Back to village - a proposal for conservation guidelines and masterplan pilot project of Youshan village

LAUREA MAGISTRALEQuesta tesi si concentra sui villaggi tradizionali di Wuyuan che sono stati ignorati negli ultimi decenni, e prende come esempio il villaggio di Youshan. Sulla base delle potenzialità di questi villaggi nell'offrire nuovi stili di vita per la società moderna, riscontrate dall'autore in precedenti studi sul sito, l'argomento di questa tesi è confermato. Da un lato, l'importanza dei valori storici e culturali dei villaggi tradizionali dovrebbe essere preservata e sviluppata; dall’altro, si dovrebbe esplorare il potenziale di questi villaggi per adattarsi alle società moderne. Per raggiungere questi due obiettivi generali, sono essenziali approcci e passaggi scientifici: In questa tesi sono contenute tre fasi: ricerca del sito, proposta di linee guida e progettazione del progetto pilota. La ricerca sul sito conclude la ricerca territoriale, lo studio sullo stato attuale del villaggio e soprattutto la ricerca storica. Successivamente viene avanzata la proposta di linee guida per la conservazione e il riuso e vengono forniti suggerimenti su tre livelli: paesaggio urbano, paesaggio stradale ed edificio. Infine, viene elaborato un progetto pilota, seguendo le linee guida precedenti. Gli approcci utilizzati in questa tesi includono la lettura e la conclusione della biografia, studi di casi, la lettura e la conclusione di documenti ufficiali, la raccolta e l'analisi dei dati. In conclusione, questa tesi introduce la storia e le caratteristiche del villaggio Youshan, analizza le sue condizioni, compresi vantaggi e svantaggi, e infine propone una corretta linea guida di conservazione offrendo allo stesso tempo un progetto pilota.This thesis focuses on the traditional villages of Wuyuan which were ignored during the last decades, and picks Youshan Village as a sample. Based on the potential of these villages to offer a new lifestyle for modern society which the author found in previous site studies, the topic of this thesis is confirmed. On the one hand, the importance of traditional villages' historical and cultural values should be preserved and developed; in another hand, the potential for these villages to adapt to modern societies should be explored. To achieve these two general goals, scientific approaches and steps are essential: Three steps are contained in this thesis: site research, guideline proposal, and pilot project design. The site research concludes territorial research, village current condition study, and especially history research. Then the conservation and reuse guideline proposal is raised and suggestions are given on three levels: urbanscape, streetscape, and building. Finally, a pilot project is designed, following the previous guidelines. Approaches used in this thesis include biography reading and conclusion, case studies, official documents reading and conclusion, data collection, and analysis. In conclusion, this thesis introduces the history and characteristics of Youshan village, analyzes its conditions, including advantages and disadvantages, and at last, proposes a proper conservation guideline while offering a pilot project

Non-Visible Light Data Synthesis and Application: A Case Study for Synthetic Aperture Radar Imagery

We explore the "hidden" ability of large-scale pre-trained image generation models, such as Stable Diffusion and Imagen, in non-visible light domains, taking Synthetic Aperture Radar (SAR) data for a case study. Due to the inherent challenges in capturing satellite data, acquiring ample SAR training samples is infeasible. For instance, for a particular category of ship in the open sea, we can collect only few-shot SAR images which are too limited to derive effective ship recognition models. If large-scale models pre-trained with regular images can be adapted to generating novel SAR images, the problem is solved. In preliminary study, we found that fine-tuning these models with few-shot SAR images is not working, as the models can not capture the two primary differences between SAR and regular images: structure and modality. To address this, we propose a 2-stage low-rank adaptation method, and we call it 2LoRA. In the first stage, the model is adapted using aerial-view regular image data (whose structure matches SAR), followed by the second stage where the base model from the first stage is further adapted using SAR modality data. Particularly in the second stage, we introduce a novel prototype LoRA (pLoRA), as an improved version of 2LoRA, to resolve the class imbalance problem in SAR datasets. For evaluation, we employ the resulting generation model to synthesize additional SAR data. This augmentation, when integrated into the training process of SAR classification as well as segmentation models, yields notably improved performance for minor classe

Large Convolutional Model Tuning via Filter Subspace

Efficient fine-tuning methods are critical to address the high computational and parameter complexity while adapting large pre-trained models to downstream tasks. Our study is inspired by prior research that represents each convolution filter as a linear combination of a small set of filter subspace elements, referred to as filter atoms. In this paper, we propose to fine-tune pre-trained models by adjusting only filter atoms, which are responsible for spatial-only convolution, while preserving spatially-invariant channel combination knowledge in atom coefficients. In this way, we bring a new filter subspace view for model tuning. Furthermore, each filter atom can be recursively decomposed as a combination of another set of atoms, which naturally expands the number of tunable parameters in the filter subspace. By only adapting filter atoms constructed by a small number of parameters, while maintaining the rest of model parameters constant, the proposed approach is highly parameter-efficient. It effectively preserves the capabilities of pre-trained models and prevents overfitting to downstream tasks. Extensive experiments show that such a simple scheme surpasses previous tuning baselines for both discriminate and generative tasks

XplainLLM: A QA Explanation Dataset for Understanding LLM Decision-Making

Large Language Models (LLMs) have recently made impressive strides in natural language understanding tasks. Despite their remarkable performance, understanding their decision-making process remains a big challenge. In this paper, we look into bringing some transparency to this process by introducing a new explanation dataset for question answering (QA) tasks that integrates knowledge graphs (KGs) in a novel way. Our dataset includes 12,102 question-answer-explanation (QAE) triples. Each explanation in the dataset links the LLM's reasoning to entities and relations in the KGs. The explanation component includes a why-choose explanation, a why-not-choose explanation, and a set of reason-elements that underlie the LLM's decision. We leverage KGs and graph attention networks (GAT) to find the reason-elements and transform them into why-choose and why-not-choose explanations that are comprehensible to humans. Through quantitative and qualitative evaluations, we demonstrate the potential of our dataset to improve the in-context learning of LLMs, and enhance their interpretability and explainability. Our work contributes to the field of explainable AI by enabling a deeper understanding of the LLMs decision-making process to make them more transparent and thereby, potentially more reliable, to researchers and practitioners alike. Our dataset is available at: https://github.com/chen-zichen/XplainLLM_dataset.gitComment: 17 pages, 6 figures, 7 tables. Our dataset is available at: https://github.com/chen-zichen/XplainLLM_dataset.gi

LMExplainer: a Knowledge-Enhanced Explainer for Language Models

Large language models (LMs) such as GPT-4 are very powerful and can process different kinds of natural language processing (NLP) tasks. However, it can be difficult to interpret the results due to the multi-layer nonlinear model structure and millions of parameters. Lack of understanding of how the model works can make the model unreliable and dangerous for everyday users in real-world scenarios. Most recent works exploit the weights of attention to provide explanations for model predictions. However, pure attention-based explanation is unable to support the growing complexity of the models, and cannot reason about their decision-making processes. Thus, we propose LMExplainer, a knowledge-enhanced interpretation module for language models that can provide human-understandable explanations. We use a knowledge graph (KG) and a graph attention neural network to extract the key decision signals of the LM. We further explore whether interpretation can also help AI understand the task better. Our experimental results show that LMExplainer outperforms existing LM+KG methods on CommonsenseQA and OpenBookQA. We also compare the explanation results with generated explanation methods and human-annotated results. The comparison shows our method can provide more comprehensive and clearer explanations. LMExplainer demonstrates the potential to enhance model performance and furnish explanations for the reasoning processes of models in natural language

Desenvolvimento sustentável de setores emergentes quanto a propriedade intelectual diante da globalização econômica e da Internet das Coisas

This study proposes a method to enhance agricultural production efficiency in terms of intellectual property (IP) through smart agricultural practices. First, this study analyzes the key evaluation indicators of IoT in agriculture. A sustainability model and evaluation indicator system are designed using the driver-state-response framework. The principal component analysis algorithm is then used to assign values to the indicators. Finally, the sustainability level scores of the Internet of Agriculture (IoA) model of IP emerging industries in each municipality within the province are calculated, and different strategies are proposed based on thesescores. These results provide specific data support for the sustainable development of IP emerging industries under economic globalization.Este artículo propone un método de mejora de la producción agrícola de las industrias emergentes en términos de propiedad intelectual, a través de prácticas agrícolas inteligentes. En primer lugar, este artículo analiza los indicadores clave de evaluación del internet de las cosas en la agricultura. El modelo de sostenibilidad y el sistema de indicadores de evaluación se diseñan con el marco conductor-estado-respuesta. Por último, se calculan las puntuaciones del nivel de sostenibilidad del modelo de internet de la agricultura (IoA) de las industrias emergentes de propiedad intelectual en cada municipio de la provincia. Los resultados de la investigación aportan datos concretos que respaldan el desarrollo sostenible de las industrias emergentes de propiedad intelectual en el marco de la globalización económica. Este artigo propõe um método para aprimorar a produção agrícola sob a perspectiva da produção agrícola inteligente para melhorar a eficiência dessa área em indústrias emergentes em propriedade intelectual. Em primeiro lugar, este artigo analisa os principais indicadores de avaliação da Internet das Coisas na agricultura. O modelo de sustentabilidade e o sistema de indicadores de avaliação são projetados com a estrutura de resposta ao estado do condutor. Em seguida, o indicador é atribuído a um valor usando um algoritmo de análise de componentes principais. Por fim, são calculadas as pontuações do nível de sustentabilidade do modelo da Internet da Agricultura (IoA) de indústrias emergentes em propriedade intelectual em cada município da província. Os resultados da pesquisa fornecem suporte de dados específicos para o desenvolvimento sustentável de indústrias emergentes em propriedade intelectual sob a globalização econômica

Field Study: How Are Vulnerable Children in China Developing Through Sport‐Based Social Projects?

According to a UNICEF report, there are 65.17 million children living in poverty‐stricken areas of China, accounting for 21.9% of the national child population. Authorities focus on economic aid and basic safety protection for vulnerable children but lack support in psychological, emotional, and social areas. While international scholars have recognized sports‐based social projects (SBSPs) as an effective tool for promoting child development, there is limited research on the role of SBSPs in advancing vulnerable children's development in China. To provide empirical data on the outcomes of SBSPs in China and discuss their mechanisms and conditions, the author conducted a field study of a project called "Angel" in the suburbs of Beijing. Through 101 hours of observation and 17 hours of in‐depth interviews, the thematic analysis revealed five core themes: initial backgrounds, developmental challenges, collective life, sport activities, and growth. The study found that these children, with backgrounds of poor education, isolation, and poverty, exhibited Developmental Challenges such as weak social skills, cognitive limitations, and low psychological capital. However, through collective life, social interactions, educational management, independent living experiences, and sports opportunities, they showed improvements in responsibility, social skills, and optimism. The study also explored the fulfillment of basic psychological needs in sports and collective life, offering theoretical support for the role of SBSPs in promoting child development