This paper explores new frontiers in agricultural natural language processing (NLP) by investigating the effectiveness of food-related text corpora for pretraining transformer-based language models. Specifically, we focus on semantic matching, establishing mappings between food descriptions and nutrition data through fine-tuning AgriBERT with the FoodOn ontology. Our work introduces an expanded comparison with state-of-the-art language models such as GPT-4, Mistral-large, Claude 3 Sonnet, and Gemini 1.0 Ultra. This exploratory investigation, rather than a direct comparison, aims to understand how AgriBERT, a domain-specific, fine-tuned, open-source model, complements the broad knowledge and generative abilities of these advanced LLMs in addressing the unique challenges of the agricultural sector. We also experiment with other applications, such as cuisine prediction from ingredients, expanding our research to include various NLP tasks beyond semantic matching. Overall, this paper underscores the potential of integrating domain-specific models like AgriBERT with advanced LLMs to enhance the performance and applicability of agricultural NLP applications.

Pretraining domain-specific language models remains an important challenge which limits their applicability in various areas such as agriculture. This paper investigates the effectiveness of leveraging food related text corpora (e.g., food and agricultural literature) in pretraining transformer-based language models. We evaluate our trained language model, called AgriBERT, on the task of semantic matching, i.e., establishing mapping between food descriptions and nutrition data, which is a long-standing challenge in the agricultural domain. In particular, we formulate the task as an answer selection problem, fine-tune the trained language model with the help of an external source of knowledge (e.g., FoodOn ontology), and establish a baseline for this task. The experimental results reveal that our language model substantially outperforms other language models and baselines in the task of matching food description and nutrition.

Remote-sensing images serve as key data sources which play a crucial role in recording the target information of ground features. Due to the limitations of the existing imaging equipment, environments, and transmission conditions, the obtained remote-sensing images are usually contaminated by noise in real-world scenarios. To address this problem, we propose a dual-complementary convolution network (DCCNet), including structural and detailed subnetwork, for repairing the structure and details of noisy remote-sensing images. More specifically, they generate multiresolution inputs via discrete wavelet transform and shuffling operation, respectively. Since the convolution operation is imposed on low-resolution inputs, the network parameters are considerably reduced. Experimental evaluations demonstrate that our proposed network exhibits superior performance to other competing methods in remote-sensing public datasets. The code of the DCCNet is available athttps://github.com/20155104009/DCCNet.

With the growing number of audio excerpts through various media and distribution channels, advanced audio analysis approaches have received significant interest in the multimedia field. However, current audio analysis approaches are still far from satisfactory due to the semantic gaps between the low-level acoustic features and high-level semantics perceived by human brain. In order to alleviate the problem, this paper propose a novel computational framework to bridge acoustic features with high-level semantic features derived from functional magnetic resonance imaging (fMRI) signals which record the brain's response during free listening to music/speech excerpts, and to explore the brain auditory network composition of acoustic features for different types of music/speech excerpts. Specifically, we identify meaningful brain networks and corresponding brain activities representing high-level semantic features via a novel group-wise sparse representation of whole brain fMRI signals. Then we associate the brain activities with specific low-level acoustic features and analyze the auditory network composition of acoustic features for different types of music/speech excerpts. Experimental results demonstrate that multiple acoustic features are involved in the brain auditory networks during free listening to music/speech excerpts. Meanwhile, there is considerable variability of auditory network composition of acoustic features for different types of music/speech. Our results provide new insights of how to narrow the semantic gaps in audio content analysis.

Task-based fMRI (tfMRI) has been widely used to explore functional brain networks via predefined stimulus paradigm in the fMRI scan. Traditionally, the general linear model (GLM) has been a dominant approach to detect task-evoked networks. However, GLM focuses on task-evoked or event-evoked brain responses and possibly ignores the intrinsic brain functions. In comparison, dictionary learning and sparse coding methods have attracted much attention recently, and these methods have shown the promise of automatically and systematically decomposing fMRI signals into meaningful task-evoked and intrinsic concurrent networks. Nevertheless, two notable limitations of current data-driven dictionary learning method are that the prior knowledge of task paradigm is not sufficiently utilized and that the establishment of correspondences among dictionary atoms in different brains have been challenging. In this paper, we propose a novel supervised dictionary learning and sparse coding method for inferring functional networks from tfMRI data, which takes both of the advantages of model-driven method and data-driven method. The basic idea is to fix the task stimulus curves as predefined model-driven dictionary atoms and only optimize the other portion of data-driven dictionary atoms. Application of this novel methodology on the publicly available human connectome project (HCP) tfMRI datasets has achieved promising results.