Adherence to the dietary approaches to stop hypertension diet reduces the risk of diabetes mellitus: a systematic review and dose-response meta-analysis.

Background: Despite several epidemiological studies reporting a significant association between adherence to the Dietary Approaches to Stop Hypertension (DASH) diet and the risk of diabetes mellitus, the results remain controversial. In this systematic review and meta-analysis, we aimed to summarize the existing evidence from published observational studies and evaluate the dose-response relationship between adherence to the DASH diet and diabetes mellitus risk.

Methods: We performed a systematic search for relevant articles published up to September 2023 using electronic databases of PubMed, Embase, Scopus, and China National Knowledge Infrastructure (CNKI).

TPDC: Point Cloud Completion by Triangular Pyramid Features and Divide-and-Conquer in Complex Environments.

Point cloud completion recovers the complete point clouds from partial ones, providing numerous point cloud information for downstream tasks such as 3-D reconstruction and target detection. However, previous methods usually suffer from unstructured prediction of points in local regions and the discrete nature of the point cloud. To resolve these problems, we propose a point cloud completion network called TPDC.

Time-frequency volatility transmission among energy commodities and financial markets during the COVID-19 pandemic: A Novel TVP-VAR frequency connectedness approach.

This paper investigates the dynamic volatility spillover among energy commodities and financial markets in pre-and mid-COVID-19 periods by utilizing a novel TVP-VAR frequency connectedness approach and the QMLE-based realized volatility data. Our findings indicate that the volatility spillover is mainly driven by long-term components and prominently time-varying with a remarkable but short-lived surge during the COVID-19 outbreak. We further spot that WTI and NGS are prevailingly transmitting and being exposed to the system volatility simultaneously, especially during the global pandemic, suggesting the energy commodity market becoming more integrated with, more influential and meanwhile vulnerable to global financial markets.

BASeg: Boundary aware semantic segmentation for autonomous driving.

Semantic segmentation is a critical component for street understanding task in autonomous driving field. Existing various methods either focus on constructing the object's inner consistency by aggregating global or multi-scale context information, or simply combine semantic features with boundary features to refine object details. Despite impressive, most of them neglect the long-range dependences between the inner objects and boundaries.

SGSNet: A Lightweight Depth Completion Network Based on Secondary Guidance and Spatial Fusion.

The depth completion task aims to generate a dense depth map from a sparse depth map and the corresponding RGB image. As a data preprocessing task, obtaining denser depth maps without affecting the real-time performance of downstream tasks is the challenge. In this paper, we propose a lightweight depth completion network based on secondary guidance and spatial fusion named SGSNet.

Marked-LIEO: Visual Marker-Aided LiDAR/IMU/Encoder Integrated Odometry.

In this paper, we propose a visual marker-aided LiDAR/IMU/encoder integrated odometry, Marked-LIEO, to achieve pose estimation of mobile robots in an indoor long corridor environment. In the first stage, we design the pre-integration model of encoder and IMU respectively to realize the pose estimation combined with the pose estimation from the second stage providing prediction for the LiDAR odometry. In the second stage, we design low-frequency visual marker odometry, which is optimized jointly with LiDAR odometry to obtain the final pose estimation.

TIF-Reg: Point Cloud Registration with Transform-Invariant Features in SE(3).

Three-dimensional point cloud registration (PCReg) has a wide range of applications in computer vision, 3D reconstruction and medical fields. Although numerous advances have been achieved in the field of point cloud registration in recent years, large-scale rigid transformation is a problem that most algorithms still cannot effectively handle. To solve this problem, we propose a point cloud registration method based on learning and transform-invariant features (TIF-Reg).

3D Fast Object Detection Based on Discriminant Images and Dynamic Distance Threshold Clustering.

The object detection algorithm based on vehicle-mounted lidar is a key component of the perception system on autonomous vehicles. It can provide high-precision and highly robust obstacle information for the safe driving of autonomous vehicles. However, most algorithms are often based on a large amount of point cloud data, which makes real-time detection difficult.

Liver segmentation from abdominal CT volumes based on level set and sparse shape composition.

Background And Objective: Liver segmentation from abdominal CT volumes is a primary step for computer-aided surgery and liver disease diagnosis. However, accurate liver segmentation remains a challenging task for intensity inhomogeneity and serious pathologies occurring in liver CT volume. This paper presents a novel framework for accurate liver segmentation from CT images.

Matsuoka's CPG With Desired Rhythmic Signals for Adaptive Walking of Humanoid Robots.

The desired rhythmic signals for adaptive walking of humanoid robots should have proper frequencies, phases, and shapes. Matsuoka's central pattern generator (CPG) is able to generate rhythmic signals with reasonable frequencies and phases, and thus has been widely applied to control the movements of legged robots, such as walking of humanoid robots. However, it is difficult for this kind of CPG to generate rhythmic signals with desired shapes, which limits the adaptability of walking of humanoid robots in various environments.