Point Cloud Processing via Recurrent Set Encoding

We present a new permutation-invariant network for 3D point cloud processing. Our network is composed of a recurrent set encoder and a convolutional feature aggregator. Given an unordered point set, the encoder firstly partitions its ambient space into parallel beams. Points within each beam are then modeled as a sequence and encoded into subregional geometric features by a shared recurrent neural network (RNN). The spatial layout of the beams is regular, and this allows the beam features to be further fed into an efficient 2D convolutional neural network (CNN) for hierarchical feature aggregation. Our network is effective at spatial feature learning, and competes favorably with the state-of-the-arts (SOTAs) on a number of benchmarks. Meanwhile, it is significantly more efficient compared to the SOTAs.Comment: AAAI201

Nitrogen addition affects eco-physiological interactions between two tree species dominating in subtropical forests

Nitrogen (N) deposition affects plant growth and interspecific interaction. This study aimed to explore the effect of N deposition on the growth and eco-physiological interactions between two tree species dominating in subtropical forests. A greenhouse experiment was conducted for 6 months in which the conifer Cunninghamia lanceolata and the broadleaved Phoebe chekiangensis were grown in monocultures and in a mixture under two levels of N addition: 0 and 45 kg ha(-1) yr(-1). The plant growth, root architecture, biomass distribution, element contents in plants and soil, and photosynthetic physiology were determined. The height and crown width of both seedlings tended to be higher in the mixture than in the monoculture when grown without N addition. P. chekiangensis was superior to C. lanceolata in resource acquisition and showed a greater net photosynthetic rate, plant height, crown width, total biomass, and belowground biomass distribution. In the mixture, N addition increased the net photosynthetic rate and decreased the height, ground diameter, and crown width of both species. Belowground biomass distribution was decreased in C. lanceolata but increased in P. chekiangensis under N addition. The P contents in both seedlings were higher in the mixture than in monocultures. Results showed N addition aggravated the competition and weakened the growth of both species in the mixture, largely determined by the competition for resources through the changing root architecture and biomass allocation. Our results provide new insights into the mechanisms of interspecific interaction in response to increasing N deposition in silvicultural practice.Peer reviewe

Socioeconomic impact assessment of China's CO2 emissions peak prior to 2030

China is the largest emitter of carbon emissions in the world. In this paper, we present an Integrated Model of Economy and Climate (IMEC), an optimization model based on the input-output model. The model is designed to assess the tradeoff between emission deceleration and economic growth. Given that China's projected average growth rate will exceed 5% over the next two decades, we find that China may reach its peak CO2 emissions levels by 2026. According to this scenario, China's carbon emissions will peak at 11.20 Gt in 2026 and will then decline to 10.84 Gt in 2030. Accordingly, approximately 22 Gt of CO2 will be removed from 2015 to 2035 relative to the scenario wherein China's CO2 emissions peak in 2030. While this earlier peaking of carbon emissions will result in a decline in China's GDP, several sectors, such as Machinery and Education, will benefit. In order to reach peak CO2 emissions by 2026, China needs to reduce its annual GDP growth rate to less than 4.5% by 2030 and decrease energy and carbon intensity levels by 43% and 45%, respectively, from 2015 to 2030

Towards Integrated Traffic Control with Operating Decentralized Autonomous Organization

With a growing complexity of the intelligent traffic system (ITS), an integrated control of ITS that is capable of considering plentiful heterogeneous intelligent agents is desired. However, existing control methods based on the centralized or the decentralized scheme have not presented their competencies in considering the optimality and the scalability simultaneously. To address this issue, we propose an integrated control method based on the framework of Decentralized Autonomous Organization (DAO). The proposed method achieves a global consensus on energy consumption efficiency (ECE), meanwhile to optimize the local objectives of all involved intelligent agents, through a consensus and incentive mechanism. Furthermore, an operation algorithm is proposed regarding the issue of structural rigidity in DAO. Specifically, the proposed operation approach identifies critical agents to execute the smart contract in DAO, which ultimately extends the capability of DAO-based control. In addition, a numerical experiment is designed to examine the performance of the proposed method. The experiment results indicate that the controlled agents can achieve a consensus faster on the global objective with improved local objectives by the proposed method, compare to existing decentralized control methods. In general, the proposed method shows a great potential in developing an integrated control system in the ITSComment: 6 pages, 6 figures. To be published in 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC

Object-Guided Instance Segmentation for Biological Images

Instance segmentation of biological images is essential for studying object behaviors and properties. The challenges, such as clustering, occlusion, and adhesion problems of the objects, make instance segmentation a non-trivial task. Current box-free instance segmentation methods typically rely on local pixel-level information. Due to a lack of global object view, these methods are prone to over- or under-segmentation. On the contrary, the box-based instance segmentation methods incorporate object detection into the segmentation, performing better in identifying the individual instances. In this paper, we propose a new box-based instance segmentation method. Mainly, we locate the object bounding boxes from their center points. The object features are subsequently reused in the segmentation branch as a guide to separate the clustered instances within an RoI patch. Along with the instance normalization, the model is able to recover the target object distribution and suppress the distribution of neighboring attached objects. Consequently, the proposed model performs excellently in segmenting the clustered objects while retaining the target object details. The proposed method achieves state-of-the-art performances on three biological datasets: cell nuclei, plant phenotyping dataset, and neural cells.Comment: accepted to AAAI202