Continuous planting under a high density enhances the competition for nutrients among young Cunninghamia lanceolata saplings

International audienceAbstractKey messageA high-density plantation inhibited growth and biomass accumulation of Cunninghamia lanceolata(Lamb.) Hook. saplings, as well as their photosynthesis. This inhibition was enhanced in a soil that had been previously planted with the same species. The main factors limiting photosynthesis and growth were leaf-level irradiance and nutrient availability, mainly of P and Mg.ContextThe planting density and continuous planting greatly affect the photosynthesis and productivity of Chinese fir plantations. The effects of high density and of continuous plantations over several revolutions need be disentangled.AimsIn this study, the responses of C. lanceolata seedlings to a high planting density were tested. Two soils were compared: a soil from a secondary forest and one from a continuous Chinese fir plantation. The study focused on growth and the potential processes involved in deduced photosynthesis.MethodsC. lanceolata seedlings were planted in wooden boxes (100 × 100 × 50 cm) with high and low planting densities (16 vs 1 plant m−2) in two types of soil.ResultsUnder the high planting density, C. lanceolata showed less growth and biomass accumulation at the individual level and lower photosynthetic rate and instantaneous photosynthetic nutrient use efficiency (PNUE and PPUE) at the leaf level. These negative effects were larger in soils that have been continuously planted with Chinese fir. The low photosynthesis was related to low phosphorus and magnesium contents in the leaves, changes in the foliar N/P and chlorophyll a/b ratios, and the limitation of the mesophyll conductance.ConclusionsThe study showed that a high planting density induced enhanced competition for nutrients (particularly for P and Mg) and that this competition is enhanced in soils from continuous plantations compared to soils from natural forests

CC-Riddle: A Question Answering Dataset of Chinese Character Riddles

The Chinese character riddle is a unique form of cultural entertainment specific to the Chinese language. It typically comprises two parts: the riddle description and the solution. The solution to the riddle is a single character, while the riddle description primarily describes the glyph of the solution, occasionally supplemented with its explanation and pronunciation. Solving Chinese character riddles is a challenging task that demands understanding of character glyph, general knowledge, and a grasp of figurative language. In this paper, we construct a \textbf{C}hinese \textbf{C}haracter riddle dataset named CC-Riddle, which covers the majority of common simplified Chinese characters. The construction process is a combination of web crawling, language model generation and manual filtering. In generation stage, we input the Chinese phonetic alphabet, glyph and meaning of the solution character into the generation model, which then produces multiple riddle descriptions. The generated riddles are then manually filtered and the final CC-Riddle dataset is composed of both human-written riddles and these filtered, generated riddles. In order to assess the performance of language models on the task of solving character riddles, we use retrieval-based, generative and multiple-choice QA strategies to test three language models: BERT, ChatGPT and ChatGLM. The test results reveal that current language models still struggle to solve Chinese character riddles. CC-Riddle is publicly available at \url{https://github.com/pku0xff/CC-Riddle}

Toward Optimized VR/AR Ergonomics: Modeling and Predicting User Neck Muscle Contraction

Ergonomic efficiency is essential to the mass and prolonged adoption of VR/AR experiences. While VR/AR head-mounted displays unlock users' natural wide-range head movements during viewing, their neck muscle comfort is inevitably compromised by the added hardware weight. Unfortunately, little quantitative knowledge for understanding and addressing such an issue is available so far. Leveraging electromyography devices, we measure, model, and predict VR users' neck muscle contraction levels (MCL) while they move their heads to interact with the virtual environment. Specifically, by learning from collected physiological data, we develop a bio-physically inspired computational model to predict neck MCL under diverse head kinematic states. Beyond quantifying the cumulative MCL of completed head movements, our model can also predict potential MCL requirements with target head poses only. A series of objective evaluations and user studies demonstrate its prediction accuracy and generality, as well as its ability in reducing users' neck discomfort by optimizing the layout of visual targets. We hope this research will motivate new ergonomic-centered designs for VR/AR and interactive graphics applications. Source code is released at: https://github.com/NYU-ICL/xr-ergonomics-neck-comfort.Comment: ACM SIGGRAPH 2023 Conference Proceeding

Investigating Zero- and Few-shot Generalization in Fact Verification

In this paper, we explore zero- and few-shot generalization for fact verification (FV), which aims to generalize the FV model trained on well-resourced domains (e.g., Wikipedia) to low-resourced domains that lack human annotations. To this end, we first construct a benchmark dataset collection which contains 11 FV datasets representing 6 domains. We conduct an empirical analysis of generalization across these FV datasets, finding that current models generalize poorly. Our analysis reveals that several factors affect generalization, including dataset size, length of evidence, and the type of claims. Finally, we show that two directions of work improve generalization: 1) incorporating domain knowledge via pretraining on specialized domains, and 2) automatically generating training data via claim generation.Comment: AACL-IJCNLP 2023 (main conference, long paper

ChatDoctor: A Medical Chat Model Fine-tuned on LLaMA Model using Medical Domain Knowledge

Recent large language models (LLMs) in the general domain, such as ChatGPT, have shown remarkable success in following instructions and producing human-like responses. However, such language models have yet to be adapted for the medical domain, resulting in poor accuracy of responses and an inability to provide sound advice on medical diagnoses, medications, etc. To address this problem, we fine-tuned our ChatDoctor model based on 100k real-world patient-physician conversations from an online medical consultation site. Besides, we add autonomous knowledge retrieval capabilities to our ChatDoctor, for example, Wikipedia or a disease database as a knowledge brain. By fine-tuning the LLMs using these 100k patient-physician conversations, our model showed significant improvements in understanding patients' needs and providing informed advice. The autonomous ChatDoctor model based on Wikipedia and Database Brain can access real-time and authoritative information and answer patient questions based on this information, significantly improving the accuracy of the model's responses, which shows extraordinary potential for the medical field with a low tolerance for error. To facilitate the further development of dialogue models in the medical field, we make available all source code, datasets, and model weights available at: https://github.com/Kent0n-Li/ChatDoctor

WGCN: Graph Convolutional Networks with Weighted Structural Features

Graph structural information such as topologies or connectivities provides valuable guidance for graph convolutional networks (GCNs) to learn nodes' representations. Existing GCN models that capture nodes' structural information weight in- and out-neighbors equally or differentiate in- and out-neighbors globally without considering nodes' local topologies. We observe that in- and out-neighbors contribute differently for nodes with different local topologies. To explore the directional structural information for different nodes, we propose a GCN model with weighted structural features, named WGCN. WGCN first captures nodes' structural fingerprints via a direction and degree aware Random Walk with Restart algorithm, where the walk is guided by both edge direction and nodes' in- and out-degrees. Then, the interactions between nodes' structural fingerprints are used as the weighted node structural features. To further capture nodes' high-order dependencies and graph geometry, WGCN embeds graphs into a latent space to obtain nodes' latent neighbors and geometrical relationships. Based on nodes' geometrical relationships in the latent space, WGCN differentiates latent, in-, and out-neighbors with an attention-based geometrical aggregation. Experiments on transductive node classification tasks show that WGCN outperforms the baseline models consistently by up to 17.07% in terms of accuracy on five benchmark datasets

Dechlorination of Chloral Hydrate by Pseudomonas putida LF54 which Possesses Biofilm Adhesin Protein LapA

Because of the lack of enzymes in critical steps of catabolic pathways, low-molecular-weight halogenated compounds are often recalcitrant to biodegradation. In our previous study, we isolated Pseudomonas sp. LF54 (LF54), the first bacterium that has been shown to use chloral hydrate (CH) as sole carbon source by an assimilation pathway in which dechlorination is the critical step. In this study, we identified a transposon (Tn) mutant that can render LF54 defective in CH dechlorination. The molecular characterization of Tn mutants revealed that the transposon insertion sites map to lapA. Sequence analyses verified the existence of lapA in LF54. Additionally, induced expression of lapA in the conditional lapA mutant of LF54 further verified that defective lapA expression renders LF54 defective in dechlorination. Recent studies have revealed that the largest cell-surface-associated protein LapA, a biofilm adhesin, is able to initiate biofilm formation. This function was also verified in the induced conditional lapA mutant and in LF54. Furthermore, we also found out that the defective lapA mutant rendered the variation of bacterial motility. LapA, the largest biofilm adhesin protein of P. putida, which influences CH dechlorination and flagella motility, is a novel discovery not previously reported

Effects of 4A Zeolite Additions on the Structure and Performance of LDPE Blend Microfiltration Membrane through Thermally Induced Phase Separation Method

Microfiltration membranes, 4A zeolite/LDPE, were prepared by blending low density polyethylene (LDPE) and4A zeolite through thermally induced phase separation (TIPS) process with diphenyl ether (DPE) as diluent. The effects of 4A zeolite loading on the pore structure and water permeation performance of the 4A zeolite/LDPE blend membranes were investigated. The incorporation of 4A zeolite particles greatly enhanced the connectivity of membrane pores, the pore size, and thus the water flux of 4A zeolite/LDPE blend membranes due to the gradually stronger DPE-zeolite affinity with the increase of the 4A zeolite loading. The water flux increased from 0 of LDPE control membrane to 87 L/m2h of 4A zeolite/LDPE blend membrane with 4A zeolite loading of 10 wt%. In addition, increasing the DPE content and cooling bath temperature is in favor of the water flux of 4A zeolite/LDPE blend membranes