GridMM: Grid Memory Map for Vision-and-Language Navigation

Vision-and-language navigation (VLN) enables the agent to navigate to a remote location following the natural language instruction in 3D environments. To represent the previously visited environment, most approaches for VLN implement memory using recurrent states, topological maps, or top-down semantic maps. In contrast to these approaches, we build the top-down egocentric and dynamically growing Grid Memory Map (i.e., GridMM) to structure the visited environment. From a global perspective, historical observations are projected into a unified grid map in a top-down view, which can better represent the spatial relations of the environment. From a local perspective, we further propose an instruction relevance aggregation method to capture fine-grained visual clues in each grid region. Extensive experiments are conducted on both the REVERIE, R2R, SOON datasets in the discrete environments, and the R2R-CE dataset in the continuous environments, showing the superiority of our proposed method

UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebase

Point-, voxel-, and range-views are three representative forms of point clouds. All of them have accurate 3D measurements but lack color and texture information. RGB images are a natural complement to these point cloud views and fully utilizing the comprehensive information of them benefits more robust perceptions. In this paper, we present a unified multi-modal LiDAR segmentation network, termed UniSeg, which leverages the information of RGB images and three views of the point cloud, and accomplishes semantic segmentation and panoptic segmentation simultaneously. Specifically, we first design the Learnable cross-Modal Association (LMA) module to automatically fuse voxel-view and range-view features with image features, which fully utilize the rich semantic information of images and are robust to calibration errors. Then, the enhanced voxel-view and range-view features are transformed to the point space,where three views of point cloud features are further fused adaptively by the Learnable cross-View Association module (LVA). Notably, UniSeg achieves promising results in three public benchmarks, i.e., SemanticKITTI, nuScenes, and Waymo Open Dataset (WOD); it ranks 1st on two challenges of two benchmarks, including the LiDAR semantic segmentation challenge of nuScenes and panoptic segmentation challenges of SemanticKITTI. Besides, we construct the OpenPCSeg codebase, which is the largest and most comprehensive outdoor LiDAR segmentation codebase. It contains most of the popular outdoor LiDAR segmentation algorithms and provides reproducible implementations. The OpenPCSeg codebase will be made publicly available at https://github.com/PJLab-ADG/PCSeg.Comment: ICCV 2023; 21 pages; 9 figures; 18 tables; Code at https://github.com/PJLab-ADG/PCSe

Dual Opposing Roles of Metallothionein Overexpression in C57BL/6J Mouse Pancreatic β-Cells

<div><p>Background</p><p>Growing evidence indicates that oxidative stress (OS), a persistent state of excess amounts of reactive oxygen species (ROS) along with reactive nitrogen species (RNS), plays an important role in insulin resistance, diabetic complications, and dysfunction of pancreatic β-cells. Pancreatic β-cells contain exceptionally low levels of antioxidant enzymes, rendering them susceptible to ROS-induced damage. Induction of antioxidants has been proposed to be a way for protecting β-cells against oxidative stress. Compared to other antioxidants that act against particular β-cell damages, metallothionein (MT) is the most effective in protecting β-cells from several oxidative stressors including nitric oxide, peroxynitrite, hydrogen peroxide, superoxide and streptozotocin (STZ). We hypothesized that MT overexpression in pancreatic β-cells would preserve β-cell function in C57BL/6J mice, an animal model susceptible to high fat diet-induced obesity and type 2 diabetes.</p><p>Research Design and Methods</p><p>The pancreatic β-cell specific MT overexpression was transferred to C57BL/6J background by backcrossing. We studied transgenic MT (MT-tg) mice and wild-type (WT) littermates at 8 weeks and 18 weeks of age. Several tests were performed to evaluate the function of islets, including STZ <i>in vivo</i> treatment, intraperitoneal glucose tolerance tests (IPGTT) and plasma insulin levels during IPGTT, pancreatic and islet insulin content measurement, insulin secretion, and islet morphology assessment. Gene expression in islets was performed by quantitative real-time PCR and PCR array analysis. Protein levels in pancreatic sections were evaluated by using immunohistochemistry.</p><p>Results</p><p>The transgenic MT protein was highly expressed in pancreatic islets. MT-tg overexpression significantly protected mice from acute STZ-induced ROS at 8 weeks of age; unexpectedly, however, MT-tg impaired glucose stimulated insulin secretion (GSIS) and promoted the development of diabetes. Pancreatic β-cell function was significantly impaired, and islet morphology was also abnormal in MT-tg mice, and more severe damage was detected in males. The unique gene expression pattern and abnormal protein levels were observed in MT-tg islets.</p><p>Conclusions</p><p>MT overexpression protected β-cells from acute STZ-induced ROS damages at young age, whereas it impaired GSIS and promoted the development of diabetes in adult C57BL/6J mice, and more severe damage was found in males.</p></div

Characterization and phylogenetic analysis of the complete mitochondrial genome in Xiaoxiang chicken (Gallus gallus domesticus)

Xiaoxiang chicken (Gallus gallus domesticus) is one of the native breeds in the Southeastern of Guizhou province, China. The complete mitochondrial genome sequence of Xiaoxiang chicken (small-sized breed chicken) was obtained for the first time. The mitogenome is 16,784 bp in length, and it contained a D-loop region, two rRNA genes, 13 protein-coding genes, and 22 tRNA genes. A neighbour-joining phylogenetic tree was structured based on the D-loop, which indicated that the Red junglefowl was the direct ancestor of Xiaoxiang chicken, and both were closed to the Silky chicken and Dongan black chicken

Using Linkography and Situated FBS Co-Design Model to Explore User Participatory Conceptual Design Process

To unravel the complex challenges addressed by product innovation, it is oftentimes essential for users to participate in the design process. However, there is a paucity of research in terms of in-depth exploration of the cognitive patterns and dynamic design processes of co-design with user participation in the existing design cognition research. The current study aimed to investigate the cognition activities involved in the process of co-design between user and designer at both the individual and team levels. The combination method of linkography and the situated function&ndash;behavior&ndash;structure (FBS) co-design model was carried out to encode and analyze the protocol data. The results showed that, at the individual level, designers and users adopted different design strategies to promote the progress of the design. In addition, the interaction activities among users and designers varied in different co-design processes. However, at the team level, the collaborators showed systematic thinking modes, and each design move was two-way. This cognitive strategy of the innovation team ensured the continuity and effectiveness of the co-design process. Theoretically, these findings will bring new insights for studies on team cognition activities and contribute to building user-centric design theory by uncovering the dynamic design processes of co-design with user participatory. In addition, the study makes a methodological contribution by illustrating how linkography and the situated FBS co-design model can be utilized to analyze the team cognition during co-design activities

Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review

Deep Learning (DL) is the state-of-the-art machine learning technology, which shows superior performance in computer vision, bioinformatics, natural language processing, and other areas. Especially as a modern image processing technology, DL has been successfully applied in various tasks, such as object detection, semantic segmentation, and scene analysis. However, with the increase of dense scenes in reality, due to severe occlusions, and small size of objects, the analysis of dense scenes becomes particularly challenging. To overcome these problems, DL recently has been increasingly applied to dense scenes and has begun to be used in dense agricultural scenes. The purpose of this review is to explore the applications of DL for dense scenes analysis in agriculture. In order to better elaborate the topic, we first describe the types of dense scenes in agriculture, as well as the challenges. Next, we introduce various popular deep neural networks used in these dense scenes. Then, the applications of these structures in various agricultural tasks are comprehensively introduced in this review, including recognition and classification, detection, counting and yield estimation. Finally, the surveyed DL applications, limitations and the future work for analysis of dense images in agriculture are summarized

Prediction of Soluble Solids Content in Green Plum by Using a Sparse Autoencoder

The soluble solids content (SSC) affects the flavor of green plums and is an important parameter during processing. In recent years, the hyperspectral technology has been widely used in the nondestructive testing of fruit ingredients. However, the prediction accuracy of most models can hardly be improved further. The rapid development of deep learning technology has established the foundation for the improvement of building models. A new hyperspectral imaging system aimed at measuring the green plum SSC is developed, and a sparse autoencoder (SAE)&ndash;partial least squares regression (PLSR) model is combined to further improve the accuracy of component prediction. The results of the experiment show that the SAE&ndash;PLSR model, which has a correlation coefficient of 0.938 and root mean square error of 0.654 for the prediction set, can achieve better performance for the SSC prediction of green plums than the three traditional methods. In this paper, integration approaches have combined three different pretreatment methods with PLSR to predict the SSC in green plums. The SAE&ndash;PLSR model has shown good prediction performance, indicating that the proposed SAE&ndash;PLSR model can effectively detect the SSC in green plums

Ultralong afterglow and unity quantum yield from a transparent CsCdCl3:Mn crystal

Abstract Transparent afterglow crystals are keenly desired for three‐dimensional information storage. Herein, CsCdCl3 perovskite crystals were grown by a programmable cooling procedure in a hydrothermal reactor. The pristine crystal showed an abnormal optical behavior where the absorption increased by 2.3 folds at high temperature, leading to a fourfold boost of photoluminescence (PL) intensity. After Mn2+ doping, the PL quantum yield was improved to nearly unity. Importantly, the doped crystals exhibited an ultralong afterglow up to 12 h after ceasing UV excitation and a high transmittance up to 75% in the visible region. This work brought a new member to the library of transparent afterglow crystal, opening up many possibilities to advanced applications such as volumetric display and three‐dimensional information encryption