Robot assisted endoluminal intervention is an emerging technique for both benign and malignant luminal lesions. With vision-based navigation, when combined with pre-operative imaging data as priors, it is possible to recover position and pose of the endoscope without the need of additional sensors. In practice, however, aligning pre-operative and intra-operative domains is complicated by significant texture differences. Although methods such as style transfer can be used to address this issue, they require large datasets from both source and target domains with prolonged training times. This paper proposes an efficient domain transfer method based on stylized Gaussian splatting, only requiring a few of real images (10 images) with very fast training time. Specifically, the transfer process includes two phases. In the first phase, the 3D models reconstructed from CT scans are represented as differential Gaussian point clouds. In the second phase, only color appearance related parameters are optimized to transfer the style and preserve the visual content. A novel structure consistency loss is applied to latent features and depth levels to enhance the stability of the transferred images. Detailed validation was performed to demonstrate the performance advantages of the proposed method compared to that of the current state-of-the-art, highlighting the potential for intra-operative surgical navigation.

Robot assisted endoluminal intervention is an emerging tool for treating luminal lesions. Vision-based endoluminal navigation, particularly through video-CT registration, is a tangible way of obtaining absolute camera position information. By using pre-operative CT data, accurate endoscope localization can be achieved, without the need of additional tracking hardware intraoperatively. However, aligning preoperative CT with intraoperative domain remains a challenge. Although approaches such as style transfer have been explored, patient-specific textures and intra-operative artifacts can significantly complicate the task. To overcome these challenges, we propose R2Nav, a robust, real-time test time adaptation method for endoluminal navigation. R2Nav constructs a confidence buffer during the testing phase, refining the model only for frames with high uncertainty. We introduce a registration-augmented model refinement strategy, which enhances both accuracy and efficiency of the system by selecting relevant training samples from the virtual gallery. Additionally, we propose a novel warm-up strategy for the registration encoder during the initial testing phase, enabling the extraction of more robust features when the model is suboptimal. Extensive validation demonstrates that R2Nav outperforms the current state-of-the-art methods, offering significant advantages for real-time, intra-operative endoluminal navigation. Code is at: https://github.com/EndoluminalSurgicalVision-IMR/R2Nav.

Entity alignment (EA) aims to find equivalent entities in different knowledge graphs (KGs). State-of-the-art EA approaches generally use Graph Neural Networks (GNNs) to encode entities. However, most of them train the models and evaluate the results in a full-batch fashion, which prohibits EA from being scalable on large-scale datasets. To enhance the usability of GNN-based EA models in real-world applications, we present SEA, a scalable entity alignment system that enables to (i) train large-scale GNNs for EA, (ii) speed up the normalization and the evaluation process, and (iii) report clear results for users to estimate different models and parameter settings. SEA can be run on a computer with merely one graphic card. Moreover, SEA encompasses six state-of-the-art EA models and provides access for users to quickly establish and evaluate their own models. Thus, SEA allows users to perform EA without being involved in tedious implementations, such as negative sampling and GPU-accelerated evaluation. With SEA, users can gain a clear view of the model performance. In the demonstration, we show that SEA is user-friendly and is of high scalability even on computers with limited computational resources.

Entity alignment (EA) is a fundamental data integration task that identifies equivalent entities between different knowledge graphs (KGs). Temporal Knowledge graphs (TKGs) extend traditional knowledge graphs by introducing timestamps, which have received increasing attention. State-of-the-art time-aware EA studies have suggested that the temporal information of TKGs facilitates the performance of EA. However, existing studies have not thoroughly exploited the advantages of temporal information in TKGs. Also, they perform EA by pre-aligning entity pairs, which can be labor-intensive and thus inefficient. In this paper, we present DualMatch that effectively fuses the relational and temporal information for EA. DualMatch transfers EA on TKGs into a weighted graph matching problem. More specifically, DualMatch is equipped with an unsupervised method, which achieves EA without necessitating the seed alignment. DualMatch has two steps: (i) encoding temporal and relational information into embeddings separately using a novel label-free encoder, Dual-Encoder; and (ii) fusing both information and transforming it into alignment using a novel graph-matching-based decoder, GM-Decoder. DualMatch is able to perform EA on TKGs with or without supervision, due to its capability of effectively capturing temporal information. Extensive experiments on three real-world TKG datasets offer the insight that DualMatch significantly outperforms the state-of-the-art methods.

Hosting database services on cloud systems has become a common practice. This has led to the increasing volume of database workloads, which provides the opportunity for pattern analysis. Discovering workload patterns from a business logic perspective is conducive to better understanding the trends and characteristics of the database system. However, existing workload pattern discovery systems are not suitable for large-scale cloud databases which are commonly employed by the industry. This is because the workload patterns of large-scale cloud databases are generally far more complicated than those of ordinary databases. In this paper, we propose Alibaba Workload Miner (AWM), a real-time system for discovering workload patterns in complicated large-scale workloads. AW M encodes and discovers the SQL query patterns logged from user requests and optimizes the querying processing based on the discovered patterns. First, Data Collection & Preprocessing Module collects streaming query logs and encodes them into high-dimensional feature embeddings with rich semantic contexts and execution features. Next, Online Workload Mining Module separates encoded query by business groups and discovers the workload patterns for each group. Meanwhile, Offline Training Module collects labels and trains the classification model using the labels. Finally, Pattern-based Optimizing Module optimizes query processing in cloud databases by exploiting discovered patterns. Extensive experimental results on one synthetic dataset and two real-life datasets (extracted from Alibaba Cloud databases) show that AW M enhances the accuracy of pattern discovery by 66% and reduce the latency of online inference by 22%, compared with the state-of-the-arts.

Entity alignment (EA) aims at finding equivalent entities in different knowledge graphs (KGs). Embedding-based approaches have dominated the EA task in recent years. Those methods face problems that come from the geometric properties of embedding vectors, including hubness and isolation. To solve these geometric problems, many normalization approaches have been adopted for EA. However, the increasing scale of KGs renders it hard for EA models to adopt the normalization processes, thus limiting their usage in real-world applications. To tackle this challenge, we present ClusterEA, a general framework that is capable of scaling up EA models and enhancing their results by leveraging normalization methods on mini-batches with a high entity equivalent rate. ClusterEA contains three components to align entities between large-scale KGs, including stochastic training, ClusterSampler, and SparseFusion. It first trains a large-scale Siamese GNN for EA in a stochastic fashion to produce entity embeddings. Based on the embeddings, a novel ClusterSampler strategy is proposed for sampling highly overlapped mini-batches. Finally, ClusterEA incorporates SparseFusion, which normalizes local and global similarity and then fuses all similarity matrices to obtain the final similarity matrix. Extensive experiments with real-life datasets on EA benchmarks offer insight into the proposed framework, and suggest that it is capable of outperforming the state-of-the-art scalable EA framework by up to 8 times in terms of [email protected]