A Comparative Study of Grade 12 Students’ Use of Direct and Indirect Second Language Learning Strategies According to Their Gender and Classes at Luchuan High School, Guangxi, China

This research mainly focused on the Chinese Grade 12 students’ use of second language learning strategies during the academic year 2016-2017, at Luchuan High school, Guangxi, China. A total of 120 students from grade 12 in this study. The data were collected based on the questionnaire of learning strategies for English learning. The study found that Grade 12 students used both direct strategies and indirect strategies for their English learning at a medium level; the use of overall learning strategies was also at a medium level. There was no significantly difference of students' use of second language learning strategies according to their gender, the direct strategies and indirect strategies. There is a significant difference of students' use of second language learning strategies according to their classes

Responses of Terrestrial Biogeochemical Cycles to Global Change – Syntheses and Data-Model Integration

Global observations and model simulations show that atmospheric carbon dioxide (CO2) concentrations and surface temperatures have been and will keep increasing. These environmental changes have significant influences on terrestrial biogeochemical cycles. On the other way, how changes in terrestrial biogeochemistry in response to the environmental changes can either amplify or alleviate climate change. Soils, the primary research subject of this dissertation, store more than twice as much carbon (C) as the atmosphere. As such, small changes in soil C may have large impacts on the magnitude of atmospheric CO2 concentrations and therefore climate change. However, due to the huge storage and relatively long residence time, how soil C responds to increasing atmospheric CO2 concentrations and surface temperatures is still unclear. The unclear response of soil C is one of the most important reasons for the uncertainties of the magnitude of global change in this century. In this dissertation, I attempted to study the responses of soil C and related biogeochemical processes to increased temperature and CO2 concentrations, through syntheses and data-model integration. In the first study, I estimated the responses of two critical soil C dynamic processes, replenishment and priming effect, to increased C input. With the responses of the two processes, I estimated the net change of soil organic C by increased C input. Results show that approximately 58% of newly added C is transferred into soil organic C (SOC) via replenishment, whereas the additional loss of old SOC due to priming effect only accounts for 8.4% of the added new C in the first year after a one-time new C input. As a result, the new C input leads to a net increase in SOC, ranging from 40% to 49% of the added new C. The magnitude of the net increase in SOC is positively correlated with the nitrogen-to-C ratio of the added substrates. Furthermore, a 100-year modeling experiment confirms that an increase in new C input leads to significant SOC accumulation over time. The findings suggest that increasing plant productivity and the consequent increase in C input to soils likely promote SOC storage despite the enhanced decomposition of old C, potentially mitigating further climate change. The first study evaluated impacts of C input on soil C dynamics. My second study evaluated how nitrogen (N) regulates C input under elevated CO2. A popular hypothesis of the N constraint to the CO2 fertilization effect is progressive N limitation (PNL), which postulates that the stimulation of plant growth by CO2 enrichment results in more N sequestered in plant, litter and soil organic matter (SOM) so that, the N availability for plant growth progressively declines in soils over time. The reduced N availability then in turn constrains the further CO2 fertilization effect on plant growth over longer time scales. Although extensive research has explored whether or not PNL occurs under CO2 enrichment, a comprehensive assessment of the N processes that regulate PNL is still lacking. In the second study, I quantitatively synthesized the responses of all major processes and pools in the terrestrial N cycle with meta-analysis of CO2 experimental data available in the literature. The results showed that CO2 enrichment significantly increased N sequestration in the plant and litter pools but not in the soil pool, partially supporting one of the basic assumptions in the PNL hypothesis that elevated CO2 results in more N sequestered in organic pools. However, CO2 enrichment significantly increased the N influx via biological N fixation and the loss via nitrous oxide (N2O) emission, but decreased the N efflux via leaching. In addition, no general diminution was observed in effects of CO2 fertilization on plant growth. Overall, the analyses suggest that the extra N supply by the increased biological N fixation and decreased leaching may potentially alleviate PNL under elevated CO2 conditions in spite of the increases in plant N sequestration and N2O emission. Moreover, the syntheses indicate that CO2 enrichment increases soil ammonium (NH4+) to nitrate (NO3-) ratio. The changed NH4+/NO3- ratio and subsequent biological processes may result in changes in soil microenvironments, above-belowground community structures and associated interactions, which could potentially affect the terrestrial biogeochemical cycles. In addition, the data synthesis suggests that more long-term studies, especially in regions other than temperate ones, are needed for comprehensive assessments of the PNL hypothesis. In the third study, I evaluated methods for estimating the temperature sensitivity (Q10) of SOC decomposition since the Q10 estimate substantially depends on their specific assumptions. I compared several commonly used methods (i.e., one-pool (1P) model, two-discrete-pool (2P) model, three-discrete-pool (3P) model, and time-for-substrate (T4S) Q10 method) plus a new and more process-oriented approach for estimating Q10 of SOC decomposition from laboratory incubation data. The process-oriented approach is a three-transfer-pool (3PX) model that resembles the decomposition sub-model commonly used in Earth system models. The estimated Q10s generally increased with the soil recalcitrance, but decreased with the incubation temperature increase. The results indicated that the 1P model did not adequately simulate the dynamics of SOC decomposition and thus was not adequate for the Q10 estimation. All the multi-pool models fitted the soil incubation data well. The Akaike information criterion (AIC) analysis suggested that the 2P model is the most parsimonious. As the incubation progressed, Q10 estimated by the 3PX model was smaller than those by the 2P and 3P models because the continuous C transfers from the slow and passive pools to the active pool were included in the 3PX model. Although the T4S method could estimate the Q10 of labile carbon appropriately, the analyses showed that it overestimated that of recalcitrant SOM. The similar structure of 3PX model with the decomposition sub-model of Earth system models provides a possible approach, via the data assimilation techniques, to incorporate results from numerous incubation experiments into Earth system models. In the fourth study, I studied how warming affect SOC storage in Alaskan tundra. By combining a process-based model and a unique field experiment, this study shows that warming reduced the base turnover rate of SOC, which is the representation of unresolved microbial community and activity on the resolved scale. The reduced base turnover rate of SOC suggests that microbial decomposers acclimate to warming in Alaskan tundra. Although warming still accelerates SOC loss, the acclimation counterbalances the SOC loss acceleration by 62%. Our study suggests that it is critical to incorporate changes in biological properties (as parameters) to improve the model performance in predicting C dynamics and its feedback to climate change. This dissertation demonstrates that integrating data and model can advance our understanding of biogeochemical cycles in the context of global change. Future research is needed to study the integrated effect of global change factors on the responses and feedbacks of biogeochemical cycles to global change

Provable Guarantees for Neural Networks via Gradient Feature Learning

Neural networks have achieved remarkable empirical performance, while the current theoretical analysis is not adequate for understanding their success, e.g., the Neural Tangent Kernel approach fails to capture their key feature learning ability, while recent analyses on feature learning are typically problem-specific. This work proposes a unified analysis framework for two-layer networks trained by gradient descent. The framework is centered around the principle of feature learning from gradients, and its effectiveness is demonstrated by applications in several prototypical problems, such as mixtures of Gaussians and parity functions. The framework also sheds light on interesting network learning phenomena such as feature learning beyond kernels and the lottery ticket hypothesis.Comment: NeurIPS 2023, 71 page

Optimal Monotone Mean-Variance Problem in a Catastrophe Insurance Model

This paper explores an optimal investment and reinsurance problem involving both ordinary and catastrophe insurance businesses. The catastrophic events are modeled as following a compound Poisson process, impacting the ordinary insurance business. The claim intensity for the ordinary insurance business is described using a Cox process with a shot-noise intensity, the jump of which is proportional to the size of the catastrophe event. This intensity increases when a catastrophe occurs and then decays over time. The insurer's objective is to maximize their terminal wealth under the Monotone Mean-Variance (MMV) criterion. In contrast to the classical Mean-Variance (MV) criterion, the MMV criterion is monotonic across its entire domain, aligning better with fundamental economic principles. We first formulate the original MMV optimization problem as an auxiliary zero-sum game. Through solving the Hamilton-Jacobi-Bellman-Isaacs (HJBI) equation, explicit forms of the value function and optimal strategies are obtained. Additionally, we provides the efficient frontier within the MMV criterion. Several numerical examples are presented to demonstrate the practical implications of the results

Towards Few-Shot Adaptation of Foundation Models via Multitask Finetuning

Foundation models have emerged as a powerful tool for many AI problems. Despite the tremendous success of foundation models, effective adaptation to new tasks, particularly those with limited labels, remains an open question and lacks theoretical understanding. An emerging solution with recent success in vision and NLP involves finetuning a foundation model on a selection of relevant tasks, before its adaptation to a target task with limited labeled samples. In this paper, we study the theoretical justification of this multitask finetuning approach. Our theoretical analysis reveals that with a diverse set of related tasks, this multitask finetuning leads to reduced error in the target task, in comparison to directly adapting the same pretrained model. We quantify the relationship between finetuning tasks and target tasks by diversity and consistency metrics, and further propose a practical task selection algorithm. We substantiate our theoretical claims with extensive empirical evidence. Further, we present results affirming our task selection algorithm adeptly chooses related finetuning tasks, providing advantages to the model performance on target tasks. We believe our study shed new light on the effective adaptation of foundation models to new tasks that lack abundant labels. Our code is available at https://github.com/OliverXUZY/Foudation-Model_Multitask.Comment: Published at ICLR 2024. 54 page

EVE: Efficient Vision-Language Pre-training with Masked Prediction and Modality-Aware MoE

Building scalable vision-language models to learn from diverse, multimodal data remains an open challenge. In this paper, we introduce an Efficient Vision-languagE foundation model, namely EVE, which is one unified multimodal Transformer pre-trained solely by one unified pre-training task. Specifically, EVE encodes both vision and language within a shared Transformer network integrated with modality-aware sparse Mixture-of-Experts (MoE) modules, which capture modality-specific information by selectively switching to different experts. To unify pre-training tasks of vision and language, EVE performs masked signal modeling on image-text pairs to reconstruct masked signals, i.e., image pixels and text tokens, given visible signals. This simple yet effective pre-training objective accelerates training by 3.5x compared to the model pre-trained with Image-Text Contrastive and Image-Text Matching losses. Owing to the combination of the unified architecture and pre-training task, EVE is easy to scale up, enabling better downstream performance with fewer resources and faster training speed. Despite its simplicity, EVE achieves state-of-the-art performance on various vision-language downstream tasks, including visual question answering, visual reasoning, and image-text retrieval.Comment: Accepted by AAAI 202