Text Coherence Analysis Based on Deep Neural Network

In this paper, we propose a novel deep coherence model (DCM) using a convolutional neural network architecture to capture the text coherence. The text coherence problem is investigated with a new perspective of learning sentence distributional representation and text coherence modeling simultaneously. In particular, the model captures the interactions between sentences by computing the similarities of their distributional representations. Further, it can be easily trained in an end-to-end fashion. The proposed model is evaluated on a standard Sentence Ordering task. The experimental results demonstrate its effectiveness and promise in coherence assessment showing a significant improvement over the state-of-the-art by a wide margin.Comment: 4 pages, 2 figures, CIKM 201

Pathways to sustainable grassland development in China: findings of three case studies

Grassland development serves as an important part of the national sustainable development strategy in China. This paper defines the strategic objectives of grassland development in China based on the national strategy, the current status of grassland development in China and the status of grassland development internationally. As China is at a transformational stage of implementing an ecological economic system in grassland development, top priorities should be given to enhance the values of grassland ecosystem services, reduce the pressures on the grasslands, and restructure the grassland industry. Case studies on three pasture areas in Sichuan and Inner Mongolia, which have distinct ecological and climatic features and are at different development stages, revealed that the core issue for sustainable development of grassland in China is in addressing the conflict between the people and grasslands. Improving the social security system and enhancing the capacity of the herders in implementing sustainable development are the recommended pathways for sustainable grassland development in China.Die Entwicklung von Steppengebieten ist ein wichtiges Element der Nachhaltigkeitsstrategie in der VR China. Dieses Papier erläutert die strategischen Ziele der Steppengebietsentwicklung im Rahmen der Strategie zur nachhaltigen Entwicklung in China und bettet diese in die internationalen Entwicklungen der Steppengebietspolitik ein. Zur Lösung der Armutsfalle der Herdenbesitzer in Steppengebieten wird eine Transformationsstrategie der integrierten ökologischen, ökonomischen und sozialen Nachhaltigkeit vorgeschlagen und programmatisch ausformuliert. Die Bewertung und Honorierung von Ökosystemdienstleistungen spielt darin eine zentrale Rolle wie auch die Verbesserung der Sozialversicherungssysteme für ländliche Räume. Drei Fallbeispiele aus Sichuan und der Inneren Mongolei zeigen die Machbarkeit und die verschiedenen Stufen dieser Strategie unter sehr unterschiedlichen klimatischen und sozioökonomischen Entwicklungsbedingungen

Auffusion: Leveraging the Power of Diffusion and Large Language Models for Text-to-Audio Generation

Recent advancements in diffusion models and large language models (LLMs) have significantly propelled the field of AIGC. Text-to-Audio (TTA), a burgeoning AIGC application designed to generate audio from natural language prompts, is attracting increasing attention. However, existing TTA studies often struggle with generation quality and text-audio alignment, especially for complex textual inputs. Drawing inspiration from state-of-the-art Text-to-Image (T2I) diffusion models, we introduce Auffusion, a TTA system adapting T2I model frameworks to TTA task, by effectively leveraging their inherent generative strengths and precise cross-modal alignment. Our objective and subjective evaluations demonstrate that Auffusion surpasses previous TTA approaches using limited data and computational resource. Furthermore, previous studies in T2I recognizes the significant impact of encoder choice on cross-modal alignment, like fine-grained details and object bindings, while similar evaluation is lacking in prior TTA works. Through comprehensive ablation studies and innovative cross-attention map visualizations, we provide insightful assessments of text-audio alignment in TTA. Our findings reveal Auffusion's superior capability in generating audios that accurately match textual descriptions, which further demonstrated in several related tasks, such as audio style transfer, inpainting and other manipulations. Our implementation and demos are available at https://auffusion.github.io.Comment: Demo and implementation at https://auffusion.github.i