Efficient Cross-Task Prompt Tuning for Few-Shot Conversational Emotion Recognition

Emotion Recognition in Conversation (ERC) has been widely studied due to its importance in developing emotion-aware empathetic machines. The rise of pre-trained language models (PLMs) has further pushed the limit of ERC performance. However, most recent works on ERC using PLMs are heavily data-driven, and requires fine-tuning the entire PLMs. To improve both sample and computational efficiency, we propose a derivative-free optimization method called Cross-Task Prompt Tuning (CTPT) for few-shot conversational emotion recognition. Unlike existing methods that learn independent knowledge from individual tasks, CTPT leverages sharable cross-task knowledge by exploiting external knowledge from other source tasks to improve learning performance under the few-shot setting. Moreover, CTPT only needs to optimize a vector under the low intrinsic dimensionality without gradient, which is highly parameter-efficient compared with existing approaches. Experiments on five different contextual conversation datasets demonstrate that our CTPT method has superior results on both few-shot scenarios and zero-shot transfers.Comment: Findings of EMNLP 202

Experimental Study on P-V diagram and motion curve of suction and discharge valve in reciprocating compressor

In this paper, through the establishment of the experiment test platform, the synchronous test of crankshaft rotation angle, cylinder pressure and valve motion of reciprocating refrigeration compressor is realized. And the factors affecting the performance of reciprocating refrigeration compressor and the performance of compressor are studied. The rotary encoder is used for monitoring transient pressure variation of suction and discharge valve port and internal cylinder during compressor running. And it can measure precisely the displacement of valve movement by optical fiber sensor. Through the study analysis on P-V diagrams under different conditions, compressor suction and discharge process of work and work loss are given to present relation between work and the refrigeration compressor condensing temperature. And also analyzing suction and discharge valve movement curve is to study the delay phenomenon while valve opening and closing, and present the most optimal proposal for valve plate structure design

Capturing Popularity Trends: A Simplistic Non-Personalized Approach for Enhanced Item Recommendation

Recommender systems have been gaining increasing research attention over the years. Most existing recommendation methods focus on capturing users' personalized preferences through historical user-item interactions, which may potentially violate user privacy. Additionally, these approaches often overlook the significance of the temporal fluctuation in item popularity that can sway users' decision-making. To bridge this gap, we propose Popularity-Aware Recommender (PARE), which makes non-personalized recommendations by predicting the items that will attain the highest popularity. PARE consists of four modules, each focusing on a different aspect: popularity history, temporal impact, periodic impact, and side information. Finally, an attention layer is leveraged to fuse the outputs of four modules. To our knowledge, this is the first work to explicitly model item popularity in recommendation systems. Extensive experiments show that PARE performs on par or even better than sophisticated state-of-the-art recommendation methods. Since PARE prioritizes item popularity over personalized user preferences, it can enhance existing recommendation methods as a complementary component. Our experiments demonstrate that integrating PARE with existing recommendation methods significantly surpasses the performance of standalone models, highlighting PARE's potential as a complement to existing recommendation methods. Furthermore, the simplicity of PARE makes it immensely practical for industrial applications and a valuable baseline for future research.Comment: 9 pages, 5 figure

Are ID Embeddings Necessary? Whitening Pre-trained Text Embeddings for Effective Sequential Recommendation

Recent sequential recommendation models have combined pre-trained text embeddings of items with item ID embeddings to achieve superior recommendation performance. Despite their effectiveness, the expressive power of text features in these models remains largely unexplored. While most existing models emphasize the importance of ID embeddings in recommendations, our study takes a step further by studying sequential recommendation models that only rely on text features and do not necessitate ID embeddings. Upon examining pretrained text embeddings experimentally, we discover that they reside in an anisotropic semantic space, with an average cosine similarity of over 0.8 between items. We also demonstrate that this anisotropic nature hinders recommendation models from effectively differentiating between item representations and leads to degenerated performance. To address this issue, we propose to employ a pre-processing step known as whitening transformation, which transforms the anisotropic text feature distribution into an isotropic Gaussian distribution. Our experiments show that whitening pre-trained text embeddings in the sequential model can significantly improve recommendation performance. However, the full whitening operation might break the potential manifold of items with similar text semantics. To preserve the original semantics while benefiting from the isotropy of the whitened text features, we introduce WhitenRec+, an ensemble approach that leverages both fully whitened and relaxed whitened item representations for effective recommendations. We further discuss and analyze the benefits of our design through experiments and proofs. Experimental results on three public benchmark datasets demonstrate that WhitenRec+ outperforms state-of-the-art methods for sequential recommendation

Fusarium Graminearum Growth Inhibition Due to Glucose Starvation Caused by Osthol

The effects of osthol, a plant coumarin, on morphology, sugar uptake and cell wall components of Fusarium graminearum were examined in vitro by electron microscopy,14C-labelling and enzyme activity detection. The results revealed that osthol could inhibit the hypha growth of F. graminearum by decreasing hyphal absorption to reducing sugar. After treatment with 100 μg·mL−1 osthol for 24 h, many hyphal fragments of F. graminearum appeared. Microscopy observation showed that the cell walls of hyphal fragments blurred and the organelles of the cells degraded with the increasing vacuoles. The N-acetyl-D-glucosamine contents and chitinase activity both increased when hypha were treated with 100 μg·mL−1 osthol, whereas the activity of β-1,6-glucanase remained unchanged. When F. graminearum fed with 14C glucose was treated with 100 μg·mL−1osthol, glucose contents decreased to the lowest level, while the contents in non-osthol treated controls remained unchanged. These results suggested that chitinase activity might be related to glucose starvation under osthol treatment, and that the appearance of hyphae fragments maybe the results of the promoted chitinase activity which itself triggered chitin degradation