LIO-PPF: Fast LiDAR-Inertial Odometry via Incremental Plane Pre-Fitting and Skeleton Tracking

As a crucial infrastructure of intelligent mobile robots, LiDAR-Inertial odometry (LIO) provides the basic capability of state estimation by tracking LiDAR scans. The high-accuracy tracking generally involves the kNN search, which is used with minimizing the point-to-plane distance. The cost for this, however, is maintaining a large local map and performing kNN plane fit for each point. In this work, we reduce both time and space complexity of LIO by saving these unnecessary costs. Technically, we design a plane pre-fitting (PPF) pipeline to track the basic skeleton of the 3D scene. In PPF, planes are not fitted individually for each scan, let alone for each point, but are updated incrementally as the scene 'flows'. Unlike kNN, the PPF is more robust to noisy and non-strict planes with our iterative Principal Component Analyse (iPCA) refinement. Moreover, a simple yet effective sandwich layer is introduced to eliminate false point-to-plane matches. Our method was extensively tested on a total number of 22 sequences across 5 open datasets, and evaluated in 3 existing state-of-the-art LIO systems. By contrast, LIO-PPF can consume only 36% of the original local map size to achieve up to 4x faster residual computing and 1.92x overall FPS, while maintaining the same level of accuracy. We fully open source our implementation at https://github.com/xingyuuchen/LIO-PPF.Comment: IROS 202

Understanding Edge-of-Stability Training Dynamics with a Minimalist Example

Recently, researchers observed that gradient descent for deep neural networks operates in an ``edge-of-stability'' (EoS) regime: the sharpness (maximum eigenvalue of the Hessian) is often larger than stability threshold 2/$\eta$ (where $\eta$ is the step size). Despite this, the loss oscillates and converges in the long run, and the sharpness at the end is just slightly below $2/\eta$. While many other well-understood nonconvex objectives such as matrix factorization or two-layer networks can also converge despite large sharpness, there is often a larger gap between sharpness of the endpoint and $2/\eta$. In this paper, we study EoS phenomenon by constructing a simple function that has the same behavior. We give rigorous analysis for its training dynamics in a large local region and explain why the final converging point has sharpness close to $2/\eta$. Globally we observe that the training dynamics for our example has an interesting bifurcating behavior, which was also observed in the training of neural nets.Comment: 53 pages, 19 figure

Dissecting Hessian: Understanding Common Structure of Hessian in Neural Networks

Hessian captures important properties of the deep neural network loss landscape. Previous works have observed low rank structure in the Hessians of neural networks. We make several new observations about the top eigenspace of layer-wise Hessian: top eigenspaces for different models have surprisingly high overlap, and top eigenvectors form low rank matrices when they are reshaped into the same shape as the corresponding weight matrix. Towards formally explaining such structures of the Hessian, we show that the new eigenspace structure can be explained by approximating the Hessian using Kronecker factorization; we also prove the low rank structure for random data at random initialization for over-parametrized two-layer neural nets. Our new understanding can explain why some of these structures become weaker when the network is trained with batch normalization. The Kronecker factorization also leads to better explicit generalization bounds.Comment: 60 pages, 30 figures. Main text: 10 pages, 7 figures. First two authors have equal contribution and are in alphabetical orde

Thyroid organoids: Advances and applications

Organoids are derived from stem cells under three-dimensional culture conditions through self-assembly, and they can recapitulate the structural and functional characteristics of organs in vivo during culture. Organoids can be generated from both normal and malignant tissues. Those derived from normal tissues are widely used in the field of regenerative medicine. Meanwhile, tumour-derived organoids retain the phenotypic heterogeneity and atypia of the primary tumour, thereby providing a reliable in vitro model for the study of tumour pathogenesis and treatment. The thyroid gland is one of the most important endocrine organs regulating the body’s energy metabolism and growth; however, it is also associated with a high incidence of malignancy. Organoid is an effective tool for thyroid research. Thyroid tumour-derived organoids can inherit the histopathological properties of primary tumours, and thyroid tissue-derived organoids can form follicular structures and secrete thyroid hormones. The above characteristics of organoids provide a reliable way to study the mechanism of thyroid genesis and tumour development in vitro. In this review, we focus on current knowledge and strategies for the establishment of thyroid organoids in thyroid regeneration and tumour research aiming to increase our understanding of the pathogenesis of thyroid tumours and the regenerative treatment of patients with hypothyroidism

Systematic benchmarking of nanopore Q20+ kit in SARS-CoV-2 whole genome sequencing

Whole genome sequencing provides rapid insight into key information about the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), such as virus typing and key mutation site, and this information is important for precise prevention, control and tracing of coronavirus disease 2019 (COVID-19) outbreak in conjunction with the epidemiological information of the case. Nanopore sequencing is widely used around the world for its short sample-to-result time, simple experimental operation and long sequencing reads. However, because nanopore sequencing is a relatively new sequencing technology, many researchers still have doubts about its accuracy. The combination of the newly launched nanopore sequencing Q20+ kit (LSK112) and flow cell R10.4 is a qualitative improvement over the accuracy of the previous kits. In this study, we firstly used LSK112 kit with flow cell R10.4 to sequence the SARS-CoV-2 whole genome, and summarized the sequencing results of the combination of LSK112 kit and flow cell R10.4 for the 1200bp amplicons of SARS-CoV-2. We found that the proportion of sequences with an accuracy of more than 99% reached 30.1%, and the average sequence accuracy reached 98.34%, while the results of the original combination of LSK109 kit and flow cell R9.4.1 were 0.61% and 96.52%, respectively. The mutation site analysis showed that it was completely consistent with the final consensus sequence of next generation sequencing (NGS). The results showed that the combination of LSK112 kit and flow cell R10.4 allowed rapid whole-genome sequencing of SARS-CoV-2 without the need for verification of NGS

Bacillus amyloliquefaciens YN201732 Produces Lipopeptides With Promising Biocontrol Activity Against Fungal Pathogen Erysiphe cichoracearum

Bacillus amyloliquefaciens YN201732 is an endophytic bacteria with high biocontrol efficiency and broad-spectrum antimicrobial activities. In order to clarify the main active ingredients and their antifungal mechanisms against powdery mildew of tobacco, this study is focused on lipopeptide obtained through acid precipitation and organic solvent extraction. HPLC and LCMS-IT-TOF were used to separate and identify antimicrobial lipopeptides. Findings revealed that bacillomycin D plays an important role against surrogate fungal pathogen Fusarium solani. Synthetic pathways of sfp, bacillomycin D, and fengycin were separately disrupted. The sfp gene knockout mutant B. amyloliquefaciens YN201732M1 only showed minor antagonistic activity against F. solani. While Erysiphe cichoracearum spore germination was inhibited and pot experiments displayed a significant decrease in tobacco powdery mildew. The spore inhibition rate of YN201732M1 was only 30.29%, and the pot experiment control effect was less than 37.39%, which was significantly lower than that of the wild type. The inhibitory effect of mutant YN201732M2 (deficient in the production of bacillomycin D) and mutant YN201732M3 (deficient in the production of fengycin) on the spore germination of E. cichoracearum were 50.22% and 53.06%, respectively, suggesting that both fengycin and bacillomycin D had potential effects on spore germination of powdery mildew. Interestingly, in a greenhouse assay, both B. amyloliquefaciens YN201732M2 and YN201732M3 mutants displayed less of a control effect on tobacco powdery mildew than wild type. The results from in vitro, spore germination, and greenhouse-pot studies demonstrated that antimicrobial lipopeptides especially bacillomycin D and fengycin may contribute to the prevention and control of tobacco powdery mildew. In addition, gene mutation related to lipopeptide synthesis can also affect the biofilm formation of strains