title:
A machine learning-based severity prediction tool for diabetic
  sensorimotor polyneuropathy using Michigan neuropathy screening
  instrumentations

abstract:
  Background: Diabetic Sensorimotor polyneuropathy (DSPN) is a major long-term
complication in diabetic patients associated with painful neuropathy, foot
ulceration and amputation. The Michigan neuropathy screening instrument (MNSI)
is one of the most common screening techniques for DSPN, however, it does not
provide any direct severity grading system. Method: For designing and modelling
the DSPN severity grading systems for MNSI, 19 years of data from Epidemiology
of Diabetes Interventions and Complications (EDIC) clinical trials were used.
MNSI variables and patient outcomes were investigated using machine learning
tools to identify the features having higher association in DSPN
identification. A multivariable logistic regression-based nomogram was
generated and validated for DSPN severity grading. Results: The top-7 ranked
features from MNSI: 10-gm filament, Vibration perception (R), Vibration
perception (L), previous diabetic neuropathy, the appearance of deformities,
appearance of callus and appearance of fissure were identified as key features
for identifying DSPN using the extra tree model. The area under the curve (AUC)
of the nomogram for the internal and external datasets were 0.9421 and 0.946,
respectively. From the developed nomogram, the probability of having DSPN was
predicted and a DSPN severity scoring system for MNSI was developed from the
probability score. The model performance was validated on an independent
dataset. Patients were stratified into four severity levels: absent, mild,
moderate, and severe using a cut-off value of 10.5, 12.7 and 15 for a DSPN
probability less than 50%, 75% to 90%, and above 90%, respectively.
Conclusions: This study provides a simple, easy-to-use and reliable algorithm
for defining the prognosis and management of patients with DSPN.


title:
A Generalized Cluster-Free NOMA Framework Towards Next-Generation
  Multiple Access

abstract:
  A generalized downlink multi-antenna non-orthogonal multiple access (NOMA)
transmission framework is proposed with the novel concept of cluster-free
successive interference cancellation (SIC). In contrast to conventional NOMA
approaches, where SIC is successively carried out within the same cluster, the
key idea is that the SIC can be flexibly implemented between any arbitrary
users to achieve efficient interference elimination. Based on the proposed
framework, a sum rate maximization problem is formulated for jointly optimizing
the transmit beamforming and the SIC operations between users, subject to the
SIC decoding conditions and users' minimal data rate requirements. To tackle
this highly-coupled mixed-integer nonlinear programming problem, an alternating
direction method of multipliers-successive convex approximation (ADMM-SCA)
algorithm is developed. The original problem is first reformulated into a
tractable biconvex augmented Lagrangian (AL) problem by handling the non-convex
terms via SCA. Then, this AL problem is decomposed into two subproblems that
are iteratively solved by the ADMM to obtain the stationary solution. Moreover,
to reduce the computational complexity and alleviate the parameter
initialization sensitivity of ADMM-SCA, a Matching-SCA algorithm is proposed.
The intractable binary SIC operations are solved through an extended
many-to-many matching, which is jointly combined with an SCA process to
optimize the transmit beamforming. The proposed Matching-SCA can converge to an
enhanced exchange-stable matching that guarantees the local optimality.
Numerical results demonstrate that: i) the proposed Matching-SCA algorithm
achieves comparable performance and a faster convergence compared to ADMM-SCA;
ii) the proposed generalized framework realizes scenario-adaptive
communications and outperforms traditional multi-antenna NOMA approaches in
various communication regimes.


title:
The role of spontaneous curvature in the formation of cell membrane
  necks

abstract:
  The mechanical effects of membrane compositional inhomogeneities are analyzed
in a process analogous of neck formation in cellular membranes. We cast on the
Canham-Helfrich model of fluid membranes with both the spontaneous curvature
and the surface tension being non-homogeneous functions along the cell
membrane. The inhomogeneous distribution is determined by the equilibrium
mechanical equations, and, in order to establish the role played by the
inhomogeneity, we focus on the catenoid, a surface of zero mean curvature,
which can be described in terms of the catenary curve parameterized by arc
length. We show that analytic solutions exist for the spontaneous curvature, as
well as for both, the surface tension and the radial elastic force. An analytic
expression for the constrictive force at the neck, is obtained. From the
energetic analysis, it is found that, if we fix the value of the constrictive
force at the neck, the set of solutions lies on two branches separated by an
energetic barrier. This barrier corresponds to the energy of the maximum
catenoid. If instead we fix the axial force, the solution has access to
catenoid of any size


title:
bayesassurance: An R package for calculating sample size and Bayesian
  assurance

abstract:
  We present a bayesassurance R package that computes the Bayesian assurance
under various settings characterized by different assumptions and objectives.
The package offers a constructive set of simulation-based functions suitable
for addressing a wide range of clinical trial study design problems. We provide
a detailed description of the underlying framework embedded within each of the
power and assurance functions and demonstrate their usage through a series of
worked-out examples. Through these examples, we hope to corroborate the
advantages that come with using a two-stage generalized structure. We also
illustrate scenarios where the Bayesian assurance and frequentist power
overlap, allowing the user to address both Bayesian and classical inference
problems provided that the parameters are properly defined. All
assurance-related functions included in this R package rely on a two-stage
Bayesian method that assigns two distinct priors to evaluate the unconditional
probability of observing a positive outcome, which in turn addresses subtle
limitations that take place when using the standard single-prior approach.


title:
Learning to Synthesize Volumetric Meshes from Vision-based Tactile
  Imprints

abstract:
  Vision-based tactile sensors typically utilize a deformable elastomer and a
camera mounted above to provide high-resolution image observations of contacts.
Obtaining accurate volumetric meshes for the deformed elastomer can provide
direct contact information and benefit robotic grasping and manipulation. This
paper focuses on learning to synthesize the volumetric mesh of the elastomer
based on the image imprints acquired from vision-based tactile sensors.
Synthetic image-mesh pairs and real-world images are gathered from 3D finite
element methods (FEM) and physical sensors, respectively. A graph neural
network (GNN) is introduced to learn the image-to-mesh mappings with supervised
learning. A self-supervised adaptation method and image augmentation techniques
are proposed to transfer networks from simulation to reality, from primitive
contacts to unseen contacts, and from one sensor to another. Using these
learned and adapted networks, our proposed method can accurately reconstruct
the deformation of the real-world tactile sensor elastomer in various domains,
as indicated by the quantitative and qualitative results.


title: Constraining Black Hole Natal Kicks with Astrometric Microlensing

abstract:
  Multiple pieces of evidence suggest that neutron stars receive large kicks
when formed from the remnant of a collapsing star. However, the evidence for
whether black holes (BH) receive natal kicks is less clear, reliant on weak
constraints from the analysis of BH X-ray binaries and massive runaway and
walkaway stars. Here we show for the first time that recent microlensing
detections offer a new method for measuring the kicks BHs receive at birth.
When a BH is identified through both photometric and astrometric microlensing
and when the lensed star has a known distance and proper motion, the mass,
distance and proper motion of the BH can be determined. We study the runaway
velocities for components of eccentric binaries disrupted during a supernova,
finding the peculiar velocity correlates strongly with the kick a BH received a
birth, typically within 20\%, even when the natal kick is smaller than the
orbital velocity. Therefore, by measuring the peculiar velocity of a BH or
other compact object that formed from a binary that disrupted during core
collapse, we are in effect measuring the natal kick that object received. We
focus on MOA-2011-BLG-191/OGLE-2011-BLG-0462, an isolated, single BH detected
by microlensing and consider a range of possible formation scenarios, including
its formation from the disruption of a binary during a supernova event. We
determine that MOA-2011-BLG-191/OGLE-2011-BLG-0462 has a Milky Way orbit
consistent with a thick disk population, but if it were formed within the
kinematic thin disk it received a natal kick $\lesssim$100 km s$^{-1}$.


title:
Sobolev spaces on arbitrary domains and semigroups generated by
  fractional Laplacian

abstract:
  We describe a procedure to introduce Sobolev spaces and the semigroup
generated by the fractional Dirichlet Laplacian on an arbitrary domain of
$\R^d$. In particular, the well-definedness of the spaces of both
non-homogeneous and homogeneous type together with their duality properties,
embeddings, and Gagliardo-Nirenberg inequalities will be discussed. We also
show the continuity and the smoothing property of the semigroup.


title: Practical Aspects of Zero-Shot Learning

abstract:
  One of important areas of machine learning research is zero-shot learning. It
is applied when properly labeled training data set is not available. A number
of zero-shot algorithms have been proposed and experimented with. However, none
of them seems to be the "overall winner". In situations like this, it may be
possible to develop a meta-classifier that would combine "best aspects" of
individual classifiers and outperform all of them. In this context, the goal of
this contribution is twofold. First, multiple state-of-the-art zero-shot
learning methods are compared for standard benchmark datasets. Second, multiple
meta-classifiers are suggested and experimentally compared (for the same
datasets).


title: On the structure of generic subshifts

abstract:
  We investigate generic properties (i.e. properties corresponding to residual
sets) in the space of subshifts with the Hausdorff metric. Our results deal
with four spaces: the space $\mathbf{S}$ of all subshifts, the space
$\mathbf{S}^{\prime}$ of non-isolated subshifts, the closure
$\overline{\mathbf{T}^{\prime}}$ of the infinite transitive subshifts, and the
closure $\overline{\mathbf{T}\mathbf{T}^{\prime}}$ of the infinite totally
transitive subshifts.
  In the first two settings, we prove that generic subshifts are fairly
degenerate; for instance, all points in a generic subshift are biasymptotic to
periodic orbits. In contrast, generic subshifts in the latter two spaces
possess more interesting dynamical behavior. Notably, generic subshifts in both
$\overline{\mathbf{T}^{\prime}}$ and $\overline{\mathbf{T}\mathbf{T}^{\prime}}$
are zero entropy, minimal, uniquely ergodic, and have word complexity which
realizes any possible subexponential growth rate along a subsequence. In
addition, a generic subshift in $\overline{\mathbf{T}^{\prime}}$ is a regular
Toeplitz subshift which is strongly orbit equivalent to the universal odometer.


title: A radial limit on polar circumbinary orbits from general relativity

abstract:
  A particle orbiting a misaligned eccentric orbit binary undergoes nodal
precession either around the binary angular momentum vector (a circulating
orbit) or around a stationary inclination (a librating orbit). In the absence
of general relativity, the stationary inclination is inclined by 90 degrees to
the binary angular momentum vector (aligned with the binary eccentricity
vector) and does not depend on the particle semi-major axis. General relativity
causes apsidal precession of the binary orbit. Close to the binary, the
behaviour of the particle is not significantly affected, a librating particle
precesses with the binary. However, we find that the stationary inclination and
the minimum inclination required for libration both increase with the particle
semi-major axis. There is a critical radius beyond which there are no librating
orbits, only circulating orbits, and therefore there is a maximum orbital
radius for a stationary polar orbiting body. The critical radius is within
planet forming regions around binaries with semi-major axis <= 1 au. This has
implications for the search for misaligned circumbinary planets and the radial
extent of polar circumbinary disks.


title: Spectral Line Depth Variability in Radial Velocity Spectra

abstract:
  Stellar active regions, including spots and faculae, can create radial
velocity (RV) signals that interfere with the detection and mass measurements
of low mass exoplanets. In doing so, these active regions affect each spectral
line differently, but the origin of these differences is not fully understood.
Here we explore how spectral line variability correlated with S-index (Ca H & K
emission) is related to the atomic properties of each spectral line. Next we
develop a simple analytic stellar atmosphere model that can account for the
largest sources of line variability with S-index. Then we apply this model to
HARPS spectra of {\alpha} Cen B to explain Fe I line depth changes in terms of
a disk-averaged temperature difference between active and quiet regions on the
visible hemisphere of the star. This work helps establish a physical basis for
understanding how stellar activity manifests differently in each spectral line,
and may help future work mitigating the impact of stellar activity on exoplanet
RV surveys.


title: A Distribution Evolutionary Algorithm for Graph Coloring

abstract:
  Graph Coloring Problem (GCP) is a classic combinatorial optimization problem
that has a wide application in theoretical research and engineering. To address
complicated GCPs efficiently, a distribution evolutionary algorithm based on
population of probability models (DEA-PPM) is proposed. Based on a novel
representation of probability model, DEA-PPM employs a Gaussian orthogonal
search strategy to explore the probability space, by which global exploration
can be realized using a small population. With assistance of local exploitation
on a small solution population, DEA-PPM strikes a good balance between
exploration and exploitation. Numerical results demonstrate that DEA-PPM
performs well on selected complicated GCPs, which contributes to its
competitiveness to the state-of-the-art metaheuristics.


title:
CAT-Net: A Cross-Slice Attention Transformer Model for Prostate Zonal
  Segmentation in MRI

abstract:
  Prostate cancer is the second leading cause of cancer death among men in the
United States. The diagnosis of prostate MRI often relies on the accurate
prostate zonal segmentation. However, state-of-the-art automatic segmentation
methods often fail to produce well-contained volumetric segmentation of the
prostate zones since certain slices of prostate MRI, such as base and apex
slices, are harder to segment than other slices. This difficulty can be
overcome by accounting for the cross-slice relationship of adjacent slices, but
current methods do not fully learn and exploit such relationships. In this
paper, we propose a novel cross-slice attention mechanism, which we use in a
Transformer module to systematically learn the cross-slice relationship at
different scales. The module can be utilized in any existing learning-based
segmentation framework with skip connections. Experiments show that our
cross-slice attention is able to capture the cross-slice information in
prostate zonal segmentation and improve the performance of current
state-of-the-art methods. Our method significantly improves segmentation
accuracy in the peripheral zone, such that the segmentation results are
consistent across all the prostate slices (apex, mid-gland, and base).


title: Lusztig Factorization Dynamics of the Full Kostant-Toda Lattices

abstract:
  We study extensions of the classical Toda lattices at several different
space-time scales. These extensions are from the classical tridiagonal phase
spaces to the phase space of full Hessenberg matrices, referred to as the Full
Kostant-Toda Lattice. Our formulation makes it natural to make further
Lie-theoretic generalizations to dual spaces of Borel Lie algebras. Our study
brings into play factorizations of Loewner-Whitney type in terms of canonical
coordinatizations due to Lusztig. Using these coordinates we formulate precise
conditions for the well-posedness of the dynamics at the different space-time
scales. Along the way we derive a novel, minimal box-ball system for Full Toda
that doesn't involve any capacities or colorings, as well as an extension of
O'Connell's ODEs to Full Toda.


title:
Real-Variable Theory of Hardy Spaces Associated with Generalized Herz
  Spaces of Rafeiro and Samko

abstract:
  This book is devoted to exploring properties of generalized Herz spaces and
establishing a complete real-variable theory of Hardy spaces associated with
local and global generalized Herz spaces via a totally fresh perspective which
means that the authors view these generalized Herz spaces as special cases of
ball quasi-Banach function spaces. To be precise, in this book, the authors
first study some basic properties of generalized Herz spaces and obtain
boundedness and compactness characterizations of commutators on them. Then the
authors introduce the associated Herz-Hardy spaces, local Herz-Hardy spaces,
and weak Herz-Hardy spaces, and develop a complete real-variable theory of
these Herz-Hardy spaces, including their various maximal function, atomic,
finite atomic, molecular as well as various Littlewood-Paley function
characterizations. As applications, the authors establish the boundedness of
some important operators arising from harmonic analysis on these Herz-Hardy
spaces. Finally, the inhomogeneous Herz-Hardy spaces and their complete
real-variable theory are also investigated. Due to the deficiency of the
associate space of the global Herz space, the known real-variable
characterizations about Hardy-type spaces associated with ball quasi-Banach
function spaces are not applicable to Hardy spaces associated with global
generalized Herz spaces which need an improved generalization of the existing
one, done by the authors also in this book and having more additional
anticipating applications. The authors should also point out that, with the
fresh perspective and the improved conclusions on the real-variable theory of
Hardy spaces associated with ball quasi-Banach function spaces, the exponents
in all the obtained results of this book are sharp. Moreover, all of these
results in this book are new and have never been published before.


title:
Autonomous Road Vehicle Emergency Obstacle Avoidance Maneuver Framework
  at Highway Speeds

abstract:
  An Autonomous Road Vehicle (ARV) can navigate various types of road networks
using inputs such as throttle (acceleration), braking (deceleration), and
steering (change of lateral direction). In most ARV driving scenarios that
involve normal vehicle traffic and encounters with vulnerable road users
(VRUs), ARVs are not required to take evasive action. This paper presents a
novel Emergency Obstacle Avoidance Maneuver (EOAM) methodology for ARVs
traveling at higher speeds and lower road surface friction, involving
time-critical maneuver determination and control. The proposed EOAM Framework
offers usage of the ARV's sensing, perception, control, and actuation system
abilities as one cohesive system, to accomplish avoidance of an on-road
obstacle, based first on performance feasibility and second on passenger
comfort, and is designed to be well-integrated within an ARV high-level system.
Co-simulation including the ARV EOAM logic in Simulink and a vehicle model in
CarSim is conducted with speeds ranging from 55 to 165 km/h and on road
surfaces with friction ranging from 1.0 to 0.1. The results are analyzed and
given in the context of an entire ARV system, with implications for future
work.


title:
On Convergence of General Truncation-Augmentation Schemes for
  Approximating Stationary Distributions of Markov Chains

abstract:
  In the analysis of Markov chains and processes, it is sometimes convenient to
replace an unbounded state space with a "truncated" bounded state space. When
such a replacement is made, one often wants to know whether the equilibrium
behavior of the truncated chain or process is close to that of the untruncated
system. For example, such questions arise naturally when considering numerical
methods for computing stationary distributions on unbounded state space. In
this paper, we study general truncation-augmentation schemes, in which the
substochastic truncated "northwest corner" of the transition matrix or kernel
is stochasticized (or augmented) arbitrarily. In the presence of a Lyapunov
condition involving a coercive function, we show that such schemes are
generally convergent in countable state space, provided that the truncation is
chosen as a sublevel set of the Lyapunov function. For stochastically monotone
Markov chains on $\mathbb Z_+$, we prove that we can always choose the
truncation sets to be of the form $\{0,1,...,n\}$. We then provide sufficient
conditions for weakly continuous Markov chains under which general
truncation-augmentation schemes converge weakly in continuous state space.
Finally, we briefly discuss the extension of the theory to continuous time
Markov jump processes.


title:
Asymmetric Rogers--Ramanujan type identities. I. The Andrews--Uncu
  Conjecture

abstract:
  In this work, we start an investigation of asymmetric Rogers--Ramanujan type
identities. The first object is the following unexpected relation $$\sum_{n\ge
0} \frac{(-1)^n q^{3\binom{n}{2}+4n}(q;q^3)_n}{(q^9;q^9)_n} =
\frac{(q^{4};q^{6})_\infty (q^{12};q^{18})_\infty}{(q^{5};q^{6})_\infty
(q^{9};q^{18})_\infty}$$ and its $a$-generalization. We then use this identity
as a key ingredient to confirm a recent conjecture of G. E. Andrews and A. K.
Uncu.


title: Efficiency of Nonthermal Particle Acceleration in Magnetic Reconnection

abstract:
  The nonthermal particle acceleration during magnetic reconnection remains a
fundamental topic in several astrophysical phenomena, such as solar flares,
pulsar wind, magnetars, etc, for more than half a century, and one of the
unresolved questions is its efficiency. Recently, nonthermal particle
acceleration mechanisms during reconnection have been extensively studied by
particle-in-cell simulations, yet it is an intriguing enigma as to how the
magnetic field energy is divided into thermally heated plasmas and nonthermal
particles. Here we study both non-relativistic and relativistic magnetic
reconnections using large-scale particle-in-cell simulation for a pair plasma,
and indicate that the production of the nonthermal particle becomes efficient
with increasing the plasma temperature. In the relativistic hot plasma case, we
determine that the heated plasmas by reconnection can be approximated by a
kappa distribution function with the kappa index of approximately 3 or less
(equivalent to 2 or less for the power-law index), and the nonthermal energy
density of reconnection is approximately over 95% of the total internal energy
in the downstream exhaust.

categories: astro-ph.HE astro-ph.SR physics.plasm-ph physics.space-ph


title:
High-Dimensional Vector Autoregression with Common Response and
  Predictor Factors

abstract:
  Reinterpreting the reduced-rank vector autoregressive (VAR) model of order
one as a supervised factor model, where two factor modelings are simultaneously
conducted to response and predictor spaces, this article introduces a new
model, called vector autoregression with common response and predictor factors,
to further explore the common structure between the response and predictors of
a high-dimensional time series. The new model can provide better physical
interpretations and improve estimation efficiency. In conjunction with the
tensor operation, the model can easily be extended to any finite-order VAR
models. A regularization-based method is considered for the high-dimensional
estimation with the gradient descent algorithm, and its computational and
statistical convergence guarantees are established. Simulation experiments
confirm our theoretical findings, and a macroeconomic application showcases the
appealing properties of the proposed model in structural analysis and
forecasting.


title: Self-Supervised Light Field Depth Estimation Using Epipolar Plane Images

abstract:
  Exploiting light field data makes it possible to obtain dense and accurate
depth map. However, synthetic scenes with limited disparity range cannot
contain the diversity of real scenes. By training in synthetic data, current
learning-based methods do not perform well in real scenes. In this paper, we
propose a self-supervised learning framework for light field depth estimation.
Different from the existing end-to-end training methods using disparity label
per pixel, our approach implements network training by estimating EPI disparity
shift after refocusing, which extends the disparity range of epipolar lines. To
reduce the sensitivity of EPI to noise, we propose a new input mode called
EPI-Stack, which stacks EPIs in the view dimension. This method is less
sensitive to noise scenes than traditional input mode and improves the
efficiency of estimation. Compared with other state-of-the-art methods, the
proposed method can also obtain higher quality results in real-world scenarios,
especially in the complex occlusion and depth discontinuity.


title:
Assessing Evolutionary Terrain Generation Methods for Curriculum
  Reinforcement Learning

abstract:
  Curriculum learning allows complex tasks to be mastered via incremental
progression over `stepping stone' goals towards a final desired behaviour.
Typical implementations learn locomotion policies for challenging environments
through gradual complexification of a terrain mesh generated through a
parameterised noise function. To date, researchers have predominantly generated
terrains from a limited range of noise functions, and the effect of the
generator on the learning process is underrepresented in the literature. We
compare popular noise-based terrain generators to two indirect encodings, CPPN
and GAN. To allow direct comparison between both direct and indirect
representations, we assess the impact of a range of representation-agnostic
MAP-Elites feature descriptors that compute metrics directly from the generated
terrain meshes. Next, performance and coverage are assessed when training a
humanoid robot in a physics simulator using the PPO algorithm. Results describe
key differences between the generators that inform their use in curriculum
learning, and present a range of useful feature descriptors for uptake by the
community.


title:
An Evaluation Dataset for Legal Word Embedding: A Case Study On Chinese
  Codex

abstract:
  Word embedding is a modern distributed word representations approach widely
used in many natural language processing tasks. Converting the vocabulary in a
legal document into a word embedding model facilitates subjecting legal
documents to machine learning, deep learning, and other algorithms and
subsequently performing the downstream tasks of natural language processing
vis-\`a-vis, for instance, document classification, contract review, and
machine translation. The most common and practical approach of accuracy
evaluation with the word embedding model uses a benchmark set with linguistic
rules or the relationship between words to perform analogy reasoning via
algebraic calculation. This paper proposes establishing a 1,134 Legal
Analogical Reasoning Questions Set (LARQS) from the 2,388 Chinese Codex corpus
using five kinds of legal relations, which are then used to evaluate the
accuracy of the Chinese word embedding model. Moreover, we discovered that
legal relations might be ubiquitous in the word embedding model.


title:
Disentangling Object Motion and Occlusion for Unsupervised Multi-frame
  Monocular Depth

abstract:
  Conventional self-supervised monocular depth prediction methods are based on
a static environment assumption, which leads to accuracy degradation in dynamic
scenes due to the mismatch and occlusion problems introduced by object motions.
Existing dynamic-object-focused methods only partially solved the mismatch
problem at the training loss level. In this paper, we accordingly propose a
novel multi-frame monocular depth prediction method to solve these problems at
both the prediction and supervision loss levels. Our method, called
DynamicDepth, is a new framework trained via a self-supervised cycle consistent
learning scheme. A Dynamic Object Motion Disentanglement (DOMD) module is
proposed to disentangle object motions to solve the mismatch problem. Moreover,
novel occlusion-aware Cost Volume and Re-projection Loss are designed to
alleviate the occlusion effects of object motions. Extensive analyses and
experiments on the Cityscapes and KITTI datasets show that our method
significantly outperforms the state-of-the-art monocular depth prediction
methods, especially in the areas of dynamic objects. Our code will be made
publicly available.


title: Unified Transformer Tracker for Object Tracking

abstract:
  As an important area in computer vision, object tracking has formed two
separate communities that respectively study Single Object Tracking (SOT) and
Multiple Object Tracking (MOT). However, current methods in one tracking
scenario are not easily adapted to the other due to the divergent training
datasets and tracking objects of both tasks. Although UniTrack
\cite{wang2021different} demonstrates that a shared appearance model with
multiple heads can be used to tackle individual tracking tasks, it fails to
exploit the large-scale tracking datasets for training and performs poorly on
single object tracking. In this work, we present the Unified Transformer
Tracker (UTT) to address tracking problems in different scenarios with one
paradigm. A track transformer is developed in our UTT to track the target in
both SOT and MOT. The correlation between the target and tracking frame
features is exploited to localize the target. We demonstrate that both SOT and
MOT tasks can be solved within this framework. The model can be simultaneously
end-to-end trained by alternatively optimizing the SOT and MOT objectives on
the datasets of individual tasks. Extensive experiments are conducted on
several benchmarks with a unified model trained on SOT and MOT datasets. Code
will be available at https://github.com/Flowerfan/Trackron.


title:
Improving Generalization of Deep Neural Network Acoustic Models with
  Length Perturbation and N-best Based Label Smoothing

abstract:
  We introduce two techniques, length perturbation and n-best based label
smoothing, to improve generalization of deep neural network (DNN) acoustic
models for automatic speech recognition (ASR). Length perturbation is a data
augmentation algorithm that randomly drops and inserts frames of an utterance
to alter the length of the speech feature sequence. N-best based label
smoothing randomly injects noise to ground truth labels during training in
order to avoid overfitting, where the noisy labels are generated from n-best
hypotheses. We evaluate these two techniques extensively on the 300-hour
Switchboard (SWB300) dataset and an in-house 500-hour Japanese (JPN500) dataset
using recurrent neural network transducer (RNNT) acoustic models for ASR. We
show that both techniques improve the generalization of RNNT models
individually and they can also be complementary. In particular, they yield good
improvements over a strong SWB300 baseline and give state-of-art performance on
SWB300 using RNNT models.


title:
Min-Max Similarity: A Contrastive Learning Based Semi-Supervised
  Learning Network for Surgical Tools Segmentation

abstract:
  Segmentation of images is a popular topic in medical AI. This is mainly due
to the difficulty to obtain a significant number of pixel-level annotated data
to train a neural network. To address this issue, we proposed a semi-supervised
segmentation network based on contrastive learning. In contrast to the previous
state-of-the-art, we introduce a contrastive learning form of dual-view
training by employing classifiers and projectors to build all-negative, and
positive and negative feature pairs respectively to formulate the learning
problem as solving min-max similarity problem. The all-negative pairs are used
to supervise the networks learning from different views and make sure to
capture general features, and the consistency of unlabeled predictions is
measured by pixel-wise contrastive loss between positive and negative pairs. To
quantitative and qualitative evaluate our proposed method, we test it on two
public endoscopy surgical tool segmentation datasets and one cochlear implant
surgery dataset which we manually annotate the cochlear implant in surgical
videos. The segmentation performance (dice coefficients) indicates that our
proposed method outperforms state-of-the-art semi-supervised and fully
supervised segmentation algorithms consistently. The code is publicly available
at: https://github.com/AngeLouCN/Min_Max_Similarity


title: DesCert: Design for Certification

abstract:
  The goal of the DARPA Automated Rapid Certification Of Software (ARCOS)
program is to "automate the evaluation of software assurance evidence to enable
certifiers to determine rapidly that system risk is acceptable." As part of
this program, the DesCert project focuses on the assurance-driven development
of new software. The DesCert team consists of SRI International, Honeywell
Research, and the University of Washington. We have adopted a formal,
tool-based approach to the construction of software artifacts that are
supported by rigorous evidence. The DesCert workflow integrates evidence
generation into a design process that goes from requirements capture and
analysis to the decomposition of the high-level software requirements into
architecture properties and software components with assertional contracts, and
on to software that can be analyzed both dynamically and statically. The
generated evidence is organized by means of an assurance ontology and
integrated into the RACK knowledge base.


title: A note on the factorization of iterated quadratics over finite fields

abstract:
  Let $f$ be a monic quadratic polynomial over a finite field of odd
characteristic. In 2012, Boston and Jones constructed a Markov process based on
the post-critical orbit of $f$, and conjectured that its limiting distribution
explains the factorization of large iterates of $f$. Later on, Boston, Xia, and
the author did extensive Magma computations and found some exceptional families
of quadratics that do not seem to follow the original Markov model conjectured
by Boston and Jones. They did this by empirically observing that certain
factorization patterns predicted by the Boston-Jones model never seem to occur
for these polynomials, and suggested a multi-step Markov model which takes
these missing factorization patterns into account. In this note, we provide
proofs for all these missing factorization patterns. These are the first
provable results that explain why the original conjecture of Boston and Jones
does not hold for \emph{all} monic quadratic polynomials.


title:
The 3D Euler equations with inflow, outflow and vorticity boundary
  conditions

abstract:
  The 3D incompressible Euler equations in a bounded domain are most often
supplemented with impermeable boundary conditions, which constrain the fluid to
neither enter nor leave the domain. We establish the well-posedness of
solutions with inflow, outflow of velocity when either the full value of the
velocity is specified on inflow, or only the normal component is specified
along with the vorticity (and an additional constraint). We do this for
multiply connected domains and establish compatibility conditions to obtain
arbitrarily high H\"{o}lder regularity.


title:
EstrellaNueva: an open-source software to study the interactions and
  detection of neutrinos emitted by supernovae

abstract:
  Supernovae emit large fluxes of neutrinos which can be detected by detectors
on Earth. Future tonne-scale detectors will be sensitive to several neutrino
interaction channels, with thousands of events expected if a supernova emerges
in the galaxy neighborhood. There is a limited number of tools to study the
interaction rates of supernova neutrinos, although a plethora of available
supernova models exists. EstrellaNueva is an open-source software to calculate
expected rates of supernova neutrinos in detectors using target materials with
typical compositions, and additional compositions can be easily added. This
software considers the flavor transformation of neutrinos in the supernova
through the adiabatic Mikheyev--Smirnov--Wolfenstein effect, and their
interaction in detectors through several channels. Most of the interaction
cross sections have been analytically implemented, such as neutrino-electron
and neutrino-proton elastic scattering, inverse beta decay, and coherent
elastic neutrino-nucleus scattering. This software provides a link between
supernova simulations and the expected events in detectors by calculating
fluences and event rates to ease any comparison between theory and observation.
It provides a simple and standalone tool to explore many physics scenarios
offering an option to add analytical cross sections and define any target
material.

categories: astro-ph.IM astro-ph.HE astro-ph.SR hep-ex hep-ph


title: Long-term Visual Map Sparsification with Heterogeneous GNN

abstract:
  We address the problem of map sparsification for long-term visual
localization. For map sparsification, a commonly employed assumption is that
the pre-build map and the later captured localization query are consistent.
However, this assumption can be easily violated in the dynamic world.
Additionally, the map size grows as new data accumulate through time, causing
large data overhead in the long term. In this paper, we aim to overcome the
environmental changes and reduce the map size at the same time by selecting
points that are valuable to future localization. Inspired by the recent
progress in Graph Neural Network(GNN), we propose the first work that models
SfM maps as heterogeneous graphs and predicts 3D point importance scores with a
GNN, which enables us to directly exploit the rich information in the SfM map
graph. Two novel supervisions are proposed: 1) a data-fitting term for
selecting valuable points to future localization based on training queries; 2)
a K-Cover term for selecting sparse points with full map coverage. The
experiments show that our method selected map points on stable and widely
visible structures and outperformed baselines in localization performance.


title:
Visualizations of Complex Sequences of Family-Infant Vocalizations Using
  Bag-of-Audio-Words Approach Based on Wav2vec 2.0 Features

abstract:
  In the U.S., approximately 15-17% of children 2-8 years of age are estimated
to have at least one diagnosed mental, behavioral or developmental disorder.
However, such disorders often go undiagnosed, and the ability to evaluate and
treat disorders in the first years of life is limited. To analyze infant
developmental changes, previous studies have shown advanced ML models excel at
classifying infant and/or parent vocalizations collected using cell phone,
video, or audio-only recording device like LENA. In this study, we pilot test
the audio component of a new infant wearable multi-modal device that we have
developed called LittleBeats (LB). LB audio pipeline is advanced in that it
provides reliable labels for both speaker diarization and vocalization
classification tasks, compared with other platforms that only record audio
and/or provide speaker diarization labels. We leverage wav2vec 2.0 to obtain
superior and more nuanced results with the LB family audio stream. We use a
bag-of-audio-words method with wav2vec 2.0 features to create high-level
visualizations to understand family-infant vocalization interactions. We
demonstrate that our high-quality visualizations capture major types of family
vocalization interactions, in categories indicative of mental, behavioral, and
developmental health, for both labeled and unlabeled LB audio.


title:
Analysis of sloppiness in model simulations: unveiling parameter
  uncertainty when mathematical models are fitted to data

abstract:
  This work introduces a Bayesian approach to assess the sensitivity of model
outputs to changes in parameter values, constrained by the combination of prior
beliefs and data. This novel approach identifies stiff parameter combinations
that strongly affect the quality of the model-data fit while simultaneously
revealing which of these key parameter combinations are informed primarily from
the data or are also substantively influenced by the priors. We focus on the
very common context in complex systems where the amount and quality of data are
low compared to the number of model parameters to be collectively estimated,
and showcase the benefits of our technique for applications in biochemistry,
ecology, and cardiac electrophysiology. We also show how stiff parameter
combinations, once identified, uncover controlling mechanisms underlying the
system being modeled and inform which of the model parameters need to be
prioritized in future experiments for improved parameter inference from
collective model-data fitting.


title: Stable bound orbits in microstate geometries

abstract:
  We show the existence of stable bound orbits for the massive and massless
particles moving in the simplest microstate geometry spacetime in the bosonic
sector of the five-dimensional minimal supergravity. In our analysis, reducing
the motion of particles to a two-dimensional potential problem, we numerically
investigate whether the potential has a negative local minimum.


title: On the relaxed greedy deterministic row and column iterative methods

abstract:
  For solving the large-scale linear system by iteration methods, we utilize
the Petrov-Galerkin conditions and relaxed greedy index selection technique and
provide two relaxed greedy deterministic row (RGDR) and column (RGDC) iterative
methods, in which one special case of RGDR reduces to the fast deterministic
block Kaczmarz method proposed in Chen and Huang (Numer. Algor., 89: 1007-1029,
2021). Our convergence analyses reveal that the resulting algorithms all have
the linear convergence rates, which are bounded by the explicit expressions.
Numerical examples show that the proposed algorithms are more effective than
the relaxed greedy randomized row and column iterative methods.


title:
ASM-Loc: Action-aware Segment Modeling for Weakly-Supervised Temporal
  Action Localization

abstract:
  Weakly-supervised temporal action localization aims to recognize and localize
action segments in untrimmed videos given only video-level action labels for
training. Without the boundary information of action segments, existing methods
mostly rely on multiple instance learning (MIL), where the predictions of
unlabeled instances (i.e., video snippets) are supervised by classifying
labeled bags (i.e., untrimmed videos). However, this formulation typically
treats snippets in a video as independent instances, ignoring the underlying
temporal structures within and across action segments. To address this problem,
we propose \system, a novel WTAL framework that enables explicit, action-aware
segment modeling beyond standard MIL-based methods. Our framework entails three
segment-centric components: (i) dynamic segment sampling for compensating the
contribution of short actions; (ii) intra- and inter-segment attention for
modeling action dynamics and capturing temporal dependencies; (iii) pseudo
instance-level supervision for improving action boundary prediction.
Furthermore, a multi-step refinement strategy is proposed to progressively
improve action proposals along the model training process. Extensive
experiments on THUMOS-14 and ActivityNet-v1.3 demonstrate the effectiveness of
our approach, establishing new state of the art on both datasets. The code and
models are publicly available at~\url{https://github.com/boheumd/ASM-Loc}.


title:
Boundary points, minimal $L^2$ integrals and concavity property
  III---linearity on Riemann surfaces

abstract:
  In this article, we consider a modified version of minimal $L^2$ integrals on
sublevel sets of plurisubharmonic functions related to modules at boundary
points, and obtain a concavity property of the modified version. As an
application, we give a characterization for the concavity degenerating to
linearity on open Riemann surfaces.


title:
Coarse to Fine: Image Restoration Boosted by Multi-Scale Low-Rank Tensor
  Completion

abstract:
  Existing low-rank tensor completion (LRTC) approaches aim at restoring a
partially observed tensor by imposing a global low-rank constraint on the
underlying completed tensor. However, such a global rank assumption suffers the
trade-off between restoring the originally details-lacking parts and neglecting
the potentially complex objects, making the completion performance
unsatisfactory on both sides. To address this problem, we propose a novel and
practical strategy for image restoration that restores the partially observed
tensor in a coarse-to-fine (C2F) manner, which gets rid of such trade-off by
searching proper local ranks for both low- and high-rank parts. Extensive
experiments are conducted to demonstrate the superiority of the proposed C2F
scheme. The codes are available at: https://github.com/RuiLin0212/C2FLRTC.


title: 3D Shape Reconstruction from 2D Images with Disentangled Attribute Flow

abstract:
  Reconstructing 3D shape from a single 2D image is a challenging task, which
needs to estimate the detailed 3D structures based on the semantic attributes
from 2D image. So far, most of the previous methods still struggle to extract
semantic attributes for 3D reconstruction task. Since the semantic attributes
of a single image are usually implicit and entangled with each other, it is
still challenging to reconstruct 3D shape with detailed semantic structures
represented by the input image. To address this problem, we propose 3DAttriFlow
to disentangle and extract semantic attributes through different semantic
levels in the input images. These disentangled semantic attributes will be
integrated into the 3D shape reconstruction process, which can provide definite
guidance to the reconstruction of specific attribute on 3D shape. As a result,
the 3D decoder can explicitly capture high-level semantic features at the
bottom of the network, and utilize low-level features at the top of the
network, which allows to reconstruct more accurate 3D shapes. Note that the
explicit disentangling is learned without extra labels, where the only
supervision used in our training is the input image and its corresponding 3D
shape. Our comprehensive experiments on ShapeNet dataset demonstrate that
3DAttriFlow outperforms the state-of-the-art shape reconstruction methods, and
we also validate its generalization ability on shape completion task.


title: Idea and Theory of Particle Access

abstract:
  Aiming at some problems existing in the current quality of service (QoS)
mechanism of large-scale networks (i.e. poor scalability, coarse granularity
for provided service levels, poor fairness between different service levels,
and improving delay performance at the expense of sacrificing some resource
utilization), the paper puts forward the idea and thoery of particle access. In
the proposed particle access mechanism, the network first granulates the
information flow (that is, the information flow is subdivided into information
particles, each of which is given its corresponding attributes), and allocates
access resources to the information particle group which is composed of all the
information particles to be transmitted, so as to ensure that the occupied
bandwidth resources is minimized on the premise of meeting the delay
requirements of each information particle. Moreover, in the paper, the concepts
of both information particle and information particle group are defined; Basic
properties of the minimum reachable access bandwidth of an information particle
group are analyzed; The influences of time attribute and attribute of bearing
capacity of an information particle group on the minimum reachable access
bandwidth are analyzed; Finally, an effective method for the calculation of the
minimum reachable access bandwidth of an information particle group is given,
and a particle access algorithm based on dynamically adjusting the minimum
reachable access bandwidth is proposed. The research of the paper pave a new
way for further improving QoS mechanisms of large-scale networks, and lay the
corresponding theoretical foundation.


title:
Rotation and electric-field responses and absolute enantioselection in
  chiral crystals

abstract:
  Microscopic origin of chirality and possible electric-field induced rotation
and rotation-field induced electric polarization are investigated. By building
up a realistic tight-binding model for elemental Te crystal in terms of
symmetry-adopted basis, we identify the microscopic origin of the chirality and
essential couplings among polar and axial vectors with the same time-reversal
properties. Based on this microscopic model, we elucidate quantitatively that
the inter-band process, driven by the nearest-neighbor spin-dependent imaginary
hopping, is the key factor in the electric-field induced rotation and its
inverse response. From the symmetry point of view, these couplings are
characteristic common to any chiral material, leading to a possible
experimental approach to achieve absolute enantioselection by simultaneously
applied electric and rotation fields, or magnetic field and electric current,
and so on, as a conjugate field of the chirality.


title:
Mismatched Rate-Distortion Theory: Ensembles, Bounds, and General
  Alphabets

abstract:
  In this paper, we consider the mismatched rate-distortion problem, in which
the encoding is done using a codebook, and the encoder chooses the
minimum-distortion codeword according to a mismatched distortion function that
differs from the true one. For the case of discrete memoryless sources, we
establish achievable rate-distortion bounds using multi-user coding techniques,
namely, superposition coding and expurgated parallel coding. We give examples
where these attain the matched rate-distortion trade-off but a standard
ensemble with independent codewords fails to do so. On the other hand, in
contrast with the channel coding counterpart, we show that there are cases
where structured codebooks can perform worse than their unstructured
counterparts. In addition, in view of the difficulties in adapting the existing
and above-mentioned results to general alphabets, we consider a simpler
i.i.d.~random coding ensemble, and establish its achievable rate-distortion
bounds for general alphabets.


title: Evolution of A Peculiar Type Ibn Supernova SN 2019wep

abstract:
  We present a high-cadence short term photometric and spectroscopic monitoring
campaign of a type Ibn SN 2019wep, which is one of the rare SN Ibn after SNe
2010al and 2019uo to display signatures of flash ionization (\ion{He}{2},
\ion{C}{3}, \ion{N}{3}). We compare the decline rates and rise time of SN
2019wep with other SNe Ibn and fast transients. The post-peak decline in all
bands (0.1 mag d$^{-1}$) are consistent with SNe Ibn but less than the fast
transients. On the other hand, the $\Delta$m$_{15}$ values are slightly lower
than the average values for SNe Ibn but consistent with the fast transients.
The rise time is typically shorter than SNe Ibn but longer than fast
transients. SN 2019wep lies at the fainter end of SNe Ibn but possesses an
average luminosity amongst the fast transients sample. The peculiar color
evolution places it between SNe Ib and the most extreme SNe Ibn. The bolometric
light curve modelling shows resemblance with SN 2019uo with ejecta masses
consistent with SNe Ib. SN 2019wep belongs to the "P cygni" sub-class of SNe
Ibn and shows faster evolution in line velocities as compared to the "emission"
sub-class. The post-maximum spectra show close resemblance with ASASSN-15ed
hinting it to be of SN Ib nature. The low \ion{He}{1} CSM velocities and
residual H$\alpha$ further justifies it and gives evidence of an intermittent
progenitor between WR and LBV star.


title: AnoDFDNet: A Deep Feature Difference Network for Anomaly Detection

abstract:
  This paper proposed a novel anomaly detection (AD) approach of High-speed
Train images based on convolutional neural networks and the Vision Transformer.
Different from previous AD works, in which anomalies are identified with a
single image using classification, segmentation, or object detection methods,
the proposed method detects abnormal difference between two images taken at
different times of the same region. In other words, we cast anomaly detection
problem with a single image into a difference detection problem with two
images. The core idea of the proposed method is that the 'anomaly' usually
represents an abnormal state instead of a specific object, and this state
should be identified by a pair of images. In addition, we introduced a deep
feature difference AD network (AnoDFDNet) which sufficiently explored the
potential of the Vision Transformer and convolutional neural networks. To
verify the effectiveness of the proposed AnoDFDNet, we collected three
datasets, a difference dataset (Diff Dataset), a foreign body dataset (FB
Dataset), and an oil leakage dataset (OL Dataset). Experimental results on
above datasets demonstrate the superiority of proposed method. Source code are
available at https://github.com/wangle53/AnoDFDNet.


title:
Unveiling Topological Charges and Their Manipulation in Electronic
  Circuits

abstract:
  Leveraging topological properties in the response of electromagnetic systems
can greatly enhance their potential. Although the investigation of
singularity-based electromagnetics and non-Hermitian electronics has
considerably increased in recent years in the context of various scattering
anomalies, their topological properties have not been fully assessed. In this
work, we theoretically and experimentally demonstrate that non-Hermitian
perturbations around bound states in the continuum can lead to singularities of
the scattering matrix, which are topologically nontrivial and comply with
charge conservation. The associated scattering matrix poles, zeros and
pole-zero pairs delineate extreme scattering events, including lasing, coherent
perfect absorption, and absorber-lasers. The presented framework enables a
recipe for generation, annihilation and addition of these singularities in
electric circuits, with potential for extreme scattering engineering across a
broad range of the electromagnetic spectrum for sensing, wireless power
transfer, lasing and thermal emission devices.


title: Distillation of Indistinguishable Photons

abstract:
  A reliable source of identical (indistinguishable) photons is a prerequisite
for exploiting interference effects, which is a necessary component for linear
optical based quantum computing, and applications thereof such as Boson
sampling. Generally speaking, the degree of distinguishability will determine
the efficacy of the particular approach, for example by limiting the fidelity
of constructed resource states, or reducing the complexity of an optical
circuits output distribution. It is therefore of great practical relevance to
engineer sources of highly indistinguishable photons. Inspired by magic state
distillation, we present a protocol using standard linear optics (such as
beamsplitters) which can be used to increase the indistinguishability of a
photon source, to arbitrary accuracy. In particular, in the asymptotic limit of
small error, we show that using 9 (16) photons one can distill a single purer
photon, with error decreasing by 1/3 (1/4) per iteration. We demonstrate the
scheme is robust to detection errors (such as dark counts) to second order.


title:
Model-Based Control of Planar Piezoelectric Inchworm Soft Robot for
  Crawling in Constrained Environments

abstract:
  Soft robots have drawn significant attention recently for their ability to
achieve rich shapes when interacting with complex environments. However, their
elasticity and flexibility compared to rigid robots also pose significant
challenges for precise and robust shape control in real-time. Motivated by
their potential to operate in highly-constrained environments, as in
search-and-rescue operations, this work addresses these challenges of soft
robots by developing a model-based full-shape controller, validated and
demonstrated by experiments. A five-actuator planar soft robot was constructed
with planar piezoelectric layers bonded to a steel foil substrate, enabling
inchworm-like motion. The controller uses a soft-body continuous model for
shape planning and control, given target shapes and/or environmental
constraints, such as crawling under overhead barriers or "roof" safety lines.
An approach to background model calibrations is developed to address deviations
of actual robot shape due to material parameter variations and drift. Full
experimental shape control and optimal movement under a roof safety line are
demonstrated, where the robot maximizes its speed within the overhead
constraint. The mean-squared error between the measured and target shapes
improves from ~0.05 cm$^{2}$ without calibration to ~0.01 cm$^{2}$ with
calibration. Simulation-based validation is also performed with various
different roof shapes.


title: Noise assisted quantum coherence protection in hierarchical environment

abstract:
  In this paper, we investigate coherence protection of a quantum system
coupled to a hierarchical environment by utilizing noise. As an example, we
solve the Jaynes-Cummings (J-C) model in presence of both a classical and a
quantized noise. The master equation is derived beyond the Markov
approximation, where the influence of memory effects from both noises is taken
into account. More importantly, we find that the performance of the coherence
protection sensitively depends on the non-Markovian properties of both noises.
By analyzing the mathematical mechanism of the coherence protection, we show
the decoherence caused by a non-Markovian noise with longer memory time can be
suppressed by another Markovian noise with shorter memory time. Last but not
least, as an outlook, we try to analyze the connection between the atom-cavity
entanglement and the atomic coherence, then discuss the possible clue to search
for the required noise. The results presented in this paper show the
possibility of protecting coherence by utilizing noise and may open a new path
to design noise-assisted coherence protection schemes.


title: Search Methods for Policy Decompositions

abstract:
  Computing optimal control policies for complex dynamical systems requires
approximation methods to remain computationally tractable. Several
approximation methods have been developed to tackle this problem. However,
these methods do not reason about the suboptimality induced in the resulting
control policies due to these approximations. We introduced Policy
Decomposition, an approximation method that provides a suboptimality estimate,
in our earlier work. Policy decomposition proposes strategies to break an
optimal control problem into lower-dimensional subproblems, whose optimal
solutions are combined to build a control policy for the original system.
However, the number of possible strategies to decompose a system scale quickly
with the complexity of a system, posing a combinatorial challenge. In this work
we investigate the use of Genetic Algorithm and Monte-Carlo Tree Search to
alleviate this challenge. We identify decompositions for swing-up control of a
4 degree-of-freedom manipulator, balance control of a simplified biped, and
hover control of a quadcopter.


title: Light Field Depth Estimation Based on Stitched-EPI

abstract:
  Depth estimation is one of the most essential problems for light field
applications. In EPI-based methods, the slope computation usually suffers low
accuracy due to the discretization error and low angular resolution. In
addition, recent methods work well in most regions but often struggle with
blurry edges over occluded regions and ambiguity over texture-less regions. To
address these challenging issues, we first propose the stitched-EPI and
half-stitched-EPI algorithms for non-occluded and occluded regions,
respectively. The algorithms improve slope computation by shifting and
concatenating lines in different EPIs but related to the same point in 3D
scene, while the half-stitched-EPI only uses non-occluded part of lines.
Combined with the joint photo-consistency cost proposed by us, the more
accurate and robust depth map can be obtained in both occluded and non-occluded
regions. Furthermore, to improve the depth estimation in texture-less regions,
we propose a depth propagation strategy that determines their depth from the
edge to interior, from accurate regions to coarse regions. Experimental and
ablation results demonstrate that the proposed method achieves accurate and
robust depth maps in all regions effectively.


title: SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation

abstract:
  This paper studies a practical domain adaptive (DA) semantic segmentation
problem where only pseudo-labeled target data is accessible through a black-box
model. Due to the domain gap and label shift between two domains,
pseudo-labeled target data contains mixed closed-set and open-set label noises.
In this paper, we propose a simplex noise transition matrix (SimT) to model the
mixed noise distributions in DA semantic segmentation and formulate the problem
as estimation of SimT. By exploiting computational geometry analysis and
properties of segmentation, we design three complementary regularizers, i.e.
volume regularization, anchor guidance, convex guarantee, to approximate the
true SimT. Specifically, volume regularization minimizes the volume of simplex
formed by rows of the non-square SimT, which ensures outputs of segmentation
model to fit into the ground truth label distribution. To compensate for the
lack of open-set knowledge, anchor guidance and convex guarantee are devised to
facilitate the modeling of open-set noise distribution and enhance the
discriminative feature learning among closed-set and open-set classes. The
estimated SimT is further utilized to correct noise issues in pseudo labels and
promote the generalization ability of segmentation model on target domain data.
Extensive experimental results demonstrate that the proposed SimT can be
flexibly plugged into existing DA methods to boost the performance. The source
code is available at https://github.com/CityU-AIM-Group/SimT.


title:
Periocular Biometrics and its Relevance to Partially Masked Faces: A
  Survey

abstract:
  The performance of face recognition systems can be negatively impacted in the
presence of masks and other types of facial coverings that have become
prevalent due to the COVID-19 pandemic. In such cases, the periocular region of
the human face becomes an important biometric cue. In this article, we present
a detailed review of periocular biometrics. We first examine the various face
and periocular techniques specially designed to recognize humans wearing a face
mask. Then, we review different aspects of periocular biometrics: (a) the
anatomical cues present in the periocular region useful for recognition, (b)
the various feature extraction and matching techniques developed, (c)
recognition across different spectra, (d) fusion with other biometric
modalities (face or iris), (e) recognition on mobile devices, (f) its
usefulness in other applications, (g) periocular datasets, and (h) competitions
organized for evaluating the efficacy of this biometric modality. Finally, we
discuss various challenges and future directions in the field of periocular
biometrics.


title:
Mitigation of plasma-wall interactions with low-Z powders in DIII-D high
  confinement plasmas

abstract:
  Experiments with low-Z powder injection in DIII-D high confinement discharges
demonstrated increased divertor dissipation and detachment while maintaining
good core energy confinement. Lithium (Li), boron (B), and boron nitride (BN)
powders were injected in high-confinement mode plasmas ($I_p=$1 MA, $B_t=$2 T,
$P_{NB}=$6 MW, $\langle n_e\rangle=3.6-5.0\cdot10^{19}$ m$^{-3}$) into the
upper small-angle slot (SAS) divertor for 2-s intervals at constant rates of
3-204 mg/s. The multi-species BN powders at a rate of 54 mg/s showed the most
substantial increase in divertor neutral compression by more than an order of
magnitude and lasting detachment with minor degradation of the stored magnetic
energy $W_{mhd}$ by 5%. Rates of 204 mg/s of boron nitride powder further
reduce ELM-fluxes on the divertor but also cause a drop in confinement
performance by 24% due to the onset of an $n=2$ tearing mode. The application
of powders also showed a substantial improvement of wall conditions manifesting
in reduced wall fueling source and intrinsic carbon and oxygen content in
response to the cumulative injection of non-recycling materials. The results
suggest that low-Z powder injection, including mixed element compounds, is a
promising new core-edge compatible technique that simultaneously enables
divertor detachment and improves wall conditions during high confinement
operation.


title: SPAct: Self-supervised Privacy Preservation for Action Recognition

abstract:
  Visual private information leakage is an emerging key issue for the fast
growing applications of video understanding like activity recognition. Existing
approaches for mitigating privacy leakage in action recognition require privacy
labels along with the action labels from the video dataset. However, annotating
frames of video dataset for privacy labels is not feasible. Recent developments
of self-supervised learning (SSL) have unleashed the untapped potential of the
unlabeled data. For the first time, we present a novel training framework which
removes privacy information from input video in a self-supervised manner
without requiring privacy labels. Our training framework consists of three main
components: anonymization function, self-supervised privacy removal branch, and
action recognition branch. We train our framework using a minimax optimization
strategy to minimize the action recognition cost function and maximize the
privacy cost function through a contrastive self-supervised loss. Employing
existing protocols of known-action and privacy attributes, our framework
achieves a competitive action-privacy trade-off to the existing
state-of-the-art supervised methods. In addition, we introduce a new protocol
to evaluate the generalization of learned the anonymization function to
novel-action and privacy attributes and show that our self-supervised framework
outperforms existing supervised methods. Code available at:
https://github.com/DAVEISHAN/SPAct


title: Shifted Chunk Encoder for Transformer Based Streaming End-to-End ASR

abstract:
  Currently, there are mainly three Transformer encoder based streaming End to
End (E2E) Automatic Speech Recognition (ASR) approaches, namely time-restricted
methods, chunk-wise methods, and memory based methods. However, all of them
have some limitations in aspects of global context modeling, linear
computational complexity, and model parallelism. In this work, we aim to build
a single model to achieve the benefits of all the three aspects for streaming
E2E ASR. Particularly, we propose to use a shifted chunk mechanism instead of
the conventional chunk mechanism for streaming Transformer and Conformer. This
shifted chunk mechanism can significantly enhance modeling power through
allowing chunk self-attention to capture global context across local chunks,
while keeping linear computational complexity and parallel trainable. We name
the Shifted Chunk Transformer and Conformer as SChunk-Transofromer and
SChunk-Conformer, respectively. And we verify their performance on the widely
used AISHELL-1 benckmark. Experiments show that the SChunk-Transformer and
SChunk-Conformer achieve CER 6.43% and 5.77%, respectively. That surpasses the
existing chunk-wise and memory based methods by a large margin, and is
competitive even compared with the state-of-the-art time-restricted methods
which have quadratic computational complexity.


title: Generalizing Few-Shot NAS with Gradient Matching

abstract:
  Efficient performance estimation of architectures drawn from large search
spaces is essential to Neural Architecture Search. One-Shot methods tackle this
challenge by training one supernet to approximate the performance of every
architecture in the search space via weight-sharing, thereby drastically
reducing the search cost. However, due to coupled optimization between child
architectures caused by weight-sharing, One-Shot supernet's performance
estimation could be inaccurate, leading to degraded search outcomes. To address
this issue, Few-Shot NAS reduces the level of weight-sharing by splitting the
One-Shot supernet into multiple separated sub-supernets via edge-wise
(layer-wise) exhaustive partitioning. Since each partition of the supernet is
not equally important, it necessitates the design of a more effective splitting
criterion. In this work, we propose a gradient matching score (GM) that
leverages gradient information at the shared weight for making informed
splitting decisions. Intuitively, gradients from different child models can be
used to identify whether they agree on how to update the shared modules, and
subsequently to decide if they should share the same weight. Compared with
exhaustive partitioning, the proposed criterion significantly reduces the
branching factor per edge. This allows us to split more edges (layers) for a
given budget, resulting in substantially improved performance as NAS search
spaces usually include dozens of edges (layers). Extensive empirical
evaluations of the proposed method on a wide range of search spaces
(NASBench-201, DARTS, MobileNet Space), datasets (cifar10, cifar100, ImageNet)
and search algorithms (DARTS, SNAS, RSPS, ProxylessNAS, OFA) demonstrate that
it significantly outperforms its Few-Shot counterparts while surpassing
previous comparable methods in terms of the accuracy of derived architectures.


title:
Band theory and boundary modes of high-dimensional representations of
  infinite hyperbolic lattices

abstract:
  Periodic lattices in hyperbolic space are characterized by symmetries beyond
Euclidean crystallographic groups, offering a new platform for classical and
quantum waves, demonstrating great potentials for a new class of topological
metamaterials. One important feature of hyperbolic lattices is that their
translation group is nonabelian, permitting high-dimensional irreducible
representations (irreps), in contrast to abelian translation groups in
Euclidean lattices. Here we introduce a general framework to construct wave
eigenstates of high-dimensional irreps of infinite hyperbolic lattices, thereby
generalizing Bloch's theorem, and discuss its implications on unusual
mode-counting and degeneracy, as well as bulk-edge correspondence in hyperbolic
lattices. We apply this method to a mechanical hyperbolic lattice, and
characterize its band structure and zero modes of high-dimensional irreps.

categories: cond-mat.mes-hall cond-mat.soft cond-mat.str-el math-ph math.MP


title:
OrphicX: A Causality-Inspired Latent Variable Model for Interpreting
  Graph Neural Networks

abstract:
  This paper proposes a new eXplanation framework, called OrphicX, for
generating causal explanations for any graph neural networks (GNNs) based on
learned latent causal factors. Specifically, we construct a distinct generative
model and design an objective function that encourages the generative model to
produce causal, compact, and faithful explanations. This is achieved by
isolating the causal factors in the latent space of graphs by maximizing the
information flow measurements. We theoretically analyze the cause-effect
relationships in the proposed causal graph, identify node attributes as
confounders between graphs and GNN predictions, and circumvent such confounder
effect by leveraging the backdoor adjustment formula. Our framework is
compatible with any GNNs, and it does not require access to the process by
which the target GNN produces its predictions. In addition, it does not rely on
the linear-independence assumption of the explained features, nor require prior
knowledge on the graph learning tasks. We show a proof-of-concept of OrphicX on
canonical classification problems on graph data. In particular, we analyze the
explanatory subgraphs obtained from explanations for molecular graphs (i.e.,
Mutag) and quantitatively evaluate the explanation performance with frequently
occurring subgraph patterns. Empirically, we show that OrphicX can effectively
identify the causal semantics for generating causal explanations, significantly
outperforming its alternatives.


title:
Camera-Conditioned Stable Feature Generation for Isolated Camera
  Supervised Person Re-IDentification

abstract:
  To learn camera-view invariant features for person Re-IDentification (Re-ID),
the cross-camera image pairs of each person play an important role. However,
such cross-view training samples could be unavailable under the ISolated Camera
Supervised (ISCS) setting, e.g., a surveillance system deployed across distant
scenes. To handle this challenging problem, a new pipeline is introduced by
synthesizing the cross-camera samples in the feature space for model training.
Specifically, the feature encoder and generator are end-to-end optimized under
a novel method, Camera-Conditioned Stable Feature Generation (CCSFG). Its joint
learning procedure raises concern on the stability of generative model
training. Therefore, a new feature generator, $\sigma$-Regularized Conditional
Variational Autoencoder ($\sigma$-Reg.~CVAE), is proposed with theoretical and
experimental analysis on its robustness. Extensive experiments on two ISCS
person Re-ID datasets demonstrate the superiority of our CCSFG to the
competitors.


title:
Examples of open manifolds with positive Ricci curvature and non-proper
  Busemann functions

abstract:
  We give the first example of an open manifold with positive Ricci curvature
and a non-proper Busemann function at a point. This provides counterexamples to
a longtime well-known open question whether the Busemann function at a point of
an open manifold with nonnegative Ricci curvature is proper.


title: Are Edge Weights in Summary Graphs Useful? -- A Comparative Study

abstract:
  Which one is better between two representative graph summarization models
with and without edge weights? From web graphs to online social networks, large
graphs are everywhere. Graph summarization, which is an effective graph
compression technique, aims to find a compact summary graph that accurately
represents a given large graph. Two versions of the problem, where one allows
edge weights in summary graphs and the other does not, have been studied in
parallel without direct comparison between their underlying representation
models. In this work, we conduct a systematic comparison by extending three
search algorithms to both models and evaluating their outputs on eight datasets
in five aspects: (a) reconstruction error, (b) error in node importance, (c)
error in node proximity, (d) the size of reconstructed graphs, and (e)
compression ratios. Surprisingly, using unweighted summary graphs leads to
outputs significantly better in all the aspects than using weighted ones, and
this finding is supported theoretically. Notably, we show that a
state-of-the-art algorithm can be improved substantially (specifically, 8.2X,
7.8X, and 5.9X in terms of (a), (b), and (c), respectively, when (e) is fixed)
based on the observation.


title: Fans and polytopes in tilting theory

abstract:
  For a finite dimensional algebra $A$ over a field $k$, the 2-term silting
complexes of $A$ gives a simplicial complex $\Delta(A)$ called the
$g$-simplicial complex. We give tilting theoretic interpretations of the
$h$-vectors and Dehn-Sommerville equations of $\Delta(A)$. Using $g$-vectors of
2-term silting complexes, $\Delta(A)$ gives a nonsingular fan $\Sigma(A)$ in
the real Grothendieck group $K_0(\mathrm{proj } A)_\mathbb{R}$ called the
$g$-fan. For example, the fan of $g$-vectors of a cluster algebra is given by
the $g$-fan of a Jacobian algebra of a non-degenerate quiver with potential. We
give several properties of $\Sigma(A)$ including idempotent reductions,
sign-coherence, Jasso reductions and a connection with Newton polytopes of
$A$-modules. Moreover, $\Sigma(A)$ gives a (possibly infinite and non-convex)
polytope $P(A)$ in $K_0(\mathrm{proj } A)_\mathbb{R}$ called the $g$-polytope
of $A$. We call $A$ $g$-convex if $P(A)$ is convex. In this case, we show that
it is a reflexive polytope, and that the dual polytope is given by the 2-term
simple minded collections of $A$.
  We give an explicit classification of $g$-convex algebras of rank $2$. We
classify algebras whose $g$-polytopes are smooth Fano. We classify classical
and generalized preprojective algebras which are $g$-convex, and also describe
their $g$-polytope as the dual polytopes of short root polytopes of type $A$
and $B$. We also classify Brauer graph algebras which are $g$-convex, and
describe their $g$-polytopes as root polytopes of type $A$ and $C$.


title:
Search for the chiral magnetic effect in collisions between two isobars
  with nuclear structures

abstract:
  Isobar collisions which were thought with a same background and different
magnetic fields provide an opportunity to verify the chiral magnetic effect
(CME) in relativistic heavy-ion collisions. However, the first result from the
RHIC-STAR isobar experiment did not observed the CME signal, but discovered
that the backgrounds are different between
$_{44}^{96}\textrm{Ru}+_{44}^{96}\textrm{Ru}$ and
$_{40}^{96}\textrm{Zr}+_{40}^{96}\textrm{Zr}$ collisions. We test eighteen
cases of Woods-Saxon parameter settings resulted from different nuclear
deformation or nuclear structure effects using a mutiphase transport model. We
find out that seven cases can reasonably reproduce three reference ratios
measured by the STAR experiment. With considering both the halo-type neutron
skin structure and CME-like charge separation, we demonstrate that it is
difficult for the CME observables ($\Delta\delta$, $\Delta\delta$ ratio,
$\Delta\gamma$ and $\Delta\gamma/v_{2}$ ratio) to distinguish the presence or
absence of the CME, if the CME strength is weak in isobar collisions. It is
because the final state interactions significantly weaken the initial CME
signal, resulting in non-linear sensitivities of the CME observables to the CME
strength. Therefore, more sensitive observables are required to search for the
possible small CME signal in isobar collisions.


title: Spatial-Aware Local Community Detection Guided by Dominance Relation

abstract:
  The problem of finding the spatial-aware community for a given node has been
defined and investigated in geo-social networks. However, existing studies
suffer from two limitations: a) the criteria of defining communities are
determined by parameters, which are difficult to set; b) algorithms may require
global information and are not suitable for situations where the network is
incomplete. Therefore, we propose spatial-aware local community detection
(SLCD), which finds the spatial-aware local community with only local
information and defines the community based on the difference in the sparseness
of edges inside and outside the community. Specifically, to address the SLCD
problem, we design a novel spatial aware local community detection algorithm
based on dominance relation, but this algorithm incurs high cost. To further
improve the efficiency, we propose an approximate algorithm. Experimental
results demonstrate that the proposed approximate algorithm outperforms the
comparison algorithms.


title: Affine Medical Image Registration with Coarse-to-Fine Vision Transformer

abstract:
  Affine registration is indispensable in a comprehensive medical image
registration pipeline. However, only a few studies focus on fast and robust
affine registration algorithms. Most of these studies utilize convolutional
neural networks (CNNs) to learn joint affine and non-parametric registration,
while the standalone performance of the affine subnetwork is less explored.
Moreover, existing CNN-based affine registration approaches focus either on the
local misalignment or the global orientation and position of the input to
predict the affine transformation matrix, which are sensitive to spatial
initialization and exhibit limited generalizability apart from the training
dataset. In this paper, we present a fast and robust learning-based algorithm,
Coarse-to-Fine Vision Transformer (C2FViT), for 3D affine medical image
registration. Our method naturally leverages the global connectivity and
locality of the convolutional vision transformer and the multi-resolution
strategy to learn the global affine registration. We evaluate our method on 3D
brain atlas registration and template-matching normalization. Comprehensive
results demonstrate that our method is superior to the existing CNNs-based
affine registration methods in terms of registration accuracy, robustness and
generalizability while preserving the runtime advantage of the learning-based
methods. The source code is available at https://github.com/cwmok/C2FViT.


title: The analytic radial acceleration relation for galaxy clusters

abstract:
  Recently, a tight correlation between the dynamical radial acceleration and
the baryonic radial acceleration in galaxies - the radial acceleration relation
- has been discovered. This has been claimed as an indirect support of the
modified gravity theories. However, whether the radial acceleration relation
could also be found in galaxy clusters is controversial. In this article, we
derive and present an analytic radial acceleration relation for the central
region of galaxy clusters. We examine the data of some large galaxy clusters
and we find that the resulting radial acceleration relation has a very large
scatter. Moreover, although the radial acceleration relation for galaxy
clusters shows some agreement with the one discovered in galaxies for a certain
range of baryonic radial acceleration, their functional forms are somewhat
different from each other. This suggests that the radial acceleration relation
may not be a universal relation in general.


title:
Detection of extended millimeter emission in the host galaxy of 3C273
  and its implications for QSO feedback via high dynamic range ALMA imaging

abstract:
  We estimate the amount of negative feedback energy injected into the ISM of
the host galaxy of 3C273, a prototypical radio loud quasar. We obtained 93, 233
and 343 GHz continuum images with the Atacama Large Millimeter/Sub-millimeter
Array (ALMA). After self calibration and point source subtraction, we reach an
image dynamic range of $\sim 85000$ at 93\ GHz, $\sim 39000$ at 233\ GHz and
$\sim 2500$ at 343\ GHz. These are currently the highest image dynamic range
obtained using ALMA. We detect spatially extended millimeter emission
associated with the host galaxy, cospatial with the Extended Emission Line
Region (EELR) observed in the optical. The millimeter spectral energy
distribution and comparison with centimeter data show that the extended
emission cannot be explained by dust thermal emission, synchrotron or thermal
bremsstrahlung arising from massive star formation. We interpret the extended
millimeter emission as thermal bremsstrahlung from gas directly ionized by the
central source. The extended flux indicates that at least $\sim 7\%$ of the
bolometric flux of the nuclear source was used to ionize atomic hydrogen in the
host galaxy. The ionized gas is estimated to be as massive as $10^{10}$ to
$10^{11}\ \mathrm{M_\odot}$, but the molecular gas fraction with respect to the
stellar mass is consistent with other ellipticals, suggesting that direct
ionization ISM by the QSO may not be sufficient to suppress star formation, or
we are witnessing a short timescale before negative feedback becomes
observable. The discovery of a radio counterpart to EELRs provides a new
pathway to studying the QSO-host ISM interaction.


title: AR Point&Click: An Interface for Setting Robot Navigation Goals

abstract:
  This paper considers the problem of designating navigation goal locations for
interactive mobile robots. We propose a point-and-click interface, implemented
with an Augmented Reality (AR) headset. The cameras on the AR headset are used
to detect natural pointing gestures performed by the user. The selected goal is
visualized through the AR headset, allowing the users to adjust the goal
location if desired. We conduct a user study in which participants set
consecutive navigation goals for the robot using three different interfaces: AR
Point & Click, Person Following and Tablet (birdeye map view). Results show
that the proposed AR Point&Click interface improved the perceived accuracy,
efficiency and reduced mental load compared to the baseline tablet interface,
and it performed on-par to the Person Following method. These results show that
the AR Point\&Click is a feasible interaction model for setting navigation
goals.


title:
Comparing the effects of Boltzmann machines as associative memory in
  Generative Adversarial Networks between classical and quantum sampling

abstract:
  We investigate the quantum effect on machine learning (ML) models exemplified
by the Generative Adversarial Network (GAN), which is a promising deep learning
framework. In the general GAN framework the generator maps uniform noise to a
fake image. In this study, we utilize the Associative Adversarial Network
(AAN), which consists of a standard GAN and an associative memory. Further, we
set a Boltzmann Machine (BM), which is an undirected graphical model that
learns low-dimensional features extracted from a discriminator, as the memory.
Owing to difficulty calculating the BM's log-likelihood gradient, it is
necessary to approximate it by using the sample mean obtained from the BM,
which has tentative parameters. To calculate the sample mean, a Markov Chain
Monte Carlo (MCMC) is often used. In a previous study, this was performed using
a quantum annealer device, and the performance of the "Quantum" AAN was
compared to that of the standard GAN. However, its better performance than the
standard GAN is not well understood. In this study, we introduce two methods to
draw samples: classical sampling via MCMC and quantum sampling via quantum
Monte Carlo (QMC) simulation, which is quantum simulation on the classical
computer. Then, we compare these methods to investigate whether quantum
sampling is advantageous. Specifically, calculating the discriminator loss, the
generator loss, inception score and Fr\'echet inception distance, we discuss
the possibility of AAN. We show that the AANs trained by both MCMC and QMC are
more stable during training and produce more varied images than the standard
GANs. However, the results indicate no difference in sampling by QMC simulation
compared to that by MCMC.


title:
Few Could Be Better Than All: Feature Sampling and Grouping for Scene
  Text Detection

abstract:
  Recently, transformer-based methods have achieved promising progresses in
object detection, as they can eliminate the post-processes like NMS and enrich
the deep representations. However, these methods cannot well cope with scene
text due to its extreme variance of scales and aspect ratios. In this paper, we
present a simple yet effective transformer-based architecture for scene text
detection. Different from previous approaches that learn robust deep
representations of scene text in a holistic manner, our method performs scene
text detection based on a few representative features, which avoids the
disturbance by background and reduces the computational cost. Specifically, we
first select a few representative features at all scales that are highly
relevant to foreground text. Then, we adopt a transformer for modeling the
relationship of the sampled features, which effectively divides them into
reasonable groups. As each feature group corresponds to a text instance, its
bounding box can be easily obtained without any post-processing operation.
Using the basic feature pyramid network for feature extraction, our method
consistently achieves state-of-the-art results on several popular datasets for
scene text detection.


title:
Mining the information content of member galaxies in the halo mass
  modelling

abstract:
  Motivated by previous findings that the magnitude gap between certain
satellite galaxy and the central galaxy can be used to improve the estimation
of halo mass, we carry out a systematic study of the information content of
different member galaxies in the modelling of the host halo mass using a
machine learning approach. We employ data from the hydrodynamical simulation
IllustrisTNG and train a Random Forest (RF) algorithm to predict a halo mass
from the stellar masses of its member galaxies. Exhaustive feature selection is
adopted to disentangle the importances of different galaxy members. We confirm
that an additional satellite does improve the halo mass estimation compared to
that estimated by the central alone. However, the magnitude of this improvement
does not differ significantly using different satellite galaxies. When three
galaxies are used in the halo mass prediction, the best combination is always
that of the central galaxy with the most massive satellite and the smallest
satellite. Furthermore, among the top 7 galaxies, the combination of a central
galaxy and two or three satellite galaxies gives a near-optimal estimation of
halo mass, and further addition of galaxies does not raise the precision of the
prediction. We demonstrate that these dependences can be understood from the
shape variation of the conditional satellite distribution, with different
member galaxies accounting for distinct halo-dependent features in different
parts of the cumulative stellar mass function.


title: Euclidean rings of $S$-integers in complex quadratic fields

abstract:
  We give an elementary approach to studying whether rings of $S$-integers in
complex quadratic fields are Euclidean with respect to the $S$-norm.


title:
Panoptic NeRF: 3D-to-2D Label Transfer for Panoptic Urban Scene
  Segmentation

abstract:
  Large-scale training data with high-quality annotations is critical for
training semantic and instance segmentation models. Unfortunately, pixel-wise
annotation is labor-intensive and costly, raising the demand for more efficient
labeling strategies. In this work, we present a novel 3D-to-2D label transfer
method, Panoptic NeRF, which aims for obtaining per-pixel 2D semantic and
instance labels from easy-to-obtain coarse 3D bounding primitives. Our method
utilizes NeRF as a differentiable tool to unify coarse 3D annotations and 2D
semantic cues transferred from existing datasets. We demonstrate that this
combination allows for improved geometry guided by semantic information,
enabling rendering of accurate semantic maps across multiple views.
Furthermore, this fusion process resolves label ambiguity of the coarse 3D
annotations and filters noise in the 2D predictions. By inferring in 3D space
and rendering to 2D labels, our 2D semantic and instance labels are multi-view
consistent by design. Experimental results show that Panoptic NeRF outperforms
existing semantic and instance label transfer methods in terms of accuracy and
multi-view consistency on challenging urban scenes of the KITTI-360 dataset.


title:
An accurate relativistic chiral nucleon-nucleon interaction up to the
  next-to-next-to-leading order

abstract:
  We report on the construction of an accurate relativistic chiral
nucleon-nucleon interaction up to the next-to-next-to-leading (NNLO) order. We
compare the so-obtained neutron-proton phaseshifts with the
next-to-next-to-next-to-leading order (N$^3$LO) nonrelativistic ones and we
show that up to $T_\mathrm{lab.}=200$ MeV, the relativistic chiral nuclear
force can describe the PWA93 phaseshifts and inelasticities as well as its
N$^3$LO nonrelativistic counterparts. As a result, the relativistic chiral
nuclear force can be readily used for relativistic ab initio nuclear structure,
reaction, as well astrophysical studies.


title: On-chip Light Trapping in Bilayer Moir\'e Photonic Crystal Slabs

abstract:
  There has been remarkable recent progress in the formation of nano-resonators
that support ultra-low-loss, compact dielectric photonic crystals with
exceptional high-Q modes that operate at visible or telecom wavelengths. New
insights into modal engineering have recently emerged from researchers
exploring exotic electronic phases in 2D materials. The phenomenon relates to a
twist in the angle between two layers of materials with high periodic spatial
ordering, such as graphene. A moire pattern forms, and at a particular magic
angle of twist, the electronic behavior significantly changes, enjoying a flat
energy-momentum dispersion relationship. There is an optical analog to the
electron twistronics: bilayer moire photonic crystal slabs can realize a
flat-band condition. Under these conditions, the propagating modes have zero
group velocity, thus giving rise to momentum-free trapping of Bloch waves in
both transverse and vertical directions, creating high quality-factors and
exceptionally small modal volumes that eventually lead to the enhancement of
the Purcell effect. The dramatically different means of light-localization
afforded by moire-structured cavities, very small mode volumes, and spatial
determination by the overall moire pattern can manipulate spontaneous emission.
This provides many opportunities for applications such as low-threshold lasing,
single-photon source, quantum electrodynamics, photonic circuit, and quantum
information processing.


title:
Temporal Feature Alignment and Mutual Information Maximization for
  Video-Based Human Pose Estimation

abstract:
  Multi-frame human pose estimation has long been a compelling and fundamental
problem in computer vision. This task is challenging due to fast motion and
pose occlusion that frequently occur in videos. State-of-the-art methods strive
to incorporate additional visual evidences from neighboring frames (supporting
frames) to facilitate the pose estimation of the current frame (key frame). One
aspect that has been obviated so far, is the fact that current methods directly
aggregate unaligned contexts across frames. The spatial-misalignment between
pose features of the current frame and neighboring frames might lead to
unsatisfactory results. More importantly, existing approaches build upon the
straightforward pose estimation loss, which unfortunately cannot constrain the
network to fully leverage useful information from neighboring frames. To tackle
these problems, we present a novel hierarchical alignment framework, which
leverages coarse-to-fine deformations to progressively update a neighboring
frame to align with the current frame at the feature level. We further propose
to explicitly supervise the knowledge extraction from neighboring frames,
guaranteeing that useful complementary cues are extracted. To achieve this
goal, we theoretically analyzed the mutual information between the frames and
arrived at a loss that maximizes the task-relevant mutual information. These
allow us to rank No.1 in the Multi-frame Person Pose Estimation Challenge on
benchmark dataset PoseTrack2017, and obtain state-of-the-art performance on
benchmarks Sub-JHMDB and Pose-Track2018. Our code is released at
https://github. com/Pose-Group/FAMI-Pose, hoping that it will be useful to the
community.


title:
SHOP: A Deep Learning Based Pipeline for near Real-Time Detection of
  Small Handheld Objects Present in Blurry Video

abstract:
  While prior works have investigated and developed computational models
capable of object detection, models still struggle to reliably interpret images
with motion blur and small objects. Moreover, none of these models are
specifically designed for handheld object detection. In this work, we present
SHOP (Small Handheld Object Pipeline), a pipeline that reliably and efficiently
interprets blurry images containing handheld objects. The specific models used
in each stage of the pipeline are flexible and can be changed based on
performance requirements. First, images are deblurred and then run through a
pose detection system where areas-of-interest are proposed around the hands of
any people present. Next, object detection is performed on the images by a
single-stage object detector. Finally, the proposed areas-of-interest are used
to filter out low confidence detections. Testing on a handheld subset of
Microsoft Common Objects in Context (MS COCO) demonstrates that this 3 stage
process results in a 70 percent decrease in false positives while only reducing
true positives by 17 percent in its strongest configuration. We also present a
subset of MS COCO consisting solely of handheld objects that can be used to
continue the development of handheld object detection methods.
https://github.com/spider-sense/SHOP


title: Edge Detection and Deep Learning Based SETI Signal Classification Method

abstract:
  Scientists at the Berkeley SETI Research Center are Searching for
Extraterrestrial Intelligence (SETI) by a new signal detection method that
converts radio signals into spectrograms through Fourier transforms and
classifies signals represented by two-dimensional time-frequency spectrums,
which successfully converts a signal classification problem into an image
classification task. In view of the negative impact of background noises on the
accuracy of spectrograms classification, a new method is introduced in this
paper. After Gaussian convolution smoothing the signals, edge detection
functions are applied to detect the edge of the signals and enhance the outline
of the signals, then the processed spectrograms are used to train the deep
neural network to compare the classification accuracy of various image
classification networks. The results show that the proposed method can
effectively improve the classification accuracy of SETI spectrums.