Leveraging Inlier Correspondences Proportion for Point Cloud Registration

In feature-learning based point cloud registration, the correct correspondence construction is vital for the subsequent transformation estimation. However, it is still a challenge to extract discriminative features from point cloud, especially when the input is partial and composed by indistinguishable surfaces (planes, smooth surfaces, etc.). As a result, the proportion of inlier correspondences that precisely match points between two unaligned point clouds is beyond satisfaction. Motivated by this, we devise several techniques to promote feature-learning based point cloud registration performance by leveraging inlier correspondences proportion: a pyramid hierarchy decoder to characterize point features in multiple scales, a consistent voting strategy to maintain consistent correspondences and a geometry guided encoding module to take geometric characteristics into consideration. Based on the above techniques, We build our Geometry-guided Consistent Network (GCNet), and challenge GCNet by indoor, outdoor and object-centric synthetic datasets. Comprehensive experiments demonstrate that GCNet outperforms the state-of-the-art methods and the techniques used in GCNet is model-agnostic, which could be easily migrated to other feature-based deep learning or traditional registration methods, and dramatically improve the performance. The code is available at https://github.com/zhulf0804/NgeNet

Towards Accurate One-Stage Object Detection with AP-Loss

One-stage object detectors are trained by optimizing classification-loss and localization-loss simultaneously, with the former suffering much from extreme foreground-background class imbalance issue due to the large number of anchors. This paper alleviates this issue by proposing a novel framework to replace the classification task in one-stage detectors with a ranking task, and adopting the Average-Precision loss (AP-loss) for the ranking problem. Due to its non-differentiability and non-convexity, the AP-loss cannot be optimized directly. For this purpose, we develop a novel optimization algorithm, which seamlessly combines the error-driven update scheme in perceptron learning and backpropagation algorithm in deep networks. We verify good convergence property of the proposed algorithm theoretically and empirically. Experimental results demonstrate notable performance improvement in state-of-the-art one-stage detectors based on AP-loss over different kinds of classification-losses on various benchmarks, without changing the network architectures. Code is available at https://github.com/cccorn/AP-loss.Comment: 13 pages, 7 figures, 4 tables, main paper + supplementary material, accepted to CVPR 201

A novel gas ionization sensor using Pd nanoparticle-capped ZnO

A novel gas ionization sensor using Pd nanoparticle-capped ZnO (Pd/ZnO) nanorods as the anode is proposed. The Pd/ZnO nanorod-based sensors, compared with the bare ZnO nanorod, show lower breakdown voltage for the detected gases with good sensitivity and selectivity. Moreover, the sensors exhibit stable performance after more than 200 tests for both inert and active gases. The simple, low-cost, Pd/ZnO nanorod-based field-ionization gas sensors presented in this study have potential applications in the field of gas sensor devices

New Developments in Geotechnical Earthquake Engineering

Based on the review on the advances of several important problems in geotechnical seismic engineering, the authors propose the initial analysis theory of time-frequency-amplitude (known as TFA for short), in an effort to realize the organic combination of time and frequency information and develop a groundbreaking concept to the traditional idea in the geotechnical seismic engineering area

The Development of LLMs for Embodied Navigation

In recent years, the rapid advancement of Large Language Models (LLMs) such as the Generative Pre-trained Transformer (GPT) has attracted increasing attention due to their potential in a variety of practical applications. The application of LLMs with Embodied Intelligence has emerged as a significant area of focus. Among the myriad applications of LLMs, navigation tasks are particularly noteworthy because they demand a deep understanding of the environment and quick, accurate decision-making. LLMs can augment embodied intelligence systems with sophisticated environmental perception and decision-making support, leveraging their robust language and image-processing capabilities. This article offers an exhaustive summary of the symbiosis between LLMs and embodied intelligence with a focus on navigation. It reviews state-of-the-art models, research methodologies, and assesses the advantages and disadvantages of existing embodied navigation models and datasets. Finally, the article elucidates the role of LLMs in embodied intelligence, based on current research, and forecasts future directions in the field. A comprehensive list of studies in this survey is available at https://github.com/Rongtao-Xu/Awesome-LLM-E

Mammalian DNA2 helicase/nuclease cleaves G-quadruplex DNA and is required for telomere integrity

Efficient and faithful replication of telomeric DNA is critical for maintaining genome integrity. The G-quadruplex (G4) structure arising in the repetitive TTAGGG sequence is thought to stall replication forks, impairing efficient telomere replication and leading to telomere instabilities. However, pathways modulating telomeric G4 are poorly understood, and it is unclear whether defects in these pathways contribute to genome instabilities in vivo. Here, we report that mammalian DNA2 helicase/nuclease recognizes and cleaves telomeric G4 in vitro. Consistent with DNA2’s role in removing G4, DNA2 deficiency in mouse cells leads to telomere replication defects, elevating the levels of fragile telomeres (FTs) and sister telomere associations (STAs). Such telomere defects are enhanced by stabilizers of G4. Moreover, DNA2 deficiency induces telomere DNA damage and chromosome segregation errors, resulting in tetraploidy and aneuploidy. Consequently, DNA2-deficient mice develop aneuploidy-associated cancers containing dysfunctional telomeres. Collectively, our genetic, cytological, and biochemical results suggest that mammalian DNA2 reduces replication stress at telomeres, thereby preserving genome stability and suppressing cancer development, and that this may involve, at least in part, nucleolytic processing of telomeric G4