Investigating public attitudes on social media is important in opinion mining systems. Stance detection aims to analyze the attitude of an opinionated text (e.g., favor, neutral, or against) toward a given target. Existing methods mainly address this problem from the perspective of fine-tuning. Recently, prompt-tuning has achieved success in natural language processing tasks. However, conducting prompt-tuning methods for stance detection in real-world remains a challenge for several reasons: (1) The text form of stance detection is usually short and informal, which makes it difficult to design label words for the verbalizer. (2) The tweet text may not explicitly give the attitude. Instead, users may use various hashtags or background knowledge to express stance-aware perspectives. In this article, we first propose a prompt-tuning-based framework that performs stance detection in a cloze question manner. Specifically, a knowledge-enhanced prompt-tuning framework (KEprompt) method is designed, which consists of an automatic verbalizer (AutoV) and background knowledge injection (BKI). Specifically, in AutoV, we introduce a semantic graph to build a better mapping from the predicted word of the pretrained language model and detection labels. In BKI, we first propose a topic model for learning hashtag representation and introduce ConceptGraph as the supplement of the target. At last, we present a challenging dataset for stance detection, where all stance categories are expressed in an implicit manner. Extensive experiments on a large real-world dataset demonstrate the superiority of KEprompt over state-of-the-art methods.

Microarray technology is a popular technique that has been extensively applied in cancer diagnosis. Many studies have used high-dimensional microarray data to identify informative features to classify the types of cancer, yet numerous irrelevant features that exist in microarray data may introduce the noise and decrease classification accuracy. Regularization techniques are common methods for feature selection, which can be used to reduce irrelevant features and avoid overfitting. In recent years, different regularization methods have been proposed. Theoretically, the Lq (0<q<1) type penalty function with the lower value of .. would acquire better sparse solutions. In addition, the loss function in most regression models is based on least-squares minimization. However, the least-square method is sensitive to noise and has poor robustness, especially when the error has a heavy-tailed distribution. It is well known that the least absolute deviation regression is the most common method for the robust regression, which can overcome the big noise problem. In general, there is a high level of noise in microarray data, which deter the development of microarray technology. To solve the above problems, we propose a robust logistic regression based on the Lq (0<q<1) regularization approach, which is a feasible and effective approach for feature selection in microarray classification. The Lq (0<q<1) regularization leads to a non-convex optimization problem that is difficult to be solved. In this paper, we utilize a genetic algorithm based on the global search strategy to obtain an optimal solution.

In recent years, the widespread use of smartphones has become the focus of a discussion on stress and distraction as a negative influence of mobile phones. We argue that this is not just rooted in the cognitive distraction from the present, but also that the style of touch user interface design, while optimized for efficient data manipulation, adds to the stress. With inspiration from the movement qualities of the ancient practice of Tai Chi we investigate if a radical redesign of interaction style can potentially contribute to relieve stress and improve the crucial skill of directing attention at will. In this paper, we present the design and initial evaluation of a stress relief application called PAUSE, which uses Mindful Touch as an interaction style. We suggest that Mindful Touch can co-exist with the current smartphone interaction as a complementary style, to form a more balanced, constructive relationship between people and smartphones.

Three fluorinated (¹⁹F or ¹⁸F) small molecules were evaluated as fluorescent or radiolabeled probes for Aβ deposits in the brain.