Generator with Triangulation for Pedestrians Trajectory Prediction

Pedestrian trajectory prediction is a basic task in computer vision field. The prosperity of artificial intelligence makes the automatic drive, human-robot interaction and surveillance video attract a great deal of attention. Generally, researchers always place emphasis on pedestrian trajectory. The focuses of pedestrian trajectory prediction task are motion pattern modelling and spatio-temporal interaction modelling in the current study. In our paper, we present a GAN-based framework to model pedestrian motion pattern. A Delaunay triangulation algorithm is applied to map the pedestrian interaction. From the perspective of space, both the position interaction and motion interaction of pedestrians can be considered. For example, the influence of the movement direction and motion potential energy of pedestrians on the surrounding pedestrians can be modelled

DSMNet: Deep High-precision 3D Surface Modeling from Sparse Point Cloud Frames

Existing point cloud modeling datasets primarily express the modeling precision by pose or trajectory precision rather than the point cloud modeling effect itself. Under this demand, we first independently construct a set of LiDAR system with an optical stage, and then we build a HPMB dataset based on the constructed LiDAR system, a High-Precision, Multi-Beam, real-world dataset. Second, we propose an modeling evaluation method based on HPMB for object-level modeling to overcome this limitation. In addition, the existing point cloud modeling methods tend to generate continuous skeletons of the global environment, hence lacking attention to the shape of complex objects. To tackle this challenge, we propose a novel learning-based joint framework, DSMNet, for high-precision 3D surface modeling from sparse point cloud frames. DSMNet comprises density-aware Point Cloud Registration (PCR) and geometry-aware Point Cloud Sampling (PCS) to effectively learn the implicit structure feature of sparse point clouds. Extensive experiments demonstrate that DSMNet outperforms the state-of-the-art methods in PCS and PCR on Multi-View Partial Point Cloud (MVP) database. Furthermore, the experiments on the open source KITTI and our proposed HPMB datasets show that DSMNet can be generalized as a post-processing of Simultaneous Localization And Mapping (SLAM), thereby improving modeling precision in environments with sparse point clouds.Comment: To be published in IEEE Geoscience and Remote Sensing Letters (GRSL

2-Hy­droxy-N,N,N-trimethyl-3-tetra­decyl­oxypropan-1-aminium bromide

In the crystal structure of the title compound, C20H44NO2 +·Br−, the cation and anion are connected via an O—H⋯Br hydrogen bond, forming an ionic pair. The cation is disordered over two conformations related by a mirror plane, and the anion is situated on a mirror plane so that the asymmetric unit contains half of the ionic pair. The long alkyl chain in the cation adopts an all-trans conformation. The crystal packing exhibits weak inter­molecular C—H⋯O inter­actions

Design and Implementation of a Wireless Sensor Network-Based Remote Water-Level Monitoring System

The proposed remote water-level monitoring system (RWMS) consists of a field sensor module, a base station module, adata center module and aWEB releasing module. It has advantages in real time and synchronized remote control, expandability, and anti-jamming capabilities. The RWMS can realize real-time remote monitoring, providing early warning of events and protection of the safety of monitoring personnel under certain dangerous circumstances. This system has been successfully applied in Poyanghu Lake. The cost of the whole system is approximately 1,500 yuan (RMB)

Density functional theory calculation of the properties of carbon vacancy defects in silicon carbide

As a promisingmaterial for quantumtechnology, silicon carbide (SiC) has attracted great interest inmaterials science. Carbon vacancy is a dominant defect in 4H-SiC. Thus, understanding the properties of this defect is critical to its application, and the atomic and electronic structures of the defects needs to be identified. In this study, density functional theorywas used to characterize the carbon vacancy defects in hexagonal (h) and cubic (k) lattice sites. The zero-phonon line energies, hyperfine tensors, and formation energies of carbon vacancies with different charge states (2-, -, 0,+ and 2+) in different supercells (72, 128, 400 and 576 atoms)were calculated using standard Perdew-Burke-Ernzerhof and Heyd-Scuseria-Ernzerhof methods. Results show that the zero-phonon line energies of carbon vacancy defects are much lower than those of divacancy defects, indicating that the former is more likely to reach the excited state than the latter. The hyperfine tensors of VC+(h) and VC+(k) were calculated. Comparison of the calculated hyperfine tensor with the experimental results indicates the existence of carbon vacancies in SiC lattice. The calculation of formation energy shows that the most stable carbon vacancy defects in the material are VC2+(k), VC+(k), VC(k), VC-(k) and VC2-(k) as the electronic chemical potential increases.Peer reviewe

A Physically Based Algorithm for Non-Blackbody Correction of Cloud-Top Temperature and Application to Convection Study

Cloud-top temperature (CTT) is an important parameter for convective clouds and is usually different from the 11-micrometers brightness temperature due to non-blackbody effects. This paper presents an algorithm for estimating convective CTT by using simultaneous passive [Moderate Resolution Imaging Spectroradiometer (MODIS)] and active [CloudSat 1 Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO)] measurements of clouds to correct for the non-blackbody effect. To do this, a weighting function of the MODIS 11-micrometers band is explicitly calculated by feeding cloud hydrometer profiles from CloudSat and CALIPSO retrievals and temperature and humidity profiles based on ECMWF analyses into a radiation transfer model.Among 16 837 tropical deep convective clouds observed by CloudSat in 2008, the averaged effective emission level (EEL) of the 11-mm channel is located at optical depth; approximately 0.72, with a standard deviation of 0.3. The distance between the EEL and cloud-top height determined by CloudSat is shown to be related to a parameter called cloud-top fuzziness (CTF), defined as the vertical separation between 230 and 10 dBZ of CloudSat radar reflectivity. On the basis of these findings a relationship is then developed between the CTF and the difference between MODIS 11-micrometers brightness temperature and physical CTT, the latter being the non-blackbody correction of CTT. Correction of the non-blackbody effect of CTT is applied to analyze convective cloud-top buoyancy. With this correction, about 70% of the convective cores observed by CloudSat in the height range of 6-10 km have positive buoyancy near cloud top, meaning clouds are still growing vertically, although their final fate cannot be determined by snapshot observations

Water Extract of Liuwei Dihuang Reduces Weight Gain and Visceral Fat in Obese Rats

The present study was conducted to determine the effect and mechanism of action of Liuwei Dihuang (LWDH) on weight gain and visceral fat deposition in male obese-prone CD rats. The rats were divided into three groups and fed a high-fat diet (60 kcal% from fat). Two treatment groups received 600 (WE600) or 1200 (WE1200) mg/kg/d LWDH water extract dissolved in water by gavage feeding once a day for 10 weeks. The control rats were gavaged with the vehicle. Daily food intake and weekly body weight were recorded. Energy metabolism was measured using an indirect calorimeter during week 8 of the treatment. At the end of the study, rats were sacrificed. Immediately, visceral fat pads were dissected and weighed. Serum was collected for the measurement of blood lipids and hormones. It was found that WE1200 lowered body weight after 3 weeks of treatment and the effect was maintained throughout the remaining study period. WE1200 also lowered visceral fat mass, serum leptin, plasma free fatty acids and cholesterol, respectively. The energy expenditure was increased by WE1200 in both the light and dark periods. Oxygen consumption, carbon dioxide production and fat oxidation were increased in both light and dark periods, whereas carbohydrate oxidation increased only in the light period in the WE1200 group. Rats in the WE600 showed lower serum free fatty acids and leptin levels, while showing no effect on the other parameters compared to the control. These results demonstrated potential of using LWDH water extract to treat obesity and its related complications. The effect may be attributable to the increase of energy expenditure, decrease of food intake and improvement of leptin sensitivity

FlashDecoding++: Faster Large Language Model Inference on GPUs

As the Large Language Model (LLM) becomes increasingly important in various domains. However, the following challenges still remain unsolved in accelerating LLM inference: (1) Synchronized partial softmax update. The softmax operation requires a synchronized update operation among each partial softmax result, leading to ~20% overheads for the attention computation in LLMs. (2) Under-utilized computation of flat GEMM. The shape of matrices performing GEMM in LLM inference is flat, leading to under-utilized computation and >50% performance loss after padding zeros in previous designs. (3) Performance loss due to static dataflow. Kernel performance in LLM depends on varied input data features, hardware configurations, etc. A single and static dataflow may lead to a 50.25% performance loss for GEMMs of different shapes in LLM inference. We present FlashDecoding++, a fast LLM inference engine supporting mainstream LLMs and hardware back-ends. To tackle the above challenges, FlashDecoding++ creatively proposes: (1) Asynchronized softmax with unified max value. FlashDecoding++ introduces a unified max value technique for different partial softmax computations to avoid synchronization. (2) Flat GEMM optimization with double buffering. FlashDecoding++ points out that flat GEMMs with different shapes face varied bottlenecks. Then, techniques like double buffering are introduced. (3) Heuristic dataflow with hardware resource adaptation. FlashDecoding++ heuristically optimizes dataflow using different hardware resource considering input dynamics. Due to the versatility of optimizations in FlashDecoding++, FlashDecoding++ can achieve up to 4.86x and 2.18x speedup on both NVIDIA and AMD GPUs compared to Hugging Face implementations. FlashDecoding++ also achieves an average speedup of 1.37x compared to state-of-the-art LLM inference engines on mainstream LLMs