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). A random-effects model was applied to calculate the combined relative risks (RR) with 95% confidence intervals (CIs) for the highest compared to the lowest categories of DASH score in relation to diabetes mellitus risk. Heterogeneity among the included studies was assessed using the Cochran's Q test and I-squared (I2) statistic. Literature search, study selection, data extraction, and quality assessment were performed by two independent reviewers. RESULTS: Fifteen studies involving 557,475 participants and 57,064 diabetes mellitus cases were eligible for our analyses. Pooled analyses from included studies showed that high adherence to the DASH diet was significantly associated with a reduced risk of diabetes mellitus (RR: 0.82; 95% CI: 0.76-0.90, P < 0.001). Moreover, the dose-response meta-analysis revealed a linear trend between adherence to the DASH diet and diabetes mellitus (RR:0.99; 95%CI: 0.97-1.02, Pdose-response = 0.546, Pnonlinearity = 0.701). Subgroup analyses further revealed a significant inverse association between adherence to the DASH diet and diabetes mellitus risk in case-control studies (RR: 0.65; 95%CI: 0.29-1.43, P < 0.001), with a marginal inverse association in cohort studies (RR:0.83; 95%CI: 0.76-0.91, P < 0.001). Additionally, we conducted analyses separately by comparison and found a significant inverse association between DASH diet and diabetes mellitus risk in T3 vs T1 comparison studies (RR = 0.74; 95%CI: 0.64-0.86, P = 0.012). CONCLUSION: The findings of this study demonstrate a protective association between adherence to the DASH diet and risk of diabetes mellitus. However, further prospective cohort studies and randomized controlled trials are needed to validate these findings.

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. Representing the point cloud as a set of unordered features of points with local geometric information, we devise a Triangular Pyramid Extractor (TPE), using the simplest 3-D structure-a triangular pyramid-to convert the point cloud to a sequence of local geometric information. Our insight of revealing local geometric information in a complex environment is to design a Divide-and-Conquer Splitting Module in a Divide-and-Conquer Splitting Decoder (DCSD) to learn point-splitting patterns that can fit local regions the best. This module employs the Divide-and-Conquer approach to parallelly handle tasks related to fitting ground-truth values to base points and predicting the displacement of split points. This approach aims to make the base points align more closely with the ground-truth values while also forecasting the displacement of split points relative to the base points. Furthermore, we propose a more realistic and challenging benchmark, ShapeNetMask, with more random point cloud input, more complex random item occlusion, and more realistic random environmental perturbations. The results show that our method outperforms both widely used benchmarks as well as the new benchmark.

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. To this end, we present a Boundary Aware Network (BASeg) for semantic segmentation by exploiting boundary information as a significant cue to guide context aggregation. Specifically, a Boundary Refined Module (BRM) is proposed in the BASeg to refine coarse low-level boundary features from a Canny detector by high-level multi-scale semantic features from the backbone, and based on which, the Context Aggregation Module (CAM) is further proposed to capture long-range dependences between the boundary regions and the object inner pixels, achieving mutual gains and enhancing the intra-class consistency. Moreover, our method can be plugged into other CNN backbones for higher performance with a minor computation budget, and obtains 45.72%, 81.2%, and 77.3% of mIoU on the datasets ADE20K, Cityscapes, and CamVid, respectively. Compared with some state-of-the-art ResNet101-based segmentation methods, extensive experiments demonstrate the effectiveness of our method. Our code is available at https://github.com/Lature-Yang/BASeg.

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. We design the image feature extraction module to better extract features from different scales between and within layers in parallel and to generate guidance features. Then, SGSNet uses the secondary guidance to complete the depth completion. The first guidance uses the lightweight guidance module to quickly guide LiDAR feature extraction with the texture features of RGB images. The second guidance uses the depth information completion module for sparse depth map feature completion and inputs it into the DA-CSPN++ module to complete the dense depth map re-guidance. By using a lightweight bootstrap module, the overall network runs ten times faster than the baseline. The overall network is relatively lightweight, up to thirty frames, which is sufficient to meet the speed needs of large SLAM and three-dimensional reconstruction for sensor data extraction. At the time of submission, the accuracy of the algorithm in SGSNet ranked first in the KITTI ranking of lightweight depth completion methods. It was 37.5% faster than the top published algorithms in the rank and was second in the full ranking.

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. In view of the wheel slipping and LiDAR degradation problems, we design an algorithm that can make the optimization weight of encoder odometry and LiDAR odometry adjust adaptively according to yaw angle and LiDAR degradation distance respectively. Finally, we realize the multi-sensor fusion localization through joint optimization of an encoder, IMU, LiDAR, and camera measurement information. Aiming at the problems of GNSS information loss and LiDAR degradation in indoor corridor environment, this method introduces the state prediction information of encoder and IMU and the absolute observation information of visual marker to achieve the accurate pose of indoor corridor environment, which has been verified by experiments in Gazebo simulation environment and real environment.

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). Our algorithm includes four modules, which are the transform-invariant feature extraction module, deep feature embedding module, corresponding point generation module and decoupled singular value decomposition (SVD) module. In the transform-invariant feature extraction module, we design TIF in SE(3) (which means the 3D rigid transformation space) which contains a triangular feature and local density feature for points. It fully exploits the transformation invariance of point clouds, making the algorithm highly robust to rigid transformation. The deep feature embedding module embeds TIF into a high-dimension space using a deep neural network, further improving the expression ability of features. The corresponding point cloud is generated using an attention mechanism in the corresponding point generation module, and the final transformation for registration is calculated in the decoupled SVD module. In an experiment, we first train and evaluate the TIF-Reg method on the ModelNet40 dataset. The results show that our method keeps the root mean squared error (RMSE) of rotation within 0.5∘ and the RMSE of translation error close to 0 m, even when the rotation is up to [-180∘, 180∘] or the translation is up to [-20 m, 20 m]. We also test the generalization of our method on the TUM3D dataset using the model trained on Modelnet40. The results show that our method's errors are close to the experimental results on Modelnet40, which verifies the good generalization ability of our method. All experiments prove that the proposed method is superior to state-of-the-art PCReg algorithms in terms of accuracy and complexity.

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. To solve this problem, this paper proposes a 3D fast object detection method based on three main steps: First, the ground segmentation by discriminant image (GSDI) method is used to convert point cloud data into discriminant images for ground points segmentation, which avoids the direct computing of the point cloud data and improves the efficiency of ground points segmentation. Second, the image detector is used to generate the region of interest of the three-dimensional object, which effectively narrows the search range. Finally, the dynamic distance threshold clustering (DDTC) method is designed for different density of the point cloud data, which improves the detection effect of long-distance objects and avoids the over-segmentation phenomenon generated by the traditional algorithm. Experiments have showed that this algorithm can meet the real-time requirements of autonomous driving while maintaining high accuracy.

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. METHODS: Firstly, a novel level set integrated with intensity bias and position constraint is applied, and for normal liver, the generated liver regions are regarded as the final results. Then, for pathological liver, a sparse shape composition (SSC)-based method is presented to refine liver shapes, followed by an improved graph cut to further optimize segmentation results. The level set-based method is capable of overcoming intensity inhomogeneity in object regions, and the SSC- and graph cut-based strategy has outstanding power to address under-segmentation appearing in pathological livers. RESULTS: The experiments conducted on public databases SLIVER07 and 3Dircadb show that the proposed method can segment both healthy and pathological liver effectively. The segmentation performance in terms of mean ASD, RMSD, MSD, VOE and RVD on SLIVER07 are 0.9mm, 1.8mm, 19.4mm, 5.1% and 0.1%, respectively, and on 3Dircadb are 1.6mm, 3.1mm, 27.2mm, 9.2% and 0.5%, respectively, which outperforms many existing methods. CONCLUSIONS: The proposed method does not require complex training procedure on numerous liver samples, and has satisfying and robust segmentation performance on both normal and pathological liver in various shapes.

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. To address this issue, a new framework that can generate desired rhythmic signals for Matsuoka's CPG is presented. The proposed framework includes three main parts. First, feature processing is conducted to transform the Matsuoka's CPG outputs into a normalized limit cycle. Second, by combining the normalized limit cycle with robot feedback as the feature inputs and setting the required learning objective, the neural network (NN) learns to generate desired rhythmic signals. Finally, in order to ensure the continuity of the desired rhythmic signals, signal filtering is applied to the outputs of NN, with the aim of smoothing the discontinuous parts. Numerical experiments on the proposed framework suggest that it can not only generate a variety of rhythmic signals with desired shapes but also preserve the frequency and phase properties of Matsuoka's CPG. In addition, the proposed framework is embedded into a control system for adaptive omnidirectional walking of humanoid robot NAO. Extensive simulation and real experiments on this control system demonstrate that the proposed framework is able to generate desired rhythmic signals for adaptive walking of NAO on fixed and changing inclined surfaces. Furthermore, the comparison studies verify that the proposed framework can significantly improve the adaptability of NAO's walking compared with the other methods.