Effects of land incremental value allocation on rural operational construction land (ROCL) under market mechanism: case study in China

The use of the market mechanism to convert the rural operational construction land (ROCL) into urban construction land without ownership changes is currently being introduced into reform pilot projects in China, changing the only form of governmental expropriation in the past. The new system allows rural collective economic organizations and members of the rural collective economy to directly participate in the allocation of land incremental value increases due to changes in land use. This replaces the previous way of allocating only the original use compensation from the government. This paper investigates the collectively owned new system, to establish the positive effects and shortcoming of the new model. Three cases are applied for the analysis using inductive-deductive reasoning methodology based on the property right and landrent theories. We have found that local government land adjustment charges on the transactions of rural construction land are suggested to be from 16 to 20 percent. The share ownership quantification model (SOQM) of land incremental value allocation between the collective economic organizations and members is effective and beneficial to the development of the rural collective economy and its members

Threshold effects as the origin of Zcs(4000)Z_{cs}(4000), Zcs(4220)Z_{cs}(4220) and X(4700)X(4700) observed in B+→J/ψϕK+B^+\to J/\psi \phi K^+

We investigate the $B^+\to J/\psi \phi K^+$ decay via various rescattering diagrams. Without introducing genuine exotic resonances, it is shown that the $Z_{cs}(4000)$, $Z_{cs}(4220)$ and $X(4700)$ reported by the LHCb collaboration can be simulated by the $J/\psi K^{*+}$, $\psi^\prime K^+$ and $\psi^\prime \phi$ threshold cusps, respectively. These cusps are enhanced by some nearby triangle singularities. The $X(4685)$ with $J^P=1^+$ cannot be well simulated by the threshold effects in our model, which implies that it may be a genuine resonance.Comment: 7 pages, 6 figures, 2 table

Bis[4-(2-hydroxy­benzyl­amino)phen­yl] ether

The title compound, C26H24N2O3, was synthesized by reduction of the corresponding Schiff base. The mol­ecule does not possess crystallographic or non-crystallographic symmetry. The dihedral angle between the oxygen-bridged benzene rings is 67.98 (8)°. Both hydroxyl groups are involved in O—H⋯O intra­molecular hydrogen bonding. The mol­ecules are linked into a two-dimensional network parallel to the (010) plane by N—H⋯O hydrogen bonds

GBD-TS: Goal-based Pedestrian Trajectory Prediction with Diffusion using Tree Sampling Algorithm

Predicting pedestrian trajectories is crucial for improving the safety and effectiveness of autonomous driving and mobile robots. However, this task is nontrivial due to the inherent stochasticity of human motion, which naturally requires the predictor to generate multi-model prediction. Previous works have used various generative methods, such as GAN and VAE, for pedestrian trajectory prediction. Nevertheless, these methods may suffer from problems, including mode collapse and relatively low-quality results. The denoising diffusion probabilistic model (DDPM) has recently been applied to trajectory prediction due to its simple training process and powerful reconstruction ability. However, current diffusion-based methods are straightforward without fully leveraging input information and usually require many denoising iterations leading to a long inference time or an additional network for initialization. To address these challenges and promote the application of diffusion models in trajectory prediction, we propose a novel scene-aware multi-modal pedestrian trajectory prediction framework called GBD. GBD combines goal prediction with the diffusion network. First, the goal predictor produces multiple goals, and then the diffusion network generates multi-modal trajectories conditioned on these goals. Furthermore, we introduce a new diffusion sampling algorithm named tree sampling (TS), which leverages common feature to reduce the inference time and improve accuracy for multi-modal prediction. Experimental results demonstrate that our GBD-TS method achieves state-of-the-art performance with real-time inference speed.Comment: Submitted to ICRA 202