542 research outputs found
Stability and error analysis of one-leg methods for nonlinear delay differential equations
AbstractThis paper is concerned with the numerical solution of delay differential equations (DDEs). We focus on the stability behaviour and error analysis of one-leg methods with respect to nonlinear DDEs. The new concepts of GR-stability, GAR-stability and weak GAR-stability are introduced. It is proved that a strongly A-stable one-leg method with linear interpolation is GAR-stable, and that an A-stable one-leg method with linear interpolation is GR-stable, weakly GAR-stable and D-convergent of order s, if it is consistent of order s in the classical sense
Statistical Guarantees of Generative Adversarial Networks for Distribution Estimation
Generative Adversarial Networks (GANs) have achieved great success in
unsupervised learning. Despite the remarkable empirical performance, there are
limited theoretical understandings on the statistical properties of GANs. This
paper provides statistical guarantees of GANs for the estimation of data
distributions which have densities in a H\"{o}lder space. Our main result shows
that, if the generator and discriminator network architectures are properly
chosen (universally for all distributions with H\"{o}lder densities), GANs are
consistent estimators of the data distributions under strong discrepancy
metrics, such as the Wasserstein distance. To our best knowledge, this is the
first statistical theory of GANs for H\"{o}lder densities. In comparison with
existing works, our theory requires minimum assumptions on data distributions.
Our generator and discriminator networks utilize general weight matrices and
the non-invertible ReLU activation function, while many existing works only
apply to invertible weight matrices and invertible activation functions. In our
analysis, we decompose the error into a statistical error and an approximation
error by a new oracle inequality, which may be of independent interest
A Closer Look at Few-Shot 3D Point Cloud Classification
In recent years, research on few-shot learning (FSL) has been fast-growing in
the 2D image domain due to the less requirement for labeled training data and
greater generalization for novel classes. However, its application in 3D point
cloud data is relatively under-explored. Not only need to distinguish unseen
classes as in the 2D domain, 3D FSL is more challenging in terms of irregular
structures, subtle inter-class differences, and high intra-class variances
{when trained on a low number of data.} Moreover, different architectures and
learning algorithms make it difficult to study the effectiveness of existing 2D
FSL algorithms when migrating to the 3D domain. In this work, for the first
time, we perform systematic and extensive investigations of directly applying
recent 2D FSL works to 3D point cloud related backbone networks and thus
suggest a strong learning baseline for few-shot 3D point cloud classification.
Furthermore, we propose a new network, Point-cloud Correlation Interaction
(PCIA), with three novel plug-and-play components called Salient-Part Fusion
(SPF) module, Self-Channel Interaction Plus (SCI+) module, and Cross-Instance
Fusion Plus (CIF+) module to obtain more representative embeddings and improve
the feature distinction. These modules can be inserted into most FSL algorithms
with minor changes and significantly improve the performance. Experimental
results on three benchmark datasets, ModelNet40-FS, ShapeNet70-FS, and
ScanObjectNN-FS, demonstrate that our method achieves state-of-the-art
performance for the 3D FSL task. Code and datasets are available at
https://github.com/cgye96/A_Closer_Look_At_3DFSL.Comment: Accepted by IJCV 202
Regression analysis of longitudinal data with mixed synchronous and asynchronous longitudinal covariates
In linear models, omitting a covariate that is orthogonal to covariates in
the model does not result in biased coefficient estimation. This in general
does not hold for longitudinal data, where additional assumptions are needed to
get unbiased coefficient estimation in addition to the orthogonality between
omitted longitudinal covariates and longitudinal covariates in the model. We
propose methods to mitigate the omitted variable bias under weaker assumptions.
A two-step estimation procedure is proposed for inference about the
asynchronous longitudinal covariates, when such covariates are observed. For
mixed synchronous and asynchronous longitudinal covariates, we get parametric
rate of convergence for the coefficient estimation of the synchronous
longitudinal covariates by the two-step method. Extensive simulation studies
provide numerical support for the theoretical findings. We illustrate the
performance of our method on dataset from the Alzheimers Disease Neuroimaging
Initiative study
Combat molten aluminum corrosion of AISI H13 steel by lowtemperature liquid nitrocarburizing
Possibility of improving the resistance of AISI H13 steel to molten aluminum corrosion by liquid nitrocarburizing (LNC) was explored. The effects of the LNC parameters in terms of temperatures (703/723/743K) and soaking time (4/8/12h) on phase transformation, microstructure, and resistance to molten aluminum were fully studied. The surface phase compositions and the cross-sectional phase distribution of the LNC treated specimens were studied by implementable X-ray diffraction analysis. Microstructure, element distribution, microhardness, and the kinetics of the nitrocarburized case formation were fully researched. Immersion test of corrosion resistance to molten aluminum was carried out at 1023K for 30min. It is observed that an oxide layer can be produced on the top of the nitrocarburized case during LNC treatment, which cannot be regularly produced by other nitriding methods. The nitrocarburized case consists of a compound layer, a diffusion layer, and a transition layer. The growth of the nitrocarburized case is proportional to the squared treatment time and follows the Arrhenius law for the treatment temperature. The activation energy is estimated to be 195.4 kJĀ·molā1. While the nitrocarburized case provided limited resistance to molten aluminum, the oxide layer formed on the top of the nitrocarburized case conferred significantly improved molten aluminum corrosion resistance, especially a duplex oxide layer produced at 743
Visual Anomaly Detection in Event Sequence Data
Anomaly detection is a common analytical task that aims to identify rare
cases that differ from the typical cases that make up the majority of a
dataset. When applied to the analysis of event sequence data, the task of
anomaly detection can be complex because the sequential and temporal nature of
such data results in diverse definitions and flexible forms of anomalies. This,
in turn, increases the difficulty in interpreting detected anomalies. In this
paper, we propose an unsupervised anomaly detection algorithm based on
Variational AutoEncoders (VAE) to estimate underlying normal progressions for
each given sequence represented as occurrence probabilities of events along the
sequence progression. Events in violation of their occurrence probability are
identified as abnormal. We also introduce a visualization system, EventThread3,
to support interactive exploration and interpretations of anomalies within the
context of normal sequence progressions in the dataset through comprehensive
one-to-many sequence comparison. Finally, we quantitatively evaluate the
performance of our anomaly detection algorithm and demonstrate the
effectiveness of our system through a case study
LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding, Reasoning, and Planning
Recent advances in Large Multimodal Models (LMM) have made it possible for
various applications in human-machine interactions. However, developing LMMs
that can comprehend, reason, and plan in complex and diverse 3D environments
remains a challenging topic, especially considering the demand for
understanding permutation-invariant point cloud 3D representations of the 3D
scene. Existing works seek help from multi-view images, and project 2D features
to 3D space as 3D scene representations. This, however, leads to huge
computational overhead and performance degradation. In this paper, we present
LL3DA, a Large Language 3D Assistant that takes point cloud as direct input and
respond to both textual-instructions and visual-prompts. This help LMMs better
comprehend human interactions and further help to remove the ambiguities in
cluttered 3D scenes. Experiments show that LL3DA achieves remarkable results,
and surpasses various 3D vision-language models on both 3D Dense Captioning and
3D Question Answering.Comment: Project Page: https://ll3da.github.io
Vote2Cap-DETR++: Decoupling Localization and Describing for End-to-End 3D Dense Captioning
3D dense captioning requires a model to translate its understanding of an
input 3D scene into several captions associated with different object regions.
Existing methods adopt a sophisticated "detect-then-describe" pipeline, which
builds explicit relation modules upon a 3D detector with numerous hand-crafted
components. While these methods have achieved initial success, the cascade
pipeline tends to accumulate errors because of duplicated and inaccurate box
estimations and messy 3D scenes. In this paper, we first propose Vote2Cap-DETR,
a simple-yet-effective transformer framework that decouples the decoding
process of caption generation and object localization through parallel
decoding. Moreover, we argue that object localization and description
generation require different levels of scene understanding, which could be
challenging for a shared set of queries to capture. To this end, we propose an
advanced version, Vote2Cap-DETR++, which decouples the queries into
localization and caption queries to capture task-specific features.
Additionally, we introduce the iterative spatial refinement strategy to vote
queries for faster convergence and better localization performance. We also
insert additional spatial information to the caption head for more accurate
descriptions. Without bells and whistles, extensive experiments on two commonly
used datasets, ScanRefer and Nr3D, demonstrate Vote2Cap-DETR and
Vote2Cap-DETR++ surpass conventional "detect-then-describe" methods by a large
margin. Codes will be made available at
https://github.com/ch3cook-fdu/Vote2Cap-DETR
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