357 research outputs found
A Perspective on Human Movement Variability With Applications in Infancy Motor Development
Movement variability is considered essential to typical motor development. However, multiple theoretical perspectives and measurement tools have limited interpretation of the importance of movement variability in biological systems. The complementary use of linear and nonlinear measures have recently allowed for the evaluation of not only the magnitude of variability but also the temporal structure of variability. As a result, the theoretical model of optimal movement variability was introduced. The model suggests that the development of healthy and highly adaptable systems relies on the achievement of an optimal state of variability. Alternatively, abnormal development may be characterized by a narrow range of behaviors, some of which may be rigid, inflexible, and highly predictable or, on the contrary, random, unfocused, and unpredictable. In the present review, this theoretical model is described as it relates to motor development in infancy and specifically the development of sitting posture
Explaining Pulsar Timing Array Observations with Primordial Gravitational Waves in Parity-Violating Gravity
The pulsar timing array (PTA) collaborations have recently suggested the
presence of a gravitational wave background at nano-Hertz frequencies. In this
paper, we explore potential inflationary interpretation of this signal within
the context of a simple and health parity-violating gravity model termed the
Nieh-Yan modified Teleparallel Gravity. Through this model, two inflationary
scenarios are evaluated, both yielding significant polarized primordial
gravitational waves (PGWs) that align well with the results from PTA
observations. Furthermore, the resulting PGWs can display strong circular
polarization and significant anisotropies in the PTA frequency band, which are
distinct features to be verified by observations of both PTA and the cosmic
microwave background.The detection of such a distinctive background of PGWs is
expected to provide strong evidence supporting our scenarios and insights into
inflationary dynamics and gravity theory.Comment: 9 pages, 8 figure
Hyperbolic Face Anti-Spoofing
Learning generalized face anti-spoofing (FAS) models against presentation
attacks is essential for the security of face recognition systems. Previous FAS
methods usually encourage models to extract discriminative features, of which
the distances within the same class (bonafide or attack) are pushed close while
those between bonafide and attack are pulled away. However, these methods are
designed based on Euclidean distance, which lacks generalization ability for
unseen attack detection due to poor hierarchy embedding ability. According to
the evidence that different spoofing attacks are intrinsically hierarchical, we
propose to learn richer hierarchical and discriminative spoofing cues in
hyperbolic space. Specifically, for unimodal FAS learning, the feature
embeddings are projected into the Poincar\'e ball, and then the hyperbolic
binary logistic regression layer is cascaded for classification. To further
improve generalization, we conduct hyperbolic contrastive learning for the
bonafide only while relaxing the constraints on diverse spoofing attacks. To
alleviate the vanishing gradient problem in hyperbolic space, a new feature
clipping method is proposed to enhance the training stability of hyperbolic
models. Besides, we further design a multimodal FAS framework with Euclidean
multimodal feature decomposition and hyperbolic multimodal feature fusion &
classification. Extensive experiments on three benchmark datasets (i.e., WMCA,
PADISI-Face, and SiW-M) with diverse attack types demonstrate that the proposed
method can bring significant improvement compared to the Euclidean baselines on
unseen attack detection. In addition, the proposed framework is also
generalized well on four benchmark datasets (i.e., MSU-MFSD, IDIAP
REPLAY-ATTACK, CASIA-FASD, and OULU-NPU) with a limited number of attack types
Time-varying effect in the competing risks based on restricted mean time lost
Patients with breast cancer tend to die from other diseases, so for studies
that focus on breast cancer, a competing risks model is more appropriate.
Considering subdistribution hazard ratio, which is used often, limited to model
assumptions and clinical interpretation, we aimed to quantify the effects of
prognostic factors by an absolute indicator, the difference in restricted mean
time lost (RMTL), which is more intuitive. Additionally, prognostic factors may
have dynamic effects (time-varying effects) in long-term follow-up. However,
existing competing risks regression models only provide a static view of
covariate effects, leading to a distorted assessment of the prognostic factor.
To address this issue, we proposed a dynamic effect RMTL regression that can
explore the between-group cumulative difference in mean life lost over a period
of time and obtain the real-time effect by the speed of accumulation, as well
as personalized predictions on a time scale. Through Monte Carlo simulation, we
validated the dynamic effects estimated by the proposed regression having low
bias and a coverage rate of around 95%. Applying this model to an elderly
early-stage breast cancer cohort, we found that most factors had different
patterns of dynamic effects, revealing meaningful physiological mechanisms
underlying diseases. Moreover, from the perspective of prediction, the mean
C-index in external validation reached 0.78. Dynamic effect RMTL regression can
analyze both dynamic cumulative effects and real-time effects of covariates,
providing a more comprehensive prognosis and better prediction when competing
risks exist
T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation
Graph Neural Networks (GNNs) have been a prevailing technique for tackling
various analysis tasks on graph data. A key premise for the remarkable
performance of GNNs relies on complete and trustworthy initial graph
descriptions (i.e., node features and graph structure), which is often not
satisfied since real-world graphs are often incomplete due to various
unavoidable factors. In particular, GNNs face greater challenges when both node
features and graph structure are incomplete at the same time. The existing
methods either focus on feature completion or structure completion. They
usually rely on the matching relationship between features and structure, or
employ joint learning of node representation and feature (or structure)
completion in the hope of achieving mutual benefit. However, recent studies
confirm that the mutual interference between features and structure leads to
the degradation of GNN performance. When both features and structure are
incomplete, the mismatch between features and structure caused by the missing
randomness exacerbates the interference between the two, which may trigger
incorrect completions that negatively affect node representation. To this end,
in this paper we propose a general GNN framework based on teacher-student
distillation to improve the performance of GNNs on incomplete graphs, namely
T2-GNN. To avoid the interference between features and structure, we separately
design feature-level and structure-level teacher models to provide targeted
guidance for student model (base GNNs, such as GCN) through distillation. Then
we design two personalized methods to obtain well-trained feature and structure
teachers. To ensure that the knowledge of the teacher model is comprehensively
and effectively distilled to the student model, we further propose a dual
distillation mode to enable the student to acquire as much expert knowledge as
possible.Comment: Accepted by AAAI2
OmniCity: Omnipotent City Understanding with Multi-level and Multi-view Images
This paper presents OmniCity, a new dataset for omnipotent city understanding
from multi-level and multi-view images. More precisely, the OmniCity contains
multi-view satellite images as well as street-level panorama and mono-view
images, constituting over 100K pixel-wise annotated images that are
well-aligned and collected from 25K geo-locations in New York City. To
alleviate the substantial pixel-wise annotation efforts, we propose an
efficient street-view image annotation pipeline that leverages the existing
label maps of satellite view and the transformation relations between different
views (satellite, panorama, and mono-view). With the new OmniCity dataset, we
provide benchmarks for a variety of tasks including building footprint
extraction, height estimation, and building plane/instance/fine-grained
segmentation. Compared with the existing multi-level and multi-view benchmarks,
OmniCity contains a larger number of images with richer annotation types and
more views, provides more benchmark results of state-of-the-art models, and
introduces a novel task for fine-grained building instance segmentation on
street-level panorama images. Moreover, OmniCity provides new problem settings
for existing tasks, such as cross-view image matching, synthesis, segmentation,
detection, etc., and facilitates the developing of new methods for large-scale
city understanding, reconstruction, and simulation. The OmniCity dataset as
well as the benchmarks will be available at
https://city-super.github.io/omnicity
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