Generalizing across Temporal Domains with Koopman Operators

In the field of domain generalization, the task of constructing a predictive model capable of generalizing to a target domain without access to target data remains challenging. This problem becomes further complicated when considering evolving dynamics between domains. While various approaches have been proposed to address this issue, a comprehensive understanding of the underlying generalization theory is still lacking. In this study, we contribute novel theoretic results that aligning conditional distribution leads to the reduction of generalization bounds. Our analysis serves as a key motivation for solving the Temporal Domain Generalization (TDG) problem through the application of Koopman Neural Operators, resulting in Temporal Koopman Networks (TKNets). By employing Koopman Operators, we effectively address the time-evolving distributions encountered in TDG using the principles of Koopman theory, where measurement functions are sought to establish linear transition relations between evolving domains. Through empirical evaluations conducted on synthetic and real-world datasets, we validate the effectiveness of our proposed approach.Comment: 15 pages, 7 figures, Accepted by AAAI 2024. arXiv admin note: text overlap with arXiv:2206.0004

Accurate automated Cobb angles estimation using multi-view extrapolation net.

Accurate automated quantitative Cobb angle estimation that quantitatively evaluates scoliosis plays an important role in scoliosis diagnosis and treatment. It solves the problem of the traditional manual method, which is the current clinical standard for scoliosis assessment, but time-consuming and unreliable. However, it is very challenging to achieve highly accurate automated Cobb angle estimation because it is difficult to utilize the information of Anterior-posterior (AP) and Lateral (LAT) view X-rays efficiently. We therefore propose a Multi-View Extrapolation Net (MVE-Net) that provides accurate automated scoliosis estimation in multi-view (both AP and LAT) X-rays. The MVE-Net consists of three parts: Joint-view net learning AP and LAT angles jointly based on landmarks learned from joint representation; Independent-view net learning AP and LAT angles independently based on landmarks learned from unique independent feature of AP or LAT angles; Inter-error correction net learning a combination function adaptively to offset the first two nets’ errors for accurate angle estimation. Experimental results on 526 X-rays show 7.81 and 6.26 Circular Mean Absolute Error in AP and LAT angle estimation, which shows the MVE-Net provides an accurate Cobb angle estimation in multi-view X-rays. Our method therefore provides effective framework for automated, accurate, and reliable scoliosis estimation

Accurate automated Cobb angles estimation using multi-view extrapolation net

Abstract(#br)Accurate automated quantitative Cobb angle estimation that quantitatively evaluates scoliosis plays an important role in scoliosis diagnosis and treatment. It solves the problem of the traditional manual method, which is the current clinical standard for scoliosis assessment, but time-consuming and unreliable. However, it is very challenging to achieve highly accurate automated Cobb angle estimation because it is difficult to utilize the information of Anterior-posterior (AP) and Lateral (LAT) view X-rays efficiently. We therefore propose a Multi-View Extrapolation Net (MVE-Net) that provides accurate automated scoliosis estimation in multi-view (both AP and LAT) X-rays. The MVE-Net consists of three parts: Joint-view net learning AP and LAT angles jointly based on landmarks learned from joint representation; Independent-view net learning AP and LAT angles independently based on landmarks learned from unique independent feature of AP or LAT angles; Inter-error correction net learning a combination function adaptively to offset the first two nets’ errors for accurate angle estimation. Experimental results on 526 X-rays show 7.81 and 6.26 Circular Mean Absolute Error in AP and LAT angle estimation, which shows the MVE-Net provides an accurate Cobb angle estimation in multi-view X-rays. Our method therefore provides effective framework for automated, accurate, and reliable scoliosis estimation

Developing a Static Kinematic Model for Continuum Robots Using Dual Quaternions for Efficient Attitude and Trajectory Planning

Kinematic modeling is essential for planning and controlling continuum robot motion. The traditional Denavit Hartenberg (DH) model involves complex matrix multiplication operations, resulting in computationally intensive inverse solutions and trajectory planning. Solving position and orientation changes in continuum robots using the double quaternion rule can reduce computational complexity. However, existing dual quaternion methods are direct equational transformations of DH rules and do not give a complete modeling process. They usually require more interpretability when applying continuum robot kinematic modeling. This paper uses the dual quaternion method to establish a kinematic model of a continuum robot. It uses a two-section continuum robot model to compare the advantages of dual quaternion and traditional modeling methods. In addition, this paper proposes a five-polynomial interpolation algorithm based on the dual quaternion method for trajectory planning of continuum robots. This method accurately models spatial bending and torsional motions of singularity-free continuum robots

Research on the Spatial Network Structure of Tourist Flows in Hangzhou Based on BERT-BiLSTM-CRF

Tourist flows, crucial information within online travelogues, reveal the interactive relationships between different tourist destinations and serve as the nerve center and link of the tourism system. This study takes Hangzhou, China, as a case to investigate the spatial network structure of its tourist flows. Firstly, a BERT-BiLSTM-CRF model and pan-attraction database are built to extract tourist attractions from online travelogues and create the tourist flow matrix. Then, this study uses social network analysis (SNA) to examine the structure of the tourist flow network from a county-level perspective. Additionally, GIS spatial analysis methods are applied to analyze the evolution of the tourist gravity center and standard deviation ellipse (SDE) of the network. The results reveal that the identification performances of the tourist flow extraction model this study proposed are significantly better than those of previous mainstream models, with an F1 value of 0.8752. Furthermore, the tourist flow network in Hangzhou displays a relatively sparse and unbalanced distribution, forming a “Core–Semi-Periphery–Periphery” structure. Lastly, from 2020 to 2022, the network’s gravity center experienced a shift towards the southwest, paralleled by an initial expansion and subsequent contraction of the SDE in the same southwest direction. These findings provide valuable insights into the spatial network structure of tourism in Hangzhou and can serve as a reference for policymakers to promote the “all-for-one” tourism

SecGraph: Towards SGX-based Efficient and Confidentiality-Preserving Graph Search

Graphs have more expressive power and are widely researched in various search demand scenarios, compared with traditional relational and XML models. Today, many graph search services have been deployed on a third-party server, which can alleviate users from the burdens of maintaining large-scale graphs and huge computation costs. Nevertheless, outsourcing graph search services to the third-party server may invade users' privacy. PeGraph was recently proposed to achieve the encrypted search over the social graph. The main idea of PeGraph is to maintain two data structures XSet and TSet motivated by the OXT technology to support encrypted conductive search. However, PeGraph still has some limitations. First, PeGraph suffers from high communication and computation costs in search operations. Second, PeGraph cannot support encrypted search over dynamic graphs. In this paper, we propose an SGX-based efficient and confidentiality-preserving graph search scheme SecGraph that can support insertion and deletion operations. We first design a new proxy-token generation method to reduce the communication cost. Then, we design an LDCF-encoded XSet based on the Logarithmic Dynamic Cuckoo Filter to reduce the computation cost. Finally, we design a new dynamic version of TSet named Twin-TSet to enable encrypted search over dynamic graphs. We have demonstrated the confidentiality preservation property of SecGraph through rigorous security analysis. Experiment results show that SecGraph yields up to 208x improvement in search time compared with PeGraph and the communication cost in PeGraph is up to 540x larger than that in SecGraph.Comment: This paper has been accepted by DASFAA 202

Foresee What You Will Learn: Data Augmentation for Domain Generalization in Non-stationary Environment

Existing domain generalization aims to learn a generalizable model to perform well even on unseen domains. For many real-world machine learning applications, the data distribution often shifts gradually along domain indices. For example, a self-driving car with a vision system drives from dawn to dusk, with the sky gradually darkening. Therefore, the system must be able to adapt to changes in ambient illuminations and continue to drive safely on the road. In this paper, we formulate such problems as Evolving Domain Generalization, where a model aims to generalize well on a target domain by discovering and leveraging the evolving pattern of the environment. We then propose Directional Domain Augmentation (DDA), which simulates the unseen target features by mapping source data as augmentations through a domain transformer. Specifically, we formulate DDA as a bi-level optimization problem and solve it through a novel meta-learning approach in the representation space. We evaluate the proposed method on both synthetic datasets and real-world datasets, and empirical results show that our approach can outperform other existing methods