Improved quantification of forest cover change and implications for the carbon cycle

Changes in forest cover significantly affect the global carbon cycle, the hydrological cycle and biodiversity richness. This dissertation explores the potential of satellite-derived land cover datasets in quantifying changes in global forest cover and carbon stock. The research involved the following three components: 1) improving forest cover characterization, 2) developing advanced methods for detecting forest cover change (FCC) and 3) estimating the amount and trend of forest carbon change. The first component sought to improve global forest cover characterization through data fusion. Multiple global land cover maps have been generated, which collectively represent our current best knowledge of global land cover, but substantial discrepancies were found in their depiction of forest. I demonstrated that the extent and density of forest cover could be much better characterized by integrating existing datasets. However, these independent map products cannot be directly compared to quantify FCC, because post-classification change detection requires significant consistency in land cover definition, satellite data source and classification procedure. The yearly vegetation continuous field (VCF) product derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) provides a prototype that fulfills such requirement. The second component was intended to explore the features of this time series dataset in change analysis. A new algorithm called VCF-based Change Analysis was developed that can explicitly characterize the timing and intensity of FCC. The efficiency and robustness of this algorithm stem from two realistic assumptions—the spatial rarity and the temporal continuity of land cover change/modification. The developed method was applied to continental scales for mapping forest disturbance hotspots. The third component of the research combined MODIS-based deforestation indicators, a Landsat sample and a biomass dataset to estimate annual carbon emissions from deforestation with a regional focus on the Amazon basin. I found that deforestation emissions varied considerably not only across regions but also from year to year. Moreover, deforestation has been progressively encroaching into higher biomass lands in the Amazon interior. These observed deforestation and emission dynamics are expected to provide scientific support to policies on reducing emissions from deforestation and forest degradation (REDD+). The generated panel data are also of great value for evaluating forest protection policies

PMET: Precise Model Editing in a Transformer

Model editing techniques modify a minor proportion of knowledge in Large Language Models (LLMs) at a relatively low cost, which have demonstrated notable success. Existing methods assume Transformer Layer (TL) hidden states are values of key-value memories of the Feed-Forward Network (FFN). They usually optimize the TL hidden states to memorize target knowledge and use it to update the weights of the FFN in LLMs. However, the information flow of TL hidden states comes from three parts: Multi-Head Self-Attention (MHSA), FFN, and residual connections. Existing methods neglect the fact that the TL hidden states contains information not specifically required for FFN. Consequently, the performance of model editing decreases. To achieve more precise model editing, we analyze hidden states of MHSA and FFN, finding that MHSA encodes certain general knowledge extraction patterns. This implies that MHSA weights do not require updating when new knowledge is introduced. Based on above findings, we introduce PMET, which simultaneously optimizes Transformer Component (TC, namely MHSA and FFN) hidden states, while only using the optimized TC hidden states of FFN to precisely update FFN weights. Our experiments demonstrate that PMET exhibits state-of-the-art performance on both the COUNTERFACT and zsRE datasets. Our ablation experiments substantiate the effectiveness of our enhancements, further reinforcing the finding that the MHSA encodes certain general knowledge extraction patterns and indicating its storage of a small amount of factual knowledge. Our code is available at https://github.com/xpq-tech/PMET.git.Comment: Preprint. Under revie

Serum Oxytocin Levels and an Oxytocin Receptor Gene Polymorphism (rs2254298) Indicate Social Deficits in Children and Adolescents with Autism Spectrum Disorders

The neuropeptide oxytocin (OT) and its receptor (OXTR) have been predicted to be involved in the regulation of social functioning in autism spectrum disorders (ASD). Objective of the study was to investigate serum OT levels and the OXTR rs2254298 polymorphism in Chinese Han children and adolescents with ASD as well as to identify their social deficits relevant to the oxytocinergic system. We tested serum OT levels using ELISA in 55 ASD subjects and 110 typically developing (TD) controls as well as genotyped the OXTR rs2254298 polymorphism using PCR-RFLP in 100 ASD subjects and 232 TD controls. Autistic symptoms were assessed by the Autism Behavior Checklist (ABC) and the Childhood Autism Rating Scale (CARS). There were no significant associations between OXTR rs2254298 polymorphism and ASD, serum OT levels and age, as well as serum OT levels and intelligent quotient (IQ) in both ASD and TD groups. However, ASD subjects exhibited elevated serum OT levels compared to TD controls and positive correlations between serum OT levels and adaptation to change score in the CARS and CARS total scores. Moreover, in the ASD group, significant relationships were revealed between the single-nucleotide polymorphism (SNP) rs2254298 and serum OT levels, the category stereotypes and object use in the ABC and the category adaptation to change in the CARS. These findings indicated that individuals with ASD may exhibit a dysregulation in OT on the basis of changes in OXTR gene expression as well as environmentally induced alterations of the oxytocinergic system to determine their social deficits