title:
Driver State Modeling through Latent Variable State Space Framework in
  the Wild

abstract:
  Analyzing the impact of the environment on drivers' stress level and workload
is of high importance for designing human-centered driver-vehicle interaction
systems and to ultimately help build a safer driving experience. However,
driver's state, including stress level and workload, are psychological
constructs that cannot be measured on their own and should be estimated through
sensor measurements such as psychophysiological measures. We propose using a
latent-variable state-space modeling framework for driver state analysis. By
using latent-variable state-space models, we model drivers' workload and stress
levels as latent variables estimated through multimodal human sensing data,
under the perturbations of the environment in a state-space format and in a
holistic manner. Through using a case study of multimodal driving data
collected from 11 participants, we first estimate the latent stress level and
workload of drivers from their heart rate, gaze measures, and intensity of
facial action units. We then show that external contextual elements such as the
number of vehicles as a proxy for traffic density and secondary task demands
may be associated with changes in driver's stress levels and workload. We also
show that different drivers may be impacted differently by the aforementioned
perturbations. We found out that drivers' latent states at previous timesteps
are highly associated with their current states. Additionally, we discuss the
utility of state-space models in analyzing the possible lag between the two
constructs of stress level and workload, which might be indicative of
information transmission between the different parts of the driver's
psychophysiology in the wild.


title: Riesz transform on exterior Lipschitz domains

abstract:
  Let $\mathscr{L}=-\text{div}A\nabla$ be a uniformly elliptic operator on
$\mathbb{R}^n$, $n\ge 2$. Let $\Omega$ be an exterior Lipschitz domain, let
$\mathscr{L}_D$ and $\mathscr{L}_N$ be the operator $\mathscr{L}$ on $\Omega$
subject to the Dirichlet and Neumann boundary values, respectively. We
establish characterization for the boundedness of the Riesz transforms
$\nabla\mathscr{L}_D^{-1/2}$, $\nabla \mathscr{L}_N^{-1/2}$ in the sharp range.
As a byproduct, we show the reverse inequality
$\|\mathscr{L}_D^{1/2}f\|_{L^p(\Omega)}\le C\|\nabla f\|_{L^p(\Omega)}$ holds
for any $1<p<\infty$. As applications, we deduce the sharp range for the
boundedness of the Riesz transforms, for operators with VMO coefficients, on
exterior Lipschitz and $C^1$ domains. Application to inhomogeneous Dirichlet
and Neumann problem is also presented. Our result is new even for the Dirichlet
and Neumann Laplacian on exterior Lipschitz and $C^1$ domains.


title:
CandidateDrug4Cancer: An Open Molecular Graph Learning Benchmark on Drug
  Discovery for Cancer

abstract:
  Anti-cancer drug discoveries have been serendipitous, we sought to present
the Open Molecular Graph Learning Benchmark, named CandidateDrug4Cancer, a
challenging and realistic benchmark dataset to facilitate scalable, robust, and
reproducible graph machine learning research for anti-cancer drug discovery.
CandidateDrug4Cancer dataset encompasses multiple most-mentioned 29 targets for
cancer, covering 54869 cancer-related drug molecules which are ranged from
pre-clinical, clinical and FDA-approved. Besides building the datasets, we also
perform benchmark experiments with effective Drug Target Interaction (DTI)
prediction baselines using descriptors and expressive graph neural networks.
Experimental results suggest that CandidateDrug4Cancer presents significant
challenges for learning molecular graphs and targets in practical application,
indicating opportunities for future researches on developing candidate drugs
for treating cancers.


title: Minimax rates for heterogeneous causal effect estimation

abstract:
  Estimation of heterogeneous causal effects - i.e., how effects of policies
and treatments vary across subjects - is a fundamental task in causal
inference, playing a crucial role in optimal treatment allocation,
generalizability, subgroup effects, and more. Many flexible methods for
estimating conditional average treatment effects (CATEs) have been proposed in
recent years, but questions surrounding optimality have remained largely
unanswered. In particular, a minimax theory of optimality has yet to be
developed, with the minimax rate of convergence and construction of
rate-optimal estimators remaining open problems. In this paper we derive the
minimax rate for CATE estimation, in a nonparametric model where distributional
components are Holder-smooth, and present a new local polynomial estimator,
giving high-level conditions under which it is minimax optimal. More
specifically, our minimax lower bound is derived via a localized version of the
method of fuzzy hypotheses, combining lower bound constructions for
nonparametric regression and functional estimation. Our proposed estimator can
be viewed as a local polynomial R-Learner, based on a localized modification of
higher-order influence function methods; it is shown to be minimax optimal
under a condition on how accurately the covariate distribution is estimated.
The minimax rate we find exhibits several interesting features, including a
non-standard elbow phenomenon and an unusual interpolation between
nonparametric regression and functional estimation rates. The latter quantifies
how the CATE, as an estimand, can be viewed as a regression/functional hybrid.
We conclude with some discussion of a few remaining open problems.


title: OmniFusion: 360 Monocular Depth Estimation via Geometry-Aware Fusion

abstract:
  A well-known challenge in applying deep-learning methods to omnidirectional
images is spherical distortion. In dense regression tasks such as depth
estimation, where structural details are required, using a vanilla CNN layer on
the distorted 360 image results in undesired information loss. In this paper,
we propose a 360 monocular depth estimation pipeline, OmniFusion, to tackle the
spherical distortion issue. Our pipeline transforms a 360 image into
less-distorted perspective patches (i.e. tangent images) to obtain patch-wise
predictions via CNN, and then merge the patch-wise results for final output. To
handle the discrepancy between patch-wise predictions which is a major issue
affecting the merging quality, we propose a new framework with the following
key components. First, we propose a geometry-aware feature fusion mechanism
that combines 3D geometric features with 2D image features to compensate for
the patch-wise discrepancy. Second, we employ the self-attention-based
transformer architecture to conduct a global aggregation of patch-wise
information, which further improves the consistency. Last, we introduce an
iterative depth refinement mechanism, to further refine the estimated depth
based on the more accurate geometric features. Experiments show that our method
greatly mitigates the distortion issue, and achieves state-of-the-art
performances on several 360 monocular depth estimation benchmark datasets.


title:
Multivariate doubly truncated moments for generalized skew-elliptical
  distributions with application to multivariate tail conditional risk measures

abstract:
  In this paper, we focus on multivariate doubly truncated first two moments of
generalized skew-elliptical (GSE) distributions and derive explicit expressions
for them.
  It includes many useful distributions, for examples, generalized skew-normal
(GSN), generalized skew-Laplace (GSLa), generalized skew-logistic (GSLo) and
generalized skew student-$t$ (GSSt) distributions, all as special cases. We
also give formulas of multivariate doubly truncated expectation and covariance
for GSE distributions. As applications, we show the results of multivariate
tail conditional expectation (MTCE) and multivariate tail covariance (MTCov)
for GSE distributions.


title: Flood hazard model calibration using multiresolution model output

abstract:
  Riverine floods pose a considerable risk to many communities. Improving the
projections of flood hazard has the potential to inform the design and
implementation of flood risk management strategies. Current flood hazard
projections are uncertain. One uncertainty that is often overlooked is
uncertainty about model parameters. Calibration methods use observations to
quantify model parameter uncertainty. With limited computational resources,
researchers typically calibrate models using either relatively few expensive
model runs at a high spatial resolution or many cheaper runs at a lower spatial
resolution. This leads to an open question: Is it possible to effectively
combine information from the high and low resolution model runs? We propose a
Gaussian process-based Bayesian emulation-calibration approach that assimilates
model outputs and observations at multiple resolutions. We demonstrate our
approach using the LISFLOOD-FP flood hazard model as a case study for a
riverine community in Pennsylvania in the Eastern United States. Compared to
considered existing single-resolution approaches, our method yields more
accurate flood predictions. Our method is rather general and can be applied to
calibrate other high dimensional computer models to help improve future
projections.


title:
A Zero Lyapunov Exponent in Genus $3$ Implies the Eierlegende
  Wollmilchsau

abstract:
  We prove that the closed orbit of the Eierlegende Wollmilchsau is the only
$\text{SL}(2,\mathbb{R})$-orbit closure in genus three with a zero Lyapunov
exponent in its Kontsevich-Zorich spectrum. The result recovers previous
partial results in this direction by Bainbridge-Habegger-M\"oller and the first
named author. The main new contribution is an understanding of the Forni
subspace along a degeneration toward the boundary of the moduli space of
curves. This results in a simple geometric criterion that excludes the
existence of a Forni subspace. Another key ingredient is the solution to the
jump problem from the work of Hu and the third named author.


title: Can 8-Year-Olds understand concepts of Einsteinian physics?

abstract:
  This paper reports the first study on the ability of Year 3 students (ages 7
to 8) to conceptualize Einsteinian concepts. This paper reports that children
aged 7-8 are not too young to learn key Einsteinian concepts, and the benefits
of reinforcement indicate that longer programs are needed for ideas to be
consolidated.


title:
X-Trans2Cap: Cross-Modal Knowledge Transfer using Transformer for 3D
  Dense Captioning

abstract:
  3D dense captioning aims to describe individual objects by natural language
in 3D scenes, where 3D scenes are usually represented as RGB-D scans or point
clouds. However, only exploiting single modal information, e.g., point cloud,
previous approaches fail to produce faithful descriptions. Though aggregating
2D features into point clouds may be beneficial, it introduces an extra
computational burden, especially in inference phases. In this study, we
investigate a cross-modal knowledge transfer using Transformer for 3D dense
captioning, X-Trans2Cap, to effectively boost the performance of single-modal
3D caption through knowledge distillation using a teacher-student framework. In
practice, during the training phase, the teacher network exploits auxiliary 2D
modality and guides the student network that only takes point clouds as input
through the feature consistency constraints. Owing to the well-designed
cross-modal feature fusion module and the feature alignment in the training
phase, X-Trans2Cap acquires rich appearance information embedded in 2D images
with ease. Thus, a more faithful caption can be generated only using point
clouds during the inference. Qualitative and quantitative results confirm that
X-Trans2Cap outperforms previous state-of-the-art by a large margin, i.e.,
about +21 and about +16 absolute CIDEr score on ScanRefer and Nr3D datasets,
respectively.


title:
Photon Ring Symmetries in Simulated Linear Polarization Images of
  Messier 87*

abstract:
  The Event Horizon Telescope (EHT) recently released the first linearly
polarized images of the accretion flow around the supermassive black hole
Messier 87*, hereafter \m{}. The spiraling polarization pattern found in EHT
images favored magnetically arrested disks (MADs) as the explanation for the
EHT image. With next-generation improvements to very long baseline
interferometry (VLBI) on the horizon, understanding similar polarized features
in the highly lensed structure known as the "photon ring," where photons make
multiple half-orbits about the black hole before reaching the observer, will be
critical to analysis of future images. Recent work has indicated that this
image region may be depolarized relative to more direct emission. We expand
this observation by decomposing photon half-orbits in the EHT library of
simulated images of the \m{} accretion system and find that images of MAD
simulations show a relative depolarization of the photon ring attributable to
destructive interference of oppositely spiraling electric field vectors; this
antisymmetry, which arises purely from strong gravitational lensing, can
produce up to ${\sim}50\%$ depolarization in the photon ring region with
respect to the direct image. In systems that are not magnetically arrested and
with the exception of systems with high spin and ions and electrons of equal
temperature, we find that highly lensed indirect sub-images are almost
completely depolarized, causing a modest depolarization of the photon ring
region in the complete image. We predict that next-generation EHT observations
of \m{} polarization should jointly constrain the black hole spin and the
underlying emission and magnetic field geometry.


title:
Can No-reference features help in Full-reference image quality
  estimation?

abstract:
  Development of perceptual image quality assessment (IQA) metrics has been of
significant interest to computer vision community. The aim of these metrics is
to model quality of an image as perceived by humans. Recent works in
Full-reference IQA research perform pixelwise comparison between deep features
corresponding to query and reference images for quality prediction. However,
pixelwise feature comparison may not be meaningful if distortion present in
query image is severe. In this context, we explore utilization of no-reference
features in Full-reference IQA task. Our model consists of both full-reference
and no-reference branches. Full-reference branches use both distorted and
reference images, whereas No-reference branch only uses distorted image. Our
experiments show that use of no-reference features boosts performance of image
quality assessment. Our model achieves higher SRCC and KRCC scores than a
number of state-of-the-art algorithms on KADID-10K and PIPAL datasets.


title: PUMA: Performance Unchanged Model Augmentation for Training Data Removal

abstract:
  Preserving the performance of a trained model while removing unique
characteristics of marked training data points is challenging. Recent research
usually suggests retraining a model from scratch with remaining training data
or refining the model by reverting the model optimization on the marked data
points. Unfortunately, aside from their computational inefficiency, those
approaches inevitably hurt the resulting model's generalization ability since
they remove not only unique characteristics but also discard shared (and
possibly contributive) information. To address the performance degradation
problem, this paper presents a novel approach called Performance Unchanged
Model Augmentation~(PUMA). The proposed PUMA framework explicitly models the
influence of each training data point on the model's generalization ability
with respect to various performance criteria. It then complements the negative
impact of removing marked data by reweighting the remaining data optimally. To
demonstrate the effectiveness of the PUMA framework, we compared it with
multiple state-of-the-art data removal techniques in the experiments, where we
show the PUMA can effectively and efficiently remove the unique characteristics
of marked training data without retraining the model that can 1) fool a
membership attack, and 2) resist performance degradation. In addition, as PUMA
estimates the data importance during its operation, we show it could serve to
debug mislabelled data points more efficiently than existing approaches.


title:
Berry phase in the phase space worldline representation: the axial
  anomaly and classical kinetic theory

abstract:
  The Berry phase is analyzed for Weyl and Dirac fermions in a phase space
representation of the worldline formalism. Kinetic theories are constructed for
both at a classical level. Whereas the Weyl fermion case reduces in dimension,
resembling a theory in quantum mechanics, the Dirac fermion case takes on a
manifestly Lorentz covariant form. To achieve a classical kinetic theory for
the non-Abelian Dirac fermion Berry phase a spinor construction of Barut and
Zanghi is utilized. The axial anomaly is also studied at a quantum level. It is
found that under an adiabatic approximation, which is necessary for
facilitating a classical kinetic theory, the index of the Dirac operator for
massless fermions vanishes. Even so, similarities of an axial rotation to an
exact non-covariant Berry phase transform are drawn by application of the
Fujikawa method to the Barut and Zanghi spinors on the worldline.


title:
Possible studies on generalized parton distributions and gravitational
  form factors in neutrino reactions

abstract:
  Spacelike and timelike generalized parton distributions (GPDs) have been
investigated in charged-lepton scattering and electron-positron collisions via
deeply virtual Compton scattering and two-photon processes, respectively.
Furthermore, we expect that hadron-accelerator-facility measurements will be
performed in future. The GPDs will play a crucial role in clarifying the
origins of hadron spins and masses in terms of quarks and gluons. It is also
possible to probe internal pressure within hadrons for understanding their
stability. Gravitational form factors of hadrons used to be considered as a
purely academic subject because gravitational interactions are too weak to be
measured in microscopic systems. However, due to the development of
hadron-tomography field, it became possible to extract the gravitational form
factors from the actual GPD measurements without relying on direct
gravitational interactions. Neutrino reactions can also be used for GPD studies
in future, for example, by using the Long-Baseline Neutrino Facility at
Fermilab. The neutrino GPD measurements are valuable especially for finding the
flavor dependence of the GPDs in a complementary way to the charged-lepton
experiments. We give an overview of the GPDs and discuss possible neutrino GPD
measurements using the single-pion production processes $\nu + N \to \ell^- +
N' + \pi$ and $\bar\nu + N \to \ell^+ + N' + \pi$.


title: Adversarially Robust Learning with Tolerance

abstract:
  We study the problem of tolerant adversarial PAC learning with respect to
metric perturbation sets. In adversarial PAC learning, an adversary is allowed
to replace a test point $x$ with an arbitrary point in a closed ball of radius
$r$ centered at $x$. In the tolerant version, the error of the learner is
compared with the best achievable error with respect to a slightly larger
perturbation radius $(1+\gamma)r$.
  For perturbation sets with doubling dimension $d$, we show that a variant of
the natural ``perturb-and-smooth'' algorithm PAC learns any hypothesis class
$\mathcal{H}$ with VC dimension $v$ in the $\gamma$-tolerant adversarial
setting with $O\left(\frac{v(1+1/\gamma)^{O(d)}}{\varepsilon}\right)$ samples.
This is the first such general guarantee with linear dependence on $v$ even for
the special case where the domain is the real line and the perturbation sets
are closed balls (intervals) of radius $r$. However, the proposed guarantees
for the perturb-and-smooth algorithm currently only hold in the tolerant robust
realizable setting and exhibit exponential dependence on $d$.
  We additionally propose an alternative learning method which yields sample
complexity bounds with only linear dependence on the doubling dimension even in
the more general agnostic case. This approach is based on sample compression.


title: Conceptual Modeling of Events Based on One-Category Ontology

abstract:
  In previous works, we proposed a one-category (entitled thimac) conceptual
model called a thinging machine (TM), which integrates staticity (e.g.,
objects) and dynamism (e.g., events) without losing valuable aspects of
diagrammatic intuition in conceptual modeling. We proposed applying TM to
conceptual modeling in software engineering (e.g., on or above the level of UML
as a conceptual modeling language). In this paper, to show such an application
in software engineering, we first present a complete high-level description of
a library service system to demonstrate the TM s applicability. Furthermore, we
explore the TM s features, emphasizing the realization of thimacs as events.
The purpose is to develop better understanding of the TM notions by contrasting
them with their uses in related fields. The notion of an event plays a
prominent role in many fields of study, including philosophy, linguistics,
literary theory, probability theory, artificial intelligence, physics, and
history. A TM event is a static thimac with a time breath (time subthimac) that
infuses dynamism into the thimac. It arises from how the TM static region is
infected with time. Such a view is contrasted with some philosophical and
linguistics definitions of an event (e.g., unit of experience of Whitehead). We
also raise interesting issues (e.g., event movement) in this study.


title:
Distributed Riemannian Optimization with Lazy Communication for
  Collaborative Geometric Estimation

abstract:
  We present the first distributed optimization algorithm with lazy
communication for collaborative geometric estimation, the backbone of modern
collaborative simultaneous localization and mapping (SLAM) and
structure-from-motion (SfM) applications. Our method allows agents to
cooperatively reconstruct a shared geometric model on a central server by
fusing individual observations, but without the need to transmit potentially
sensitive information about the agents themselves (such as their locations).
Furthermore, to alleviate the burden of communication during iterative
optimization, we design a set of communication triggering conditions that
enable agents to selectively upload local information that are useful to global
optimization. Our approach thus achieves significant communication reduction
with minimal impact on optimization performance. As our main theoretical
contribution, we prove that our method converges to first-order critical points
with a sublinear convergence rate. Numerical evaluations on bundle adjustment
problems from collaborative SLAM and SfM datasets show that our method performs
competitively against existing distributed techniques, while achieving up to
78% total communication reduction.


title: Preserving multi-level quantum coherence by dynamical decoupling

abstract:
  Quantum information processing with multi-level systems (qudits) provides
additional features and applications than the two-level systems. However,
qudits are more prone to dephasing and dynamical decoupling for qudits has
never been experimentally demonstrated. Here, as a proof-of-principle
demonstration, we experimentally apply dynamical decoupling to protect
superpositions with three levels of a trapped $^9\rm{Be}^+$ ion from ambient
noisy magnetic field, prolonging coherence by up to approximately an order of
magnitude. Our demonstration, straightforwardly scalable to more levels, may
open up a path toward long coherence quantum memory, metrology and information
processing with qudits.


title:
Transfer Learning of High-Fidelity Opacity Spectra in Autoencoders and
  Surrogate Models

abstract:
  Simulations of high energy density physics are expensive, largely in part for
the need to produce non-local thermodynamic equilibrium opacities.
High-fidelity spectra may reveal new physics in the simulations not seen with
low-fidelity spectra, but the cost of these simulations also scale with the
level of fidelity of the opacities being used. Neural networks are capable of
reproducing these spectra, but neural networks need data to to train them which
limits the level of fidelity of the training data. This paper demonstrates that
it is possible to reproduce high-fidelity spectra with median errors in the
realm of 3\% to 4\% using as few as 50 samples of high-fidelity Krypton data by
performing transfer learning on a neural network trained on many times more
low-fidelity data.


title: FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours

abstract:
  Protein structure prediction is an important method for understanding gene
translation and protein function in the domain of structural biology. AlphaFold
introduced the Transformer model to the field of protein structure prediction
with atomic accuracy. However, training and inference of the AlphaFold model
are time-consuming and expensive because of the special performance
characteristics and huge memory consumption. In this paper, we propose
FastFold, a highly efficient implementation of the protein structure prediction
model for training and inference. FastFold includes a series of GPU
optimizations based on a thorough analysis of AlphaFold's performance.
Meanwhile, with Dynamic Axial Parallelism and Duality Async Operation, FastFold
achieves high model parallelism scaling efficiency, surpassing existing popular
model parallelism techniques. Experimental results show that FastFold reduces
overall training time from 11 days to 67 hours and achieves 7.5-9.5X speedup
for long-sequence inference. Furthermore, We scaled FastFold to 512 GPUs and
achieved an aggregate of 6.02 PetaFLOPs with 90.1% parallel efficiency. The
implementation can be found at https://github.com/hpcaitech/FastFold


title: Reachability of turn sequences

abstract:
  A turn sequence of left and right turns is realized as a simple rectilinear
chain of integral segments whose turns at its bends are the same as the turn
sequence. The chain starts from the origin and ends at some point which we call
a reachable point of the turn sequence. We investigate the combinatorial and
geometric properties of the set of reachable points of a given turn sequence
such as the shape, connectedness, and sufficient and necessary conditions on
the reachability to the four signed axes. We also prove the upper and lower
bounds on the maximum distance from the origin to the closest reachable point
on signed axes for a turn sequence. The bounds are expressed in terms of the
difference between the number of left and right turns in the sequence as well
as, in certain cases, the length of the maximal monotone prefix or suffix of
the turn sequence. The bounds are exactly matched or tight within additive
constants for some signed axes.


title:
E-Polynomials of Generic $\text{GL}_n\rtimes\!<\!\sigma\!>\!~$-Character
  Varieties: Unbranched Case

abstract:
  For any unbranched double covering of compact Riemann surfaces, we study the
associated character varieties that are unitary in the global sense, which we
call $\text{GL}_n\rtimes\!<\!\sigma\!>\!~$-character varieties. We introduce
$k>0$ punctures on the surface, and restrict the monodromies around the
punctures to generic semi-simple conjugacy classes in $\text{GL}_n$, and
compute the E-polynomials of these character varieties using the character
table of $\text{GL}_n(q)$. The result is expressed as the inner product of
certain symmetric functions. We are then led to a conjectural formula for the
mixed Hodge polynomial, which is built out of (modified) Macdonald polynomials,
their self-pairings, and self-pairings of wreath Macdonald polynomials.


title: Yoneda Ext-algebras of Takeuchi smash products

abstract:
  We prove that the Yoneda Ext-algebra of a Takeuchi smash product is the
graded Takeuchi smash product of the Yoneda Ext-algebras of the two algebras or
modules involved. As an application, we prove that graded Takeuchi smash
products preserve Artin-Schelter regularity, and describe the Nakayama
automorphism of the product.


title:
Clean-Annotation Backdoor Attack against Lane Detection Systems in the
  Wild

abstract:
  We present the first backdoor attack against the lane detection systems in
the physical world. Modern autonomous vehicles adopt various deep learning
methods to train lane detection models, making it challenging to devise a
universal backdoor attack technique. In our solution, (1) we propose a novel
semantic trigger design, which leverages the traffic cones with specific poses
and locations to activate the backdoor. Such trigger can be easily realized
under the physical setting, and looks very natural not to be detected. (2) We
introduce a new clean-annotation approach to generate poisoned samples. These
samples have correct annotations but are still capable of embedding the
backdoor to the model. Comprehensive evaluations on public datasets and
physical autonomous vehicles demonstrate that our backdoor attack is effective,
stealthy and robust.


title:
MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose
  Estimation in Video

abstract:
  Recent transformer-based solutions have been introduced to estimate 3D human
pose from 2D keypoint sequence by considering body joints among all frames
globally to learn spatio-temporal correlation. We observe that the motions of
different joints differ significantly. However, the previous methods cannot
efficiently model the solid inter-frame correspondence of each joint, leading
to insufficient learning of spatial-temporal correlation. We propose MixSTE
(Mixed Spatio-Temporal Encoder), which has a temporal transformer block to
separately model the temporal motion of each joint and a spatial transformer
block to learn inter-joint spatial correlation. These two blocks are utilized
alternately to obtain better spatio-temporal feature encoding. In addition, the
network output is extended from the central frame to entire frames of the input
video, thereby improving the coherence between the input and output sequences.
Extensive experiments are conducted on three benchmarks (Human3.6M,
MPI-INF-3DHP, and HumanEva). The results show that our model outperforms the
state-of-the-art approach by 10.9% P-MPJPE and 7.6% MPJPE. The code is
available at https://github.com/JinluZhang1126/MixSTE.


title:
D^2ETR: Decoder-Only DETR with Computationally Efficient Cross-Scale
  Attention

abstract:
  DETR is the first fully end-to-end detector that predicts a final set of
predictions without post-processing. However, it suffers from problems such as
low performance and slow convergence. A series of works aim to tackle these
issues in different ways, but the computational cost is yet expensive due to
the sophisticated encoder-decoder architecture. To alleviate this issue, we
propose a decoder-only detector called D^2ETR. In the absence of encoder, the
decoder directly attends to the fine-fused feature maps generated by the
Transformer backbone with a novel computationally efficient cross-scale
attention module. D^2ETR demonstrates low computational complexity and high
detection accuracy in evaluations on the COCO benchmark, outperforming DETR and
its variants.


title: Styleverse: Towards Identity Stylization across Heterogeneous Domains

abstract:
  We propose a new challenging task namely IDentity Stylization (IDS) across
heterogeneous domains. IDS focuses on stylizing the content identity, rather
than completely swapping it using the reference identity. We use an effective
heterogeneous-network-based framework $Styleverse$ that uses a single
domain-aware generator to exploit the Metaverse of diverse heterogeneous faces,
based on the proposed dataset FS13 with limited data. FS13 means 13 kinds of
Face Styles considering diverse lighting conditions, art representations and
life dimensions. Previous similar tasks, \eg, image style transfer can handle
textural style transfer based on a reference image. This task usually ignores
the high structure-aware facial area and high-fidelity preservation of the
content. However, Styleverse intends to controllably create topology-aware
faces in the Parallel Style Universe, where the source facial identity is
adaptively styled via AdaIN guided by the domain-aware and reference-aware
style embeddings from heterogeneous pretrained models. We first establish the
IDS quantitative benchmark as well as the qualitative Styleverse matrix.
Extensive experiments demonstrate that Styleverse achieves higher-fidelity
identity stylization compared with other state-of-the-art methods.


title:
SEA: Bridging the Gap Between One- and Two-stage Detector Distillation
  via SEmantic-aware Alignment

abstract:
  We revisit the one- and two-stage detector distillation tasks and present a
simple and efficient semantic-aware framework to fill the gap between them. We
address the pixel-level imbalance problem by designing the category anchor to
produce a representative pattern for each category and regularize the
topological distance between pixels and category anchors to further tighten
their semantic bonds. We name our method SEA (SEmantic-aware Alignment)
distillation given the nature of abstracting dense fine-grained information by
semantic reliance to well facilitate distillation efficacy. SEA is well adapted
to either detection pipeline and achieves new state-of-the-art results on the
challenging COCO object detection task on both one- and two-stage detectors.
Its superior performance on instance segmentation further manifests the
generalization ability. Both 2x-distilled RetinaNet and FCOS with ResNet50-FPN
outperform their corresponding 3x ResNet101-FPN teacher, arriving 40.64 and
43.06 AP, respectively. Code will be made publicly available.


title: A Unifying Framework for Some Directed Distances in Statistics

abstract:
  Density-based directed distances -- particularly known as divergences --
between probability distributions are widely used in statistics as well as in
the adjacent research fields of information theory, artificial intelligence and
machine learning. Prominent examples are the Kullback-Leibler information
distance (relative entropy) which e.g. is closely connected to the omnipresent
maximum likelihood estimation method, and Pearson's chisquare-distance which
e.g. is used for the celebrated chisquare goodness-of-fit test. Another line of
statistical inference is built upon distribution-function-based divergences
such as e.g. the prominent (weighted versions of) Cramer-von Mises test
statistics respectively Anderson-Darling test statistics which are frequently
applied for goodness-of-fit investigations; some more recent methods deal with
(other kinds of) cumulative paired divergences and closely related concepts. In
this paper, we provide a general framework which covers in particular both the
above-mentioned density-based and distribution-function-based divergence
approaches; the dissimilarity of quantiles respectively of other statistical
functionals will be included as well. From this framework, we structurally
extract numerous classical and also state-of-the-art (including new)
procedures. Furthermore, we deduce new concepts of dependence between random
variables, as alternatives to the celebrated mutual information. Some
variational representations are discussed, too.

categories: math.ST cs.IT math.IT math.PR stat.ML stat.TH


title:
An Onsager-Machlup approach to the most probable transition pathway for
  a genetic regulatory network

abstract:
  We investigate a quantitative network of gene expression dynamics describing
the competence development in Bacillus subtilis. First, we introduce an
Onsager-Machlup approach to quantify the most probable transition pathway for
both excitable and bistable dynamics. Then, we apply a machine learning method
to calculate the most probable transition pathway via the Euler-Lagrangian
equation. Finally, we analyze how the noise intensity affects the transition
phenomena.


title:
Beyond Virtual Bazaar: How Social Commerce Promotes Inclusivity for the
  Traditionally Underserved Community in Chinese Developing Regions

abstract:
  The disadvantaged population is often underserved and marginalized in
technology engagement: prior works show they are generally more reluctant and
experience more barriers in adopting and engaging with mainstream technology.
Here, we contribute to the HCI4D and ICTD literature through a novel "counter"
case study on Chinese social commerce (e.g., Pinduoduo), which 1) first
prospers among the traditionally underserved community from developing regions
ahead of the more technologically advantaged communities, and 2) has been
heavily engaged by this community. Through 12 in-depth interviews with social
commerce users from the traditionally underserved community in Chinese
developing regions, we demonstrate how social commerce, acting as a "counter",
brings online the traditional offline socioeconomic lives the community has
lived for ages, fits into the community's social, cultural, and economic
context, and thus effectively promotes technology inclusivity. Our work
provides novel insights and implications for building inclusive technology for
the "next billion" population.


title:
Proper usage of Scherrer's and Guinier's formulas in X-ray analysis of
  size distribution in systems of monocrystalline CeO2 nanoparticles

abstract:
  In nowadays, X-ray diffraction and scattering phenomena are widely used as
analytical tools in the optimization and control of nanomaterial synthesizing
processes. In systems of monocrystalline nanoparticles with size distribution,
the physical meaning of size values as determined by using X-ray methods is
still controvertial. To answer such fundamental issue, series of virtual
nanoparticles with sizes ranging from 1 nm to 90 nm were generated and their
exact scattering power computed via pair distance distribution function.
Composed X-ray diffraction and scattering patterns from systems of virtual
nanoparticles demonstrated that diffraction and scattering phenomena see the
size distributions with different weightings. Therefore, combining both
phenomena leads to the determination of size distribution in the systems. In
practice, X-ray diffraction and small-angle scattering experiments were applied
to solve the size distributions in a series of samples of cubic ceria
nanoparticles, revealing a systematic size dispersion as a function of
synthesis parameters.


title:
Incremental Transformer Structure Enhanced Image Inpainting with Masking
  Positional Encoding

abstract:
  Image inpainting has made significant advances in recent years. However, it
is still challenging to recover corrupted images with both vivid textures and
reasonable structures. Some specific methods only tackle regular textures while
losing holistic structures due to the limited receptive fields of convolutional
neural networks (CNNs). On the other hand, attention-based models can learn
better long-range dependency for the structure recovery, but they are limited
by the heavy computation for inference with large image sizes. To address these
issues, we propose to leverage an additional structure restorer to facilitate
the image inpainting incrementally. The proposed model restores holistic image
structures with a powerful attention-based transformer model in a fixed
low-resolution sketch space. Such a grayscale space is easy to be upsampled to
larger scales to convey correct structural information. Our structure restorer
can be integrated with other pretrained inpainting models efficiently with the
zero-initialized residual addition. Furthermore, a masking positional encoding
strategy is utilized to improve the performance with large irregular masks.
Extensive experiments on various datasets validate the efficacy of our model
compared with other competitors. Our codes are released in
https://github.com/DQiaole/ZITS_inpainting.


title:
An Instance Space Analysis of Constrained Multi-Objective Optimization
  Problems

abstract:
  Multi-objective optimization problems with constraints (CMOPs) are generally
considered more challenging than those without constraints. This in part can be
attributed to the creation of infeasible regions generated by the constraint
functions, and/or the interaction between constraints and objectives. In this
paper, we explore the relationship between constrained multi-objective
evolutionary algorithms (CMOEAs) performance and CMOP instances characteristics
using Instance Space Analysis (ISA). To do this, we extend recent work focused
on the use of Landscape Analysis features to characterise CMOP. Specifically,
we scrutinise the multi-objective landscape and introduce new features to
describe the multi-objective-violation landscape, formed by the interaction
between constraint violation and multi-objective fitness. Detailed evaluation
of problem-algorithm footprints spanning six CMOP benchmark suites and fifteen
CMOEAs, illustrates that ISA can effectively capture the strength and weakness
of the CMOEAs. We conclude that two key characteristics, the isolation of
non-dominate set and the correlation between constraints and objectives
evolvability, have the greatest impact on algorithm performance. However, the
current benchmarks problems do not provide enough diversity to fully reveal the
efficacy of CMOEAs evaluated.


title:
Hybrid Optimized Deep Convolution Neural Network based Learning Model
  for Object Detection

abstract:
  Object identification is one of the most fundamental and difficult issues in
computer vision. It aims to discover object instances in real pictures from a
huge number of established categories. In recent years, deep learning-based
object detection techniques that developed from computer vision have grabbed
the public's interest. Object recognition methods based on deep learning
frameworks have quickly become a popular way to interpret moving images
acquired by various sensors. Due to its vast variety of applications for
various computer vision tasks such as activity or event detection,
content-based image retrieval, and scene understanding, academics have spent
decades attempting to solve this problem. With this goal in mind, a unique deep
learning classification technique is used to create an autonomous object
detecting system. The noise destruction and normalising operations, which are
carried out using gaussian filter and contrast normalisation techniques,
respectively, are the first steps in the study activity. The pre-processed
picture is next subjected to entropy-based segmentation algorithms, which
separate the image's significant areas in order to distinguish between distinct
occurrences. The classification challenge is completed by the suggested Hybrid
Optimized Dense Convolutional Neural Network (HODCNN). The major goal of this
framework is to aid in the precise recognition of distinct items from the
gathered input frames. The suggested system's performance is assessed by
comparing it to existing machine learning and deep learning methodologies. The
experimental findings reveal that the suggested framework has a detection
accuracy of 0.9864, which is greater than current techniques. As a result, the
suggested object detection model outperforms other current methods.


title: Faith-Shap: The Faithful Shapley Interaction Index

abstract:
  Shapley values, which were originally designed to assign attributions to
individual players in coalition games, have become a commonly used approach in
explainable machine learning to provide attributions to input features for
black-box machine learning models. A key attraction of Shapley values is that
they uniquely satisfy a very natural set of axiomatic properties. However,
extending the Shapley value to assigning attributions to interactions rather
than individual players, an interaction index, is non-trivial: as the natural
set of axioms for the original Shapley values, extended to the context of
interactions, no longer specify a unique interaction index. Many proposals thus
introduce additional less "natural" axioms, while sacrificing the key axiom of
efficiency, in order to obtain unique interaction indices. In this work, rather
than introduce additional conflicting axioms, we adopt the viewpoint of Shapley
values as coefficients of the most faithful linear approximation to the
pseudo-Boolean coalition game value function. By extending linear to
$\ell$-order polynomial approximations, we can then define the general family
of faithful interaction indices}. We show that by additionally requiring the
faithful interaction indices to satisfy interaction-extensions of the standard
individual Shapley axioms (dummy, symmetry, linearity, and efficiency), we
obtain a unique FaithfulShapley Interaction index, which we denote Faith-Shap,
as a natural generalization of the Shapley value to interactions. We then
provide some illustrative contrasts of Faith-Shap with previously proposed
interaction indices, and further investigate some of its interesting algebraic
properties. We further show the computational efficiency of computing
Faith-Shap, together with some additional qualitative insights, via some
illustrative experiments.


title: Dense Voxel Fusion for 3D Object Detection

abstract:
  Camera and LiDAR sensor modalities provide complementary appearance and
geometric information useful for detecting 3D objects for autonomous vehicle
applications. However, current fusion models underperform state-of-art
LiDAR-only methods on 3D object detection benchmarks. Our proposed solution,
Dense Voxel Fusion (DVF) is a sequential fusion method that generates
multi-scale multi-modal dense voxel feature representations, improving
expressiveness in low point density regions. To enhance multi-modal learning,
we train directly with ground truth 2D bounding box labels, avoiding noisy,
detector-specific, 2D predictions. Additionally, we use LiDAR ground truth
sampling to simulate missed 2D detections and to accelerate training
convergence. Both DVF and the multi-modal training approaches can be applied to
any voxel-based LiDAR backbone without introducing additional learnable
parameters. DVF outperforms existing sparse fusion detectors, ranking $1^{st}$
among all published fusion methods on KITTI's 3D car detection benchmark at the
time of submission and significantly improves 3D vehicle detection performance
of voxel-based methods on the Waymo Open Dataset. We also show that our
proposed multi-modal training strategy results in better generalization
compared to training using erroneous 2D predictions.


title: Centralized Fairness for Redistricting

abstract:
  In representative democracy, the electorate is often partitioned into
districts with each district electing a representative. However, these systems
have proven vulnerable to the practice of partisan gerrymandering which
involves drawing districts that elect more representatives from a given
political party. Additionally, computer-based methods have dramatically
enhanced the ability to draw districts that drastically favor one party over
others. On the positive side, researchers have recently developed tools for
measuring how gerrymandered a redistricting map is by comparing it to a large
set of randomly-generated district maps.
  While these efforts to test whether a district map is "gerrymandered" have
achieved real-world impact, the question of how best to draw districts remains
very open. Many attempts to automate the redistricting process have been
proposed, but not adopted into practice. Typically, they have focused on
optimizing certain properties (e.g., geographical compactness or partisan
competitiveness of districts) and argued that the properties are desirable.
  In this work, we take an alternative approach which seeks to find the most
"typical" redistricting map. More precisely, we introduce a family of
well-motivated distance measures over redistricting maps. Then, by generating a
large collection of maps using sampling techniques, we select the map which
minimizes the sum of the distances from the collection, i.e., the most
"central" map. We produce scalable, linear-time algorithms and derive sample
complexity guarantees. Empirically, we show the validity of our algorithms over
real world redistricting problems.


title:
Spatial entanglement between two quantum walkers with exchange symmetric
  coins

abstract:
  We investigate the dynamics of the spatial entanglement between two initially
independent walkers that perform discrete-time quantum walk with respect to
symmetric and anti-symmetric initial coin states. By considering the symmetry
associated with the setting and post-selecting the states of the two coins
accordingly, we show that, for the anti-symmetric initial coin state, the
entanglement corresponding to all the anti-symmetric results are always
constant, while that corresponding to the symmetric result is underdamped-like
with period of oscillation ($T$) being inversely proportional to the coin
operator parameter ($\theta$). The results are the opposite way around for the
case of symmetric initial coin states. We also discover that the
underdamped-like behaviour of the spatial entanglement show up in the cases of
both initial coin states when the post-selected results have no symmetry. Our
findings reveal some interesting aspects of symmetry and entanglement dynamics
in quantum walk, which may be useful for applications in quantum communication
and other quantum technology.


title:
Follow your Nose: Using General Value Functions for Directed Exploration
  in Reinforcement Learning

abstract:
  Exploration versus exploitation dilemma is a significant problem in
reinforcement learning (RL), particularly in complex environments with large
state space and sparse rewards. When optimizing for a particular goal, running
simple smaller tasks can often be a good way to learn additional information
about the environment. Exploration methods have been used to sample better
trajectories from the environment for improved performance while auxiliary
tasks have been incorporated generally where the reward is sparse. If there is
little reward signal available, the agent requires clever exploration
strategies to reach parts of the state space that contain relevant sub-goals.
However, that exploration needs to be balanced with the need for exploiting the
learned policy. This paper explores the idea of combining exploration with
auxiliary task learning using General Value Functions (GVFs) and a directed
exploration strategy. We provide a simple way to learn options (sequences of
actions) instead of having to handcraft them, and demonstrate the performance
advantage in three navigation tasks.


title:
Learning Object Relations with Graph Neural Networks for Target-Driven
  Grasping in Dense Clutter

abstract:
  Robots in the real world frequently come across identical objects in dense
clutter. When evaluating grasp poses in these scenarios, a target-driven
grasping system requires knowledge of spatial relations between scene objects
(e.g., proximity, adjacency, and occlusions). To efficiently complete this
task, we propose a target-driven grasping system that simultaneously considers
object relations and predicts 6-DoF grasp poses. A densely cluttered scene is
first formulated as a grasp graph with nodes representing object geometries in
the grasp coordinate frame and edges indicating spatial relations between the
objects. We design a Grasp Graph Neural Network (G2N2) that evaluates the grasp
graph and finds the most feasible 6-DoF grasp pose for a target object.
Additionally, we develop a shape completion-assisted grasp pose sampling method
that improves sample quality and consequently grasping efficiency. We compare
our method against several baselines in both simulated and real settings. In
real-world experiments with novel objects, our approach achieves a 77.78%
grasping accuracy in densely cluttered scenarios, surpassing the
best-performing baseline by more than 15%. Supplementary material is available
at https://sites.google.com/umn.edu/graph-grasping.


title:
On the Dynamics of Imploding and Exploding Spherical Shock Wave Inside a
  Shock Tube

abstract:
  The present work aims to study the phenomenon of shock wave focusing and the
effect of viscosity in it. The focusing is achieved with a shock tube and a
converging section attached to it. The converging section transforms the planar
shock into a spherical shock and focuses it into a confined area. A shock of an
initial strength Ms=2.94 has been chosen for the present studies. A detailed
numerical study of the focusing region shows the formation of a mushroom-shaped
structure behind the reflected shock and vortex formation. This was visualised
through numerical shadowgraph images and by tracing the streamlines in the flow
field. A study on the variation in temperature is carried out in order to have
a quantitative assessment. It was found that the temperature inside the
mushroom structure is higher than that behind the reflected shock. The study of
species mass fraction in this region is also made. The flow inside the mushroom
structure was found to be a reactive mixture of gas slug.


title: Chiral-coupling-assisted Refrigeration in Trapped Ions

abstract:
  The tapped ions can be cooled close to their motional ground state, which is
imperative in implementing quantum computation and quantum simulation. Here we
demonstrate the capability of light-mediated chiral couplings between ions,
which enables a superior cooling scheme exceeding the single-ion limit of
sideband cooling. We present the chiral-coupling-assisted refrigeration in the
target ion at the price of heating the others under asymmetric drivings, where
its steady-state phonon occupation outperforms the lower bound set by a single
ion. We further locate the optimal operation condition of the refrigeration and
identify the parameter region where a faster rate of cooling emerges. Under an
additional nonguided decay channel, the heating effect in the reciprocal
coupling regime becomes suppressed and turns into cooling instead. Our results
present a resource of collective chiral couplings which help surpass the
bottleneck of cooling procedure in applications of trapped-ion-based quantum
computer and simulator.


title: The First Insight-HXMT Gamma-Ray Burst Catalog: The First Four Years

abstract:
  The Hard X-ray Modulation Telescope (Insight-HXMT), is China's first X-ray
astronomy satellite launched on June 15, 2017. The anti-coincidence CsI
detectors of the High Energy X-ray telescope (HE) onboard Insight-HXMT could
serve as an all-sky gamma-ray monitor in about 0.2-3 MeV. In its first four
years of operation, Insight-HXMT has detected 322 Gamma-Ray Bursts (GRBs) by
offline search pipeline including blind search and targeted search. For the
GOLDEN sample of Insight-HXMT GRBs, joint analyses were performed with other
GRB missions, including Fermi Gamma-ray Burst Monitor (Fermi/GBM), Swift Burst
Alert Telescope (Swift/BAT) and Gravitational wave high-energy Electromagnetic
Counterpart All-sky Monitor (GECAM). It shows that Insight-HXMT can provide
better constraint on GRB spectrum at higher energy band. The properties of
Insight-HXMT GRBs are reported in detail, including their trigger time,
duration, spectral parameters, peak fluxes of different time scales and
fluence. This catalog is an official product of the Insight-HXMT GRB team.


title:
Ultrasound Triggering of Rayleigh-Taylor Instability: Solution of
  Compressible Navier-Stokes Equation by a Non-Overlapping Parallel Compact
  Scheme

abstract:
  Rayleigh-Taylor instability (RTI) occurs at the interface of two media when
the heavier fluid is accelerated into the lighter fluid and is a prototypical
hydrodynamic event present in many physical events. In high energy physics,
this manifests itself across a wide range of length scales from nuclear
confinement fusion at micron-scale to supernova explosion at terra scales. RTI
can also be viewed as a baroclinic instability prevalent in engineering,
geophysics, and astrophysics, a pedagogic description of which is given in
Sengupta {\it et al., Comput. Fluids,} {\bf 225}, 104995 (2021) with respect to
the experimental results of Read, {\it Physica D}, {\bf 12} 45-58 (1984). Here,
a recently proposed non-overlapping parallel algorithm is used to solve this
three-dimensional canonical problem, having the unique property of not
distinguishing between sequential and parallel computing, using 4.19 billion
points and a refined time step of $7.69 \times 10^{-8} sec$. The problem
achieves the required density gradient by considering two volumes of air at
different temperatures (with a temperature difference of 200K) separated by a
non-conducting, impermeable partition at the onset of the experiment, which is
removed impulsively at $t=0$. The resulting buoyancy force at the interface
acting from top to bottom is the seed of the baroclinic instability. Present
high precision computation enables one to capture the ensuing RTI triggered by
ultrasonic waves created at the interface.


title: A Two-Zone Model as origin of Hard TeV Spectrum in Extreme BL Lacs

abstract:
  The emission of the so-called extreme BL Lacs poses challenges to the
particle acceleration models. The hardness of their spectrum, $\lesssim 2$, in
the high-energy band demands unusual parameters using the standard one-zone
synchrotron self-Compton (SSC) model with a deficient magnetized plasma. Some
authors use either two-zone or hadronic/lepto-hadronic models to relax these
atypical values. In this work, we present a lepto-hadronic two-zone model to
explain the multi-wavelength observations of the six best-known
\textit{extreme} BL Lacs. The very-high-energy gamma-ray observations are
described by the photo-hadronic processes in a blob close to the AGN core and
by SSC and external inverse Compton-processes in an outer blob. The
photo-hadronic interactions occur when accelerated protons in the inner blob
interact with annihilation line photons from a sub-relativistic pair plasma.
The X-ray observations are described by synchrotron radiation from the outer
blob. The parameter values found from the description of the spectral energy
distribution for each object with our phenomenological model are similar to
each other, and lie in the typical range reported in BL Lacs.


title:
The symbiotic and bipolar nebula M2-9: Morphological variability of the
  collimated ionized wind arising from the core

abstract:
  We studied the central region of bipolar nebula M\,2-9 using radio-continuum
observations obtained from the Jansky Very Large Array (JVLA) and the Atacama
Large Millimeter Array (ALMA) interferometers. This work presents new images at
$\sim$ 43 GHz ($\sim$ 7.0 mm) and $\sim$ 345 GHz ($\sim$ 0.9 mm) with angular
resolutions of $\sim$ 0$\rlap{.}^{\prime\prime}$047 and
0$\rlap{.}^{\prime\prime}$09, respectively. The continuum emission at $\sim$ 43
GHz shows an elongated jet-like structure perpendicular to the $\sim$ 345 GHz
observation. We conclude that both emissions could correspond to tracing an
isothermal collimated fast wind with constant expansion velocity and being
driven by the circumstellar ring traced by ALMA. Although this configuration
has been discussed within the scope of planetary nebulae models, there is a
remarkable fact: the collimated fast wind shows morphological spatial
variability. This supports the idea of a symbiotic binary system within the
innermost part of M\,2-9, which would be composed of a white dwarf and an AGB
star. The latter could explain the mirror symmetry observed at larger scales
due to their orbital motion.


title:
The Hardness-intensity Correlation of Photospheric Emission from a
  Structured Jet for Gamma-Ray Bursts

abstract:
  For many gamma-ray bursts (GRBs), hardness-intensity correlation (HIC) can be
described by a power-law function, $E_{\rm p}\propto F^{\kappa}$, where $E_{\rm
p}$ is the peak energy of $\nu F_{\nu}$ spectrum, and $F$ is the instantaneous
energy flux. In this paper, HIC of the non-dissipative photospheric emission
from a structured jet is studied in different regimes. An intermediate
photosphere, which contains both of unsaturated and saturated emissions is
introduced, and we find positive $\kappa<1/4$ in this case. The same conclusion
could be generalized to the photospheric emission from a hybrid jet without
magnetic dissipations, or that with sub-photospheric magnetic dissipations and
fully thermalized. This may imply that the contribution peaking at $\sim1/2$ in
the distribution of observed $\kappa$ are mainly from the prompt emission of
GRBs with synchrotron origin. Besides, emissions of the intermediate
photosphere could give a smaller low-energy photon index $\alpha$ than that in
the unsaturated regime, and naturally reproduce anti-correlation in
$\alpha-E_{\rm p}$ in a GRB pulse.


title:
Frequency stabilization of a 739 nm laser to an $I_2$ spectrum for
  trapped Ytterbium ions

abstract:
  We report on the frequency stabilization of a 739 nm Ti:sapphire laser to a
hyperfine component of the $^{127}I_{2}$ B(1)-X(11) P(70) transition using
acousto-optic modulation transfer spectroscopy (MTS). A frequency stability of
$3.83\times 10^{-11}$ around 13 s averaging time is achieved when the laser
frequency is stabilized. The observed hyperfine transition of the molecular
iodine is an ideal frequency reference for locking the lasers used in
experiments with trapped ytterbium ions, since its second harmonic frequency is
the $^{2}S_{\frac{1}{2}}-^{2}P_{\frac{1}{2}}$ transition of the ytterbium ion
at 369.5 nm. By investigating the line broadening effects due to the iodine
vapor pressure and laser power, the locking is optimized to the theoretical
signal to noise ratio (TSNR) of this iodine transition.


title:
Strong and tunable magnetoelectric coupling in 2D trilayer
  heterostructures

abstract:
  The quest for electric-field control of nanoscale magnetic states such as
skyrmions, which would impact the field of spintronics, has led to a
challenging search for multiferroic materials or structures with strong
magnetoelectric coupling and efficient electric-field control. Here we report a
theoretical prediction that such phenomena can be realized in two-dimensional
(2D) bilayer FE/PMM and trilayer FE/PMM/FE heterostructures (two-terminal and
three-terminal devices), where FE is a 2D ferroelectric and PMM is a polar
magnetic metal with strong spin-orbit coupling. Such a PMM has strong
Dzyaloshinskii-Moriya interactions (DMI) that can generate skyrmions, while the
FE can generate strong magnetoelectric coupling through
polarization-polarization interactions. In trilayer heterostructures, contact
to the metallic PMM layer enables multiple polarization configurations for
electric-field control of skyrmions. We report density-functional-theory
calculations for particular material choices that demonstrate the effectiveness
of these arrangements, with the key driver being the polarization-polarization
interactions between the PMM and FE layers. The present findings provide a
method to achieve strong magnetoelectric coupling in the 2D limit and a new
perspective for the design of related spintronics.


title:
A Learning Based Framework for Handling Uncertain Lead Times in
  Multi-Product Inventory Management

abstract:
  Most existing literature on supply chain and inventory management consider
stochastic demand processes with zero or constant lead times. While it is true
that in certain niche scenarios, uncertainty in lead times can be ignored, most
real-world scenarios exhibit stochasticity in lead times. These random
fluctuations can be caused due to uncertainty in arrival of raw materials at
the manufacturer's end, delay in transportation, an unforeseen surge in
demands, and switching to a different vendor, to name a few. Stochasticity in
lead times is known to severely degrade the performance in an inventory
management system, and it is only fair to abridge this gap in supply chain
system through a principled approach. Motivated by the recently introduced
delay-resolved deep Q-learning (DRDQN) algorithm, this paper develops a
reinforcement learning based paradigm for handling uncertainty in lead times
(\emph{action delay}). Through empirical evaluations, it is further shown that
the inventory management with uncertain lead times is not only equivalent to
that of delay in information sharing across multiple echelons
(\emph{observation delay}), a model trained to handle one kind of delay is
capable to handle delays of another kind without requiring to be retrained.
Finally, we apply the delay-resolved framework to scenarios comprising of
multiple products subjected to stochasticity in lead times, and elucidate how
the delay-resolved framework negates the effect of any delay to achieve
near-optimal performance.


title: Holographic quantum simulation of entanglement renormalization circuits

abstract:
  While standard approaches to quantum simulation require a number of qubits
proportional to the number of simulated particles, current noisy quantum
computers are limited to tens of qubits. With the technique of holographic
quantum simulation, a $D$-dimensional system can be simulated with a $D{\rm
-}1$-dimensional subset of qubits, enabling the study of systems significantly
larger than current quantum computers. Using circuits derived from the
multiscale entanglement renormalization ansatz (MERA), we accurately prepare
the ground state of an $L=32$ critical, non-integrable perturbed Ising model
and measure long-range correlations on the 10 qubit Quantinuum trapped ion
computer. We introduce generalized MERA (gMERA) networks that interpolate
between MERA and matrix product state networks and demonstrate that gMERA can
capture far longer correlations than a MERA with the same number of qubits, at
the expense of greater circuit depth. Finally, we perform noisy simulations of
these two network ans\"atze and find that the optimal choice of network depends
on noise level, available qubits, and the state to be represented.


title: Sampling Random Group Fair Rankings

abstract:
  In this paper, we consider the problem of randomized group fair ranking that
merges given ranked list of items from different sensitive demographic groups
while satisfying given lower and upper bounds on the representation of each
group in the top ranks. Our randomized group fair ranking formulation works
even when there is implicit bias, incomplete relevance information, or when
only ordinal ranking is available instead of relevance scores or utility
values.
  We take an axiomatic approach and show that there is a unique distribution
$\mathcal{D}$ to sample a random group fair ranking that satisfies a natural
set of consistency and fairness axioms. Moreover, $\mathcal{D}$ satisfies
representation constraints for every group at every rank, a characteristic that
cannot be satisfied by any deterministic ranking. We propose three algorithms
to sample a random group fair ranking from $\mathcal{D}$. Our first algorithm
samples rankings from $\mathcal{D}$ exactly, in time exponential in the number
of groups. Our second algorithm samples random group fair rankings from
$\mathcal{D}$ exactly and is faster than the first algorithm when the gap
between upper and lower bounds on the representation for each group is small.
Our third algorithm samples rankings from a distribution $\epsilon$-close to
$\mathcal{D}$ in total variation distance, and has expected running time
polynomial in all input parameters and $1/\epsilon$ when there is a large gap
between upper and lower bound representation constraints for all the groups. We
experimentally validate the above guarantees of our algorithms for group
fairness in top ranks and representation in every rank on real-world data sets.


title:
Towards Contextual Spelling Correction for Customization of End-to-end
  Speech Recognition Systems

abstract:
  Contextual biasing is an important and challenging task for end-to-end
automatic speech recognition (ASR) systems, which aims to achieve better
recognition performance by biasing the ASR system to particular context phrases
such as person names, music list, proper nouns, etc. Existing methods mainly
include contextual LM biasing and adding bias encoder into end-to-end ASR
models. In this work, we introduce a novel approach to do contextual biasing by
adding a contextual spelling correction model on top of the end-to-end ASR
system. We incorporate contextual information into a sequence-to-sequence
spelling correction model with a shared context encoder. Our proposed model
includes two different mechanisms: autoregressive (AR) and non-autoregressive
(NAR). We propose filtering algorithms to handle large-size context lists, and
performance balancing mechanisms to control the biasing degree of the model. We
demonstrate the proposed model is a general biasing solution which is
domain-insensitive and can be adopted in different scenarios. Experiments show
that the proposed method achieves as much as 51% relative word error rate (WER)
reduction over ASR system and outperforms traditional biasing methods. Compared
to the AR solution, the proposed NAR model reduces model size by 43.2% and
speeds up inference by 2.1 times.


title:
Nature's nonlocality must be boundlessly multipartite: an experimental
  demonstration under strict locality condition

abstract:
  Nonlocality captures one of the counter-intuitive features of Nature that
defies classical intuition. Recent investigation reveals that our physical
world is boundlessly multipartite nonlocal, i.e., there are multipartite
correlations in nature that cannot be reproduced by sharing fewer-partite
nonlocal resources even with globally shared randomness. Here we report an
experimental demonstration of genuine tripartite nonlocality in network under
strict locality constraints, which are ensured by space-like separating all
relevant events and employing fast quantum random number generators and
high-speed polarization measurements. In particular, for a photonic quantum
triangular network we observe a locality-loophole free violation of the
Bell-type inequality by 7.57 standard deviations for a tripartite GHZ state of
fidelity $(93.05\pm0.25)\%$. Our result convincingly disproves the possibility
of simulating genuine tripartite nonlocality by bipartite nonlocal resources
and unlimited shared randomness, confirming that Nature must be fundamentally
boundlessly multipartite.


title: Viscoelasticity and Rheological Hysteresis

abstract:
  Rheological characterization of complex fluids subjected to cyclic shear-rate
sweep often exhibits hysteresis. Since both viscoelastic and thixotropic
materials show hysteresis loops, it is important to understand distinguishing
features (if any) in the same shown by either. Lately, there has been
substantial work that attempts to relate the area enclosed by the hysteresis
loop with the manner in which shear rate is varied in the cycle, in order to
infer thixotropic parameters of a material. In this work, we study the Giesekus
model, which is a standard non-linear viscoelastic model, under application of
cyclic shear-rate sweep. We find that this model produces each type of
hysteresis loop that has hitherto been ascribed to thixotropic materials. We
also show that the area of the hysteresis loop for a viscoelastic material as a
function of sweep rate shows bell-shaped/bi-modal curves as also observed for
the thixotropic materials. This study illustrates that caution needs to be
exercised before attributing hysteresis loops and associated features observed
in a material exclusively to thixotropy. Another feature related to the
hysteresis loop is the occurrence of shear banding instability. We find that
viscoelastic hysteresis may not have any connection to shear banding
instability.


title:
Providing A Compiler Technology-Based Alternative For Big Data
  Application Infrastructures

abstract:
  The unprecedented growth of data volumes has caused traditional approaches to
computing to be re-evaluated. This has started a transition towards the use of
very large-scale clusters of commodity hardware and has given rise to the
development of many new languages and paradigms for data processing and
analysis. In this paper, we propose a compiler technology-based alternative to
the development of many different Big Data application infrastructures. Key to
this approach is the development of a single intermediate representation that
enables the integration of compiler optimization and query optimization, and
the re-use of many traditional compiler techniques for parallelization such as
data distribution and loop scheduling. We show how the single intermediate can
act as a generic intermediate for Big Data languages by mapping SQL and
MapReduce onto this intermediate.


title:
Joint Localization and Orientation Estimation in Millimeter-Wave MIMO
  OFDM Systems via Atomic Norm Minimization

abstract:
  Herein, an atomic norm based method for accurately estimating the location
and orientation of a target from millimeter-wave multi-input-multi-output
(MIMO) orthogonal frequency-division multiplexing (OFDM) signals is presented.
A novel virtual channel matrix is introduced and an algorithm to extract
localization-relevant channel parameters from its atomic norm decomposition is
designed. Then, based on the extended invariance principle, a weighted least
squares problem is proposed to accurately recover the location and orientation
using both line-of-sight and non-line-of-sight channel information. The
conditions for the optimality and uniqueness of the estimate and theoretical
guarantees for the estimation error are characterized for the noiseless and the
noisy scenarios. Theoretical results are confirmed via simulation. Numerical
results investigate the robustness of the proposed algorithm to incorrect model
order selection or synchronization error, and highlight performance
improvements over a prior method. The resultant performance nearly achieves the
Cramer-Rao lower bound on the estimation error.


title:
FAST-LIVO: Fast and Tightly-coupled Sparse-Direct LiDAR-Inertial-Visual
  Odometry

abstract:
  To achieve accurate and robust pose estimation in Simultaneous Localization
and Mapping (SLAM) task, multi-sensor fusion is proven to be an effective
solution and thus provides great potential in robotic applications. This paper
proposes FAST-LIVO, a fast LiDAR-Inertial-Visual Odometry system, which builds
on two tightly-coupled and direct odometry subsystems: a VIO subsystem and a
LIO subsystem. The LIO subsystem registers raw points (instead of feature
points on e.g., edges or planes) of a new scan to an incrementally-built point
cloud map. The map points are additionally attached with image patches, which
are then used in the VIO subsystem to align a new image by minimizing the
direct photometric errors without extracting any visual features (e.g., ORB or
FAST corner features). To further improve the VIO robustness and accuracy, a
novel outlier rejection method is proposed to reject unstable map points that
lie on edges or are occluded in the image view. Experiments on both open data
sequences and our customized device data are conducted. The results show our
proposed system outperforms other counterparts and can handle challenging
environments at reduced computation cost. The system supports both multi-line
spinning LiDARs and emerging solid-state LiDARs with completely different
scanning patterns, and can run in real-time on both Intel and ARM processors.
We open source our code and dataset of this work on Github to benefit the
robotics community.


title:
The structure of cosmic strings of a $U(1)$ gauge field for the
  conservation of $B-L$

abstract:
  We consider an extension of the Standard Model, where the difference between
the baryon number $B$ and the lepton number $L$ is gauged with an Abelian gauge
field, in order to explain the exact conservation of $B-L$. To avoid a gauge
anomaly, we add a right-handed neutrino $\nu_{\rm R}$ to each fermion
generation. Here it is not sterile, so the usual Majorana term is excluded by
gauge invariance. We provide a mass term for $\nu_{\rm R}$ by adding a
non-standard 1-component Higgs field, thus arriving at a consistent extension
of the Standard Model, where the conservation of $B-L$ is natural, with a
modest number of additional fields. We study the possible formation of cosmic
strings by solving the coupled field equations of the two Higgs fields and the
non-standard U(1) gauge field. Numerical methods provide the corresponding
string profiles, depending on the Higgs winding numbers, such that the
appropriate boundary conditions in the string center and far from it are
fulfilled.


title: Are two finite $H$-spaces homotopy equivalent?

abstract:
  This paper proposes an algorithm that decides if two simply connected spaces
represented by finite simplicial sets of finite $k$-type and finite dimension
$d$ are homotopy equivalent. If the spaces are homotopy equivalent, the
algorithm finds a homotopy equivalence between their Postnikov stages in
dimension $d$. As a consequence, we get an algorithm deciding if two spaces
represented by finite simplicial sets are stably homotopy equivalent.


title:
Difference of photometric properties between regular and non-regular
  Miras in the Magellanic Clouds

abstract:
  Mira variables are asymptotic giant branch pulsating stars with long
pulsation periods and large amplitudes in optical bands. By applying the random
forest algorithm to the I-band light curves for the Miras in the Magellanic
Clouds, we have classified these Miras into regular Miras and non-regular
Miras. Wherein non-regular Miras exhibit a long-term variation in addition to
their primary pulsation periods. Our results confirm that the period-luminosity
relation for maximum light has a smaller dispersion, but only occurs on the
regular oxygen-rich (O-rich) Miras, which we recommend to be applied in future
distance scale work. We have also collected multi-band photometry for these
Miras to perform a spectral-energy-distribution (SED) fitting with stellar and
dust components, showing that a significant fraction of dust is present around
the non-regular Miras. According to our results, we believe that the periodic
long-term variations seen in the non-regular Miras might be due to the presence
of dust.


title:
An Effective Way for Cross-Market Recommendation with Hybrid Pre-Ranking
  and Ranking Models

abstract:
  The Cross-Market Recommendation task of WSDM CUP 2022 is about finding
solutions to improve individual recommendation systems in resource-scarce
target markets by leveraging data from similar high-resource source markets.
Finally, our team OPDAI won the first place with NDCG@10 score of 0.6773 on the
leaderboard. Our solution to this task will be detailed in this paper. To
better transform information from source markets to target markets, we adopt
two stages of ranking. In pre-ranking stage, we adopt diverse pre-ranking
methods or models to do feature generation. After elaborate feature analysis
and feature selection, we train LightGBM with 10-fold bagging to do the final
ranking.


title:
Relativistic free motion time of arrival operator for massive spin-0
  particles with positive energy

abstract:
  A relativistic version of the Aharonov-Bohm time of arrival operator for
spin-0 particles was constructed by Razavi in [Il Nuovo Cimento B \textbf{63},
271 (1969)]. We study the operator in detail by taking its rigged Hilbert space
extension. It is shown that the rigged Hilbert space extension of the operator
provides more insights into the time of arrival problem that goes beyond
Razavi's original results. This allows us to use time of arrival eigenfunctions
that exhibit unitary arrival to construct time of arrival distributions. The
expectation value is also calculated and shown to have quantum correction terms
which can cause a particle to either arrive earlier or later than expected.
Lastly, the constructed TOA distribution and TOA expectation value are shown to
be consistent with special relativity.


title:
Machine learning based lens-free imaging technique for field-portable
  cytometry

abstract:
  Lens-free Shadow Imaging Technique (LSIT) is a well-established technique for
the characterization of microparticles and biological cells. Due to its
simplicity and cost-effectiveness, various low-cost solutions have been
evolved, such as automatic analysis of complete blood count (CBC), cell
viability, 2D cell morphology, 3D cell tomography, etc. The developed auto
characterization algorithm so far for this custom-developed LSIT cytometer was
based on the hand-crafted features of the cell diffraction patterns from the
LSIT cytometer, that were determined from our empirical findings on thousands
of samples of individual cell types, which limit the system in terms of
induction of a new cell type for auto classification or characterization.
Further, its performance is suffering from poor image (cell diffraction
pattern) signatures due to its small signal or background noise. In this work,
we address these issues by leveraging the artificial intelligence-powered auto
signal enhancing scheme such as denoising autoencoder and adaptive cell
characterization technique based on the transfer of learning in deep neural
networks. The performance of our proposed method shows an increase in accuracy
>98% along with the signal enhancement of >5 dB for most of the cell types,
such as Red Blood Cell (RBC) and White Blood Cell (WBC). Furthermore, the model
is adaptive to learn new type of samples within a few learning iterations and
able to successfully classify the newly introduced sample along with the
existing other sample types.


title: Cell-Free Massive MIMO-OFDM for High-Speed Train Communications

abstract:
  Cell-free (CF) massive multiple-input multiple-output (MIMO) systems show
great potentials in low-mobility scenarios, due to cell boundary disappearance
and strong macro diversity. However, the great Doppler frequency offset (DFO)
leads to serious inter-carrier interference in orthogonal frequency division
multiplexing (OFDM) technology, which makes it difficult to provide
high-quality transmissions for both high-speed train (HST) operation control
systems and passengers. In this paper, we focus on the performance of CF
massive MIMO-OFDM systems with both fully centralized and local minimum mean
square error (MMSE) combining in HST communications. Considering the local
maximum ratio (MR) combining, the large-scale fading decoding (LSFD)
cooperation and the practical effect of DFO on system performance, exact
closed-form expressions for uplink spectral efficiency (SE) expressions are
derived. We observe that cooperative MMSE combining achieves better SE
performance than uncooperative MR combining. In addition, HST communications
with small cell and cellular massive MIMO-OFDM systems are compared in terms of
SE. Numerical results reveal that the CF massive MIMO-OFDM system achieves a
larger and more uniform SE than the other systems. Finally, the train antenna
centric (TA-centric) CF massive MIMO-OFDM system is designed for practical
implementation in HST communications, and three power control schemes are
adopted to optimize the propagation of TAs for reducing the impact of the DFO.


title: Cosmology and perturbations in tachyonic massive gravity

abstract:
  As massive gravity and its extensions offer physically well-defined
gravitational theories with a nonzero graviton mass, we present a new extension
of the de Rham-Gabadadze-Tolley (dRGT)massive gravity, which is tachyonic
massive gravity theory. We firstly introduce the new extension of the dRGT
massive gravity, constructed by adding a tachyonic term. We then find the
cosmological background equations, and present the analysis of
self-accelerating solutions. We examine the tensor perturbations to calculate
the dispersion relation of gravitational waves (GWs). In a special case, we
consider a constant tachyon potential for the tachyon field, and calculate the
equations of motion and self-accelerating solutions. Finally, we investigate
the background perturbations, which include tensor, vector, and scalar
perturbations in this case. We calculate the dispersion relation of GWs in the
FLRW cosmology in a tachyonic massive gravity theory. These analyses provide
potential inputs to future applications in cosmology and GWs propagation.


title: Do Prompts Solve NLP Tasks Using Natural Language?

abstract:
  Thanks to the advanced improvement of large pre-trained language models,
prompt-based fine-tuning is shown to be effective on a variety of downstream
tasks. Though many prompting methods have been investigated, it remains unknown
which type of prompts are the most effective among three types of prompts
(i.e., human-designed prompts, schema prompts and null prompts). In this work,
we empirically compare the three types of prompts under both few-shot and
fully-supervised settings. Our experimental results show that schema prompts
are the most effective in general. Besides, the performance gaps tend to
diminish when the scale of training data grows large.


title:
Combining Reinforcement Learning and Optimal Transport for the Traveling
  Salesman Problem

abstract:
  The traveling salesman problem is a fundamental combinatorial optimization
problem with strong exact algorithms. However, as problems scale up, these
exact algorithms fail to provide a solution in a reasonable time. To resolve
this, current works look at utilizing deep learning to construct reasonable
solutions. Such efforts have been very successful, but tend to be slow and
compute intensive. This paper exemplifies the integration of entropic
regularized optimal transport techniques as a layer in a deep reinforcement
learning network. We show that we can construct a model capable of learning
without supervision and inferences significantly faster than current
autoregressive approaches. We also empirically evaluate the benefits of
including optimal transport algorithms within deep learning models to enforce
assignment constraints during end-to-end training.


title: Weakly Supervised Correspondence Learning

abstract:
  Correspondence learning is a fundamental problem in robotics, which aims to
learn a mapping between state, action pairs of agents of different dynamics or
embodiments. However, current correspondence learning methods either leverage
strictly paired data -- which are often difficult to collect -- or learn in an
unsupervised fashion from unpaired data using regularization techniques such as
cycle-consistency -- which suffer from severe misalignment issues. We propose a
weakly supervised correspondence learning approach that trades off between
strong supervision over strictly paired data and unsupervised learning with a
regularizer over unpaired data. Our idea is to leverage two types of weak
supervision: i) temporal ordering of states and actions to reduce the
compounding error, and ii) paired abstractions, instead of paired data, to
alleviate the misalignment problem and learn a more accurate correspondence.
The two types of weak supervision are easy to access in real-world
applications, which simultaneously reduces the high cost of annotating strictly
paired data and improves the quality of the learned correspondence.


title:
Responsible-AI-by-Design: a Pattern Collection for Designing Responsible
  AI Systems

abstract:
  Although AI has significant potential to transform society, there are serious
concerns about its ability to behave and make decisions responsibly. Many
ethical regulations, principles, and guidelines for responsible AI have been
issued recently. However, these principles are high-level and difficult to put
into practice. In the meantime much effort has been put into responsible AI
from the algorithm perspective, but they are limited to a small subset of
ethical principles amenable to mathematical analysis. Responsible AI issues go
beyond data and algorithms and are often at the system-level crosscutting many
system components and the entire software engineering lifecycle. Based on the
result of a systematic literature review, this paper identifies one missing
element as the system-level guidance: how to design the architecture of
responsible AI systems. We present a summary of design patterns that can be
embedded into the AI systems as product features to contribute to
responsible-AI-by-design.


title:
Distributed goal assignment strategy for improving leader-following
  formation control performance

abstract:
  This paper investigates a distributed goal assignment problem in
leader-following formation control of second-order multi-agent systems. It is
assumed that each agent can communicate with nearby agents within the
communication range and the leader information is only available to a subset of
agents. Compared with existing formation control schemes addressing the goal
assignment issue, the main contribution of this paper is to construct a novel
distributed assignment strategy allotting appropriate goal positions of agents
in the leader-following formation control framework. Based on the rigorous
analysis using the Lyapunov stability theory, the enhancement of the control
performance is proved via the proposed assignment strategy. To demonstrate the
effectiveness of our theoretical results, two examples including multiple
quadrotors are simulated.


title: A Split Semantic Detection Algorithm for Psychological Sandplay Image

abstract:
  Psychological sandplay, as an important psychological analysis tool, is a
visual scene constructed by the tester selecting and placing sand objects
(e.g., sand, river, human figures, animals, vegetation, buildings, etc.). As
the projection of the tester's inner world, it contains high-level semantic
information reflecting the tester's thoughts and feelings. Most of the existing
computer vision technologies focus on the objective basic semantics (e.g.,
object's name, attribute, boundingbox, etc.) in the natural image, while few
related works pay attention to the subjective psychological semantics (e.g.,
emotion, thoughts, feelings, etc.) in the artificial image. We take the latter
semantics as the research object, take "split" (a common psychological
semantics reflecting the inner integration of testers) as the research goal,
and use the method of machine learning to realize the automatic detection of
split semantics, so as to explore the application of machine learning in the
detection of subjective psychological semantics of sandplay images. To this
end, we present a feature dimensionality reduction and extraction algorithm to
obtain a one-dimensional vector representing the split feature, and build the
split semantic detector based on Multilayer Perceptron network to get the
detection results. Experimental results on the real sandplay datasets show the
effectiveness of our proposed algorithm.


title:
Probing the interlayer coupling in 2$H$-NbS$_2$ via soft-x-ray
  angle-resolved photoemission spectroscopy

abstract:
  In the large family of two-dimensional (2D) layered materials including
graphene, its honeycomb analogs, and transition-metal dichalcogenides, the
interlayer coupling plays a rather intriguing role. On the one hand, the weak
van der Waals interaction that holds the layers together endows these compounds
with quasi-2D properties, which might imply small interlayer effects on the
electronically active bands. On the other hand, the oft-witnessed differences
in electronic, optical, and magnetic behaviors of monolayers, bilayers, and
multilayers of the same compound must have as their microscopic origin the
detailed interlayer hopping parameters. Given the few experimental reports that
have attempted to explicitly extract these parameters, we employ soft-x-ray
angle-resolved photoemission spectroscopy (SX-ARPES) to probe the interlayer
coupling in superconducting 2$H$-NbS$_2$. We visualize the S 3$p_z$ bands that
disperse with respect to the out-of-plane momentum and introduce a simple
tight-binding model to extract the interlayer hopping parameters. From
first-principles calculations, we clarify how atomic distances and the proper
accounting for screening via hybrid functionals influence these bands. The
knowledge of interlayer hopping parameters is particularly pertinent in
NbS$_2$, where recent experiments have uncovered fingerprints of
finite-momentum superconductivity in the bulk material and heterostructures.


title: Splitting Receiver with Joint Envelope and Coherent Detection

abstract:
  This letter proposes a new splitting receiver design with joint envelope
detection (ED) and coherent detection (CD). To characterize its fundamental
performance limit, we conduct high signal-to-noise ratio (SNR) analysis on the
proposed ED-CD splitting receiver and obtain closed-form approximations of both
the achievable mutual information and the optimal splitting ratio (i.e., a key
design parameter of the receiver). Our numerical results show that these high
SNR approximations are accurate over a wide range of moderate SNR values,
signifying the usefulness of the obtained analytical results. We also provide
insights on the conditions at which the proposed splitting receiver has
significant performance advantages over the traditional receivers.


title: Location problems with cutoff

abstract:
  In this paper we study a generalized version of the Weber problem of finding
a point that minimizes the sum of its distances to a finite number of given
points. In our setting these distances may be $cut$ $off$ at a given value $C >
0$, and we allow for the option of an $empty$ solution at a fixed cost $C'$. We
analyze under which circumstances these problems can be reduced to the simpler
Weber problem, and also when we definitely have to solve the more complex
problem with cutoff. We furthermore present adaptions of the algorithm of
[Drezner et al., 1991, $Transportation$ $Science$ 25(3), 183--187] to our
setting, which in certain situations are able to substantially reduce
computation times as demonstrated in a simulation study. The sensitivity with
respect to the cutoff value is also studied, which allows us to provide an
algorithm that efficiently solves the problem simultaneously for all $C>0$.


title:
Towards Bidirectional Arbitrary Image Rescaling: Joint Optimization and
  Cycle Idempotence

abstract:
  Deep learning based single image super-resolution models have been widely
studied and superb results are achieved in upscaling low-resolution images with
fixed scale factor and downscaling degradation kernel. To improve real world
applicability of such models, there are growing interests to develop models
optimized for arbitrary upscaling factors. Our proposed method is the first to
treat arbitrary rescaling, both upscaling and downscaling, as one unified
process. Using joint optimization of both directions, the proposed model is
able to learn upscaling and downscaling simultaneously and achieve
bidirectional arbitrary image rescaling. It improves the performance of current
arbitrary upscaling models by a large margin while at the same time learns to
maintain visual perception quality in downscaled images. The proposed model is
further shown to be robust in cycle idempotence test, free of severe
degradations in reconstruction accuracy when the downscaling-to-upscaling cycle
is applied repetitively. This robustness is beneficial for image rescaling in
the wild when this cycle could be applied to one image for multiple times. It
also performs well on tests with arbitrary large scales and asymmetric scales,
even when the model is not trained with such tasks. Extensive experiments are
conducted to demonstrate the superior performance of our model.


title: Compositions of Belyi Maps and their Extended Monodromy Groups

abstract:
  Given a composition of Bely\u{\i} maps $\beta \circ \gamma: X \rightarrow Z$,
paths between edges of $\beta$ are extended to form loops, then lifted by
$\gamma$. These liftings are then studied to understand how loops in $Z$ act on
edges of $\beta \circ \gamma$, demonstrating the group operation in
$\mathop{\mathrm{Mon}} \beta \circ \gamma \unlhd \mathop{\mathrm{Mon}} \gamma
\wr \mathop{\mathrm{Mon}} \beta$. Abstracting away the specific Bely\u{\i} map
$\gamma$ and finding the image of $\pi_{1}(Z)$ in $\pi_{1}(Y) \wr
\mathop{\mathrm{Mon}} \beta$ instead allows subsequently determining
$\mathop{\mathrm{Mon}} \beta \circ \gamma$, for any $\gamma$, using only the
monodromy representation of $\gamma$.


title: A Principled Design of Image Representation: Towards Forensic Tasks

abstract:
  Image forensics is a rising topic as the trustworthy multimedia content is
critical for modern society. Like other vision-related applications, forensic
analysis relies heavily on the proper image representation. Despite the
importance, current theoretical understanding for such representation remains
limited, with varying degrees of neglect for its key role. For this gap, we
attempt to investigate the forensic-oriented image representation as a distinct
problem, from the perspectives of theory, implementation, and application. Our
work starts from the abstraction of basic principles that the representation
for forensics should satisfy, especially revealing the criticality of
robustness, interpretability, and coverage. At the theoretical level, we
propose a new representation framework for forensics, called Dense Invariant
Representation (DIR), which is characterized by stable description with
mathematical guarantees. At the implementation level, the discrete calculation
problems of DIR are discussed, and the corresponding accurate and fast
solutions are designed with generic nature and constant complexity. We
demonstrate the above arguments on the dense-domain pattern detection and
matching experiments, providing comparison results with state-of-the-art
descriptors. Also, at the application level, the proposed DIR is initially
explored in passive and active forensics, namely copy-move forgery detection
and perceptual hashing, exhibiting the benefits in fulfilling the requirements
of such forensic tasks.