Effects of climate and plant functional types on forest above-ground biomass accumulation

BackgroundForest above-ground biomass (AGB) accumulation is widely considered an important tool for mitigating climate change. However, the general pattern of forest AGB accumulation associated with age and climate gradients across various forest functional types at a global scale have remained unclear. In this study, we compiled a global AGB data set and applied a Bayesian statistical model to reveal the age-related dynamics of forest AGB accumulation, and to quantify the effects of mean annual temperature and annual precipitation on the initial AGB accumulation rate and on the saturated AGB characterizing the limit to AGB accumulation.ResultsThe results of the study suggest that mean annual temperature has a significant positive effect on the initial AGB accumulation rate in needleleaf evergreen forest, and a negative effect in broadleaf deciduous forest; whereas annual precipitation has a positive effect in broadleaf deciduous forest, and negative effect in broadleaf evergreen forest. The positive effect of mean annual temperature on the saturated AGB in broadleaf evergreen forest is greater than in broadleaf deciduous forest; annual precipitation has a greater negative effect on the saturated AGB in deciduous forests than in evergreen forests. Additionally, the difference of AGB accumulation rate across four forest functional types is closely correlated with the forest development stage at a given climate.ConclusionsThe contrasting responses of AGB accumulation rate to mean annual temperature and precipitation across four forest functional types emphasizes the importance of incorporating the complexity of forest types into the models which are used in planning climate change mitigation. This study also highlights the high potential for further AGB growth in existing evergreen forests.Peer reviewe

A Sophisticated Method of the Mechanical Design of Cable Accessories Focusing on Interface Contact Pressure

The most critical positions of a prefabricated cable accessory, from the electrical point of view, are the interfaces between the stress cone and its surroundings. Accordingly, the contact pressure on those interfaces needs to be carefully designed to assure both good dielectric strength and smooth installation of the stress cone. Nevertheless, since stress cones made from rubber are under large deformation after installation, their internal stress distribution is neither practical to measure directly by planting sensors, nor feasible to compute accurately with the conventional theory of linear structural mechanics. This paper presents one sophisticated method for computing the mechanical stress distribution in rubber stress cones of cable accessories by employing hyperelastic models in a computation model based on the finite element method. This method offers accurate results for rubber bodies of complex geometries and large deformations. Based on the method, a case study of a composite prefabricated termination for extruded cables is presented, and the sensitivity analysis is given as well

An Efficient FPGA-based Accelerator for Deep Forest

Deep Forest is a prominent machine learning algorithm known for its high accuracy in forecasting. Compared with deep neural networks, Deep Forest has almost no multiplication operations and has better performance on small datasets. However, due to the deep structure and large forest quantity, it suffers from large amounts of calculation and memory consumption. In this paper, an efficient hardware accelerator is proposed for deep forest models, which is also the first work to implement Deep Forest on FPGA. Firstly, a delicate node computing unit (NCU) is designed to improve inference speed. Secondly, based on NCU, an efficient architecture and an adaptive dataflow are proposed, in order to alleviate the problem of node computing imbalance in the classification process. Moreover, an optimized storage scheme in this design also improves hardware utilization and power efficiency. The proposed design is implemented on an FPGA board, Intel Stratix V, and it is evaluated by two typical datasets, ADULT and Face Mask Detection. The experimental results show that the proposed design can achieve around 40x speedup compared to that on a 40 cores high performance x86 CPU.Comment: 5 pages, 5 figures, conferenc

A Fast Maximum kk-Plex Algorithm Parameterized by the Degeneracy Gap

Given a graph, the $k$-plex is a vertex set in which each vertex is not adjacent to at most $k-1$ other vertices in the set. The maximum $k$-plex problem, which asks for the largest $k$-plex from a given graph, is an important but computationally challenging problem in applications like graph search and community detection. So far, there is a number of empirical algorithms without sufficient theoretical explanations on the efficiency. We try to bridge this gap by defining a novel parameter of the input instance, $g_k(G)$, the gap between the degeneracy bound and the size of maximum $k$-plex in the given graph, and presenting an exact algorithm parameterized by $g_k(G)$. In other words, we design an algorithm with running time polynomial in the size of input graph and exponential in $g_k(G)$ where $k$ is a constant. Usually, $g_k(G)$ is small and bounded by $O(\log{(|V|)})$ in real-world graphs, indicating that the algorithm runs in polynomial time. We also carry out massive experiments and show that the algorithm is competitive with the state-of-the-art solvers. Additionally, for large $k$ values such as $15$ and $20$, our algorithm has superior performance over existing algorithms.Comment: IJCAI'202

Prevalence of allergic rhinitis among adults in urban and rural areas of China : a population-based cross-sectional survey

Purpose: The aim of the present study was to compare the prevalence of self-reported and confirmable allergic rhinitis (AR) with positive skin prick test (SPT) results among adults living in urban and rural areas of China. Methods: Adults from a community in Beijing and a village in Baoding were selected as representative urban and rural dwellers, respectively. All eligible residents were enrolled from the population register and received a face-to-face interview using modified validated questionnaires. Equal sets of randomly selected self-reporting AR-positive and AR-negative participants who responded to the questionnaires were also investigated using skin prick tests. Results: A total of 803 participants in the rural area and a total of 1,499 participants in the urban area completed the questionnaires, with response rates being 75.9% and 81.5% respectively. The prevalence of self-reported AR of the rural area (19.1%) was significantly higher than that of the urban area (13.5%). The elementary school of educational level increased the risk of having AR (adjusted OR=2.198, 95% CI=1.072-2.236). The positive SET rates among subjects with self-reported AR in the rural and urban areas were 32.5% and 53.3%, respectively; the confirmable AR prevalence of 6.2% and 7.2% among the rural and urban adults, respectively. Conclusions: The prevalence of confirmable AR is similar between rural and urban areas in China, although there is a higher prevalence of self-reported AR in the former

Multispecies coexistence in fragmented landscapes

Spatial dynamics have long been recognized as an important driver of biodiversity. However, our understanding of species’ coexistence under realistic landscape configurations has been limited by lack of adequate analytical tools. To fill this gap, we develop a spatially explicit metacommunity model of multiple competing species and derive analytical criteria for their coexistence in fragmented heterogeneous landscapes. Specifically, we propose measures of niche and fitness differences for metacommunities, which clarify how spatial dynamics and habitat configuration interact with local competition to determine coexistence of species. We parameterize our model with a Bayesian approach using a 36-y time-series dataset of three Daphnia species in a rockpool metacommunity covering >500 patches. Our results illustrate the emergence of interspecific variation in extinction and recolonization processes, including their dependencies on habitat size and environmental temperature. We find that such interspecific variation contributes to the coexistence of Daphnia species by reducing fitness differences and increasing niche differences. Additionally, our parameterized model allows separating the effects of habitat destruction and temperature change on species extinction. By integrating coexistence theory and metacommunity theory, our study provides platforms to increase our understanding of species’ coexistence in fragmented heterogeneous landscapes and the response of biodiversity to environmental changes

EC^2: Emergent Communication for Embodied Control

Embodied control requires agents to leverage multi-modal pre-training to quickly learn how to act in new environments, where video demonstrations contain visual and motion details needed for low-level perception and control, and language instructions support generalization with abstract, symbolic structures. While recent approaches apply contrastive learning to force alignment between the two modalities, we hypothesize better modeling their complementary differences can lead to more holistic representations for downstream adaption. To this end, we propose Emergent Communication for Embodied Control (EC^2), a novel scheme to pre-train video-language representations for few-shot embodied control. The key idea is to learn an unsupervised "language" of videos via emergent communication, which bridges the semantics of video details and structures of natural language. We learn embodied representations of video trajectories, emergent language, and natural language using a language model, which is then used to finetune a lightweight policy network for downstream control. Through extensive experiments in Metaworld and Franka Kitchen embodied benchmarks, EC^2 is shown to consistently outperform previous contrastive learning methods for both videos and texts as task inputs. Further ablations confirm the importance of the emergent language, which is beneficial for both video and language learning, and significantly superior to using pre-trained video captions. We also present a quantitative and qualitative analysis of the emergent language and discuss future directions toward better understanding and leveraging emergent communication in embodied tasks.Comment: Published in CVPR202

Multispecies coexistence in fragmented landscapes

Publisher Copyright: Copyright © 2022 the Author(s). Published by PNAS.Spatial dynamics have long been recognized as an important driver of biodiversity. However, our understanding of species’ coexistence under realistic landscape configurations has been limited by lack of adequate analytical tools. To fill this gap, we develop a spatially explicit metacommunity model of multiple competing species and derive analytical criteria for their coexistence in fragmented heterogeneous landscapes. Specifically, we propose measures of niche and fitness differences for metacommunities, which clarify how spatial dynamics and habitat configuration interact with local competition to determine coexistence of species. We parameterize our model with a Bayesian approach using a 36-y time-series dataset of three Daphnia species in a rockpool metacommunity covering >500 patches. Our results illustrate the emergence of interspecific variation in extinction and recolonization processes, including their dependencies on habitat size and environmental temperature. We find that such interspecific variation contributes to the coexistence of Daphnia species by reducing fitness differences and increasing niche differences. Additionally, our parameterized model allows separating the effects of habitat destruction and temperature change on species extinction. By integrating coexistence theory and metacommunity theory, our study provides platforms to increase our understanding of species’ coexistence in fragmented heterogeneous landscapes and the response of biodiversity to environmental changes.Peer reviewe

Every Frame Counts: Joint Learning of Video Segmentation and Optical Flow

A major challenge for video semantic segmentation is the lack of labeled data. In most benchmark datasets, only one frame of a video clip is annotated, which makes most supervised methods fail to utilize information from the rest of the frames. To exploit the spatio-temporal information in videos, many previous works use pre-computed optical flows, which encode the temporal consistency to improve the video segmentation. However, the video segmentation and optical flow estimation are still considered as two separate tasks. In this paper, we propose a novel framework for joint video semantic segmentation and optical flow estimation. Semantic segmentation brings semantic information to handle occlusion for more robust optical flow estimation, while the non-occluded optical flow provides accurate pixel-level temporal correspondences to guarantee the temporal consistency of the segmentation. Moreover, our framework is able to utilize both labeled and unlabeled frames in the video through joint training, while no additional calculation is required in inference. Extensive experiments show that the proposed model makes the video semantic segmentation and optical flow estimation benefit from each other and outperforms existing methods under the same settings in both tasks.Comment: Published in AAAI 202