effect of RGUP on the nonlinear Klein-Gordon model with spontaneous
  symmetry breaking

abstract:
  In the framework of the relativistic generalized uncertainty principle
(RGUP), we study the nonlinear Klein-Gordon field with $\phi^4$
self-interaction. This generalization comes from the quantum gravity theories
that predict the existence of a minimal measurable length scale. The quantum
gravity effects slightly modify the momentum and change the Lagrangian density
of the field. This model, specifically in the context of spontaneous symmetry
breaking, is so fruitful and leads us to many interesting phenomena. Due to the
complexity of the consequent equations, the model is normally studied via
perturbation mechanisms. However, here, we apply a generalized $tanh$-method
which supposed that the field solutions should be $sech$-functions of segments
of space and time. By this extended method, we find some more general solutions
with respect to our previous work \cite{Mir1}. Several solutions can be found
but we select only the normalizable and bounded solitary fields. The energy
spectrum of each field solution is obtained in an analytical form and
discussed. The modification parameter of the corresponding RGUP is estimated by
using the rest energy of the mass of the Higgs boson.


title:
Image Features Influence Reaction Time: A Learned Probabilistic
  Perceptual Model for Saccade Latency

abstract:
  We aim to ask and answer an essential question "how quickly do we react after
observing a displayed visual target?" To this end, we present psychophysical
studies that characterize the remarkable disconnect between human saccadic
behaviors and spatial visual acuity. Building on the results of our studies, we
develop a perceptual model to predict temporal gaze behavior, particularly
saccadic latency, as a function of the statistics of a displayed image.
Specifically, we implement a neurologically-inspired probabilistic model that
mimics the accumulation of confidence that leads to a perceptual decision. We
validate our model with a series of objective measurements and user studies
using an eye-tracked VR display. The results demonstrate that our model
prediction is in statistical alignment with real-world human behavior. Further,
we establish that many sub-threshold image modifications commonly introduced in
graphics pipelines may significantly alter human reaction timing, even if the
differences are visually undetectable. Finally, we show that our model can
serve as a metric to predict and alter reaction latency of users in interactive
computer graphics applications, thus may improve gaze-contingent rendering,
design of virtual experiences, and player performance in e-sports. We
illustrate this with two examples: estimating competition fairness in a video
game with two different team colors, and tuning display viewing distance to
minimize player reaction time.


title:
Uncertainty Minimization for Personalized Federated Semi-Supervised
  Learning

abstract:
  Since federated learning (FL) has been introduced as a decentralized learning
technique with privacy preservation, statistical heterogeneity of distributed
data stays the main obstacle to achieve robust performance and stable
convergence in FL applications. Model personalization methods have been studied
to overcome this problem. However, existing approaches are mainly under the
prerequisite of fully labeled data, which is unrealistic in practice due to the
requirement of expertise. The primary issue caused by partial-labeled condition
is that, clients with deficient labeled data can suffer from unfair performance
gain because they lack adequate insights of local distribution to customize the
global model. To tackle this problem, 1) we propose a novel personalized
semi-supervised learning paradigm which allows partial-labeled or unlabeled
clients to seek labeling assistance from data-related clients (helper agents),
thus to enhance their perception of local data; 2) based on this paradigm, we
design an uncertainty-based data-relation metric to ensure that selected
helpers can provide trustworthy pseudo labels instead of misleading the local
training; 3) to mitigate the network overload introduced by helper searching,
we further develop a helper selection protocol to achieve efficient
communication with negligible performance sacrifice. Experiments show that our
proposed method can obtain superior performance and more stable convergence
than other related works with partial labeled data, especially in highly
heterogeneous setting.