HiSC4D: Human-Centered Interaction and 4D Scene Capture in Large-Scale Space Using Wearable IMUs and LiDAR.

We introduce HiSC4D, a novel Human-centered interaction and 4D Scene Capture method, aimed at accurately and efficiently creating a dynamic digital world, containing large-scale indoor-outdoor scenes, diverse human motions, rich human-human interactions, and human-environment interactions. By utilizing body-mounted IMUs and a head-mounted LiDAR, HiSC4D can capture egocentric human motions in unconstrained space without the need for external devices and pre-built maps. This affords great flexibility and accessibility for human-centered interaction and 4D scene capturing in various environments.

Vision-Centric BEV Perception: A Survey.

In recent years, vision-centric Bird's Eye View (BEV) perception has garnered significant interest from both industry and academia due to its inherent advantages, such as providing an intuitive representation of the world and being conducive to data fusion. The rapid advancements in deep learning have led to the proposal of numerous methods for addressing vision-centric BEV perception challenges. However, there has been no recent survey encompassing this novel and burgeoning research field.

Monocular BEV Perception of Road Scenes via Front-to-Top View Projection.

HD map reconstruction is crucial for autonomous driving. LiDAR-based methods are limited due to expensive sensors and time-consuming computation. Camera-based methods usually need to perform road segmentation and view transformation separately, which often causes distortion and missing content.

LiDAR-aid Inertial Poser: Large-scale Human Motion Capture by Sparse Inertial and LiDAR Sensors.

We propose a multi-sensor fusion method for capturing challenging 3D human motions with accurate consecutive local poses and global trajectories in large-scale scenarios, only using single LiDAR and 4 IMUs, which are set up conveniently and worn lightly. Specifically, to fully utilize the global geometry information captured by LiDAR and local dynamic motions captured by IMUs, we design a two-stage pose estimator in a coarse-to-fine manner, where point clouds provide the coarse body shape and IMU measurements optimize the local actions. Furthermore, considering the translation deviation caused by the view-dependent partial point cloud, we propose a pose-guided translation corrector.

Decision Fusion Networks for Image Classification.

Convolutional neural networks, in which each layer receives features from the previous layer(s) and then aggregates/abstracts higher level features from them, are widely adopted for image classification. To avoid information loss during feature aggregation/abstraction and fully utilize lower layer features, we propose a novel decision fusion module (DFM) for making an intermediate decision based on the features in the current layer and then fuse its results with the original features before passing them to the next layers. This decision is devised to determine an auxiliary category corresponding to the category at a higher hierarchical level, which can, thus, serve as category-coherent guidance for later layers.

[Nonspecific adsorption evaluation and general minimization strategy in peptide analysis based on ultra-performance liquid chromatography-mass spectrometry].

Proteomics technology is being increasingly used in the development of novel therapeutic peptides and protein drugs, and also in the intensive search for clinical biomacromolecule diagnostic biomarkers. Ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) is a rapid method to analyze peptides and proteins in low abundance. However, the nonspecific adsorption properties of peptides may lead to the loss or interference of the analytes throughout the analytical process.

Structure-Aware Long Short-Term Memory Network for 3D Cephalometric Landmark Detection.

Detecting 3D landmarks on cone-beam computed tomography (CBCT) is crucial to assessing and quantifying the anatomical abnormalities in 3D cephalometric analysis. However, the current methods are time-consuming and suffer from large biases in landmark localization, leading to unreliable diagnosis results. In this work, we propose a novel Structure-Aware Long Short-Term Memory framework (SA-LSTM) for efficient and accurate 3D landmark detection.

Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-Based Perception.

State-of-the-art methods for driving-scene LiDAR-based perception (including point cloud semantic segmentation, panoptic segmentation and 3D detection, etc.) often project the point clouds to 2D space and then process them via 2D convolution. Although this cooperation shows the competitiveness in the point cloud, it inevitably alters and abandons the 3D topology and geometric relations.

Self-Supervised Point Set Local Descriptors for Point Cloud Registration.

Descriptors play an important role in point cloud registration. The current state-of-the-art resorts to the high regression capability of deep learning. However, recent deep learning-based descriptors require different levels of annotation and selection of patches, which make the model hard to migrate to new scenarios.

Effects of dietary live yeast Hanseniaspora opuntiae C21 on the immune and disease resistance against Vibrio splendidus infection in juvenile sea cucumber Apostichopus japonicus.

A feeding experiment was conducted to determine effects of Hanseniaspora opuntiae C21 on immune response and disease resistance against Vibrio splendidus infection in juvenile sea cucumbers Apostichopus japonicus. Sea cucumbers were fed with either diets containing C21 at 10(4), 10(5) and 10(6) CFU g(-1) feed or a control diet for 30-50 days, respectively. After feeding for 30 days and 45 days, five sea cucumbers from each tank were sampled for immunological analyses.

[Culture optimization and characterization of an alginate-lyase from Pseudoalteromonas sp. LJ1].

Objectives: The present work aimed to optimize the culture conditions to produce extracellular alginate-lyase by Pseudoalteromonas sp. LJ1.

Methods: A bacterial alginate-lyase producing strain LJ1 was isolated from Laminaria japonica by enrichment culture technique.

Optimization of kappa-carrageenase production by Pseudoalteromonas sp. AJ5.

Objectives: To optimize the culture conditions of Pseudoalteromonas sp. AJ5 for a higher production of extracellular kappa-carrageenase.

Methods: A kappa-carrageenan-degrading bacterium AJ5, capable of utilizing kappa-carrageenan as sole source of carbon and energy, was isolated from the intestine of holothurian Apostichopus japonicus by enrichment culture technique.