Heuristics-Driven Link-of-Analogy Prompting: Enhancing Large Language Models for Document-Level Event Argument Extraction

In this study, we investigate in-context learning (ICL) in document-level event argument extraction (EAE). The paper identifies key challenges in this problem, including example selection, context length limitation, abundance of event types, and the limitation of Chain-of-Thought (CoT) prompting in non-reasoning tasks. To address these challenges, we introduce the Heuristic-Driven Link-of-Analogy (HD-LoA) prompting method. Specifically, we hypothesize and validate that LLMs learn task-specific heuristics from demonstrations via ICL. Building upon this hypothesis, we introduce an explicit heuristic-driven demonstration construction approach, which transforms the haphazard example selection process into a methodical method that emphasizes task heuristics. Additionally, inspired by the analogical reasoning of human, we propose the link-of-analogy prompting, which enables LLMs to process new situations by drawing analogies to known situations, enhancing their adaptability. Extensive experiments show that our method outperforms the existing prompting methods and few-shot supervised learning methods, exhibiting F1 score improvements of 4.53% and 9.38% on the document-level EAE dataset. Furthermore, when applied to sentiment analysis and natural language inference tasks, the HD-LoA prompting achieves accuracy gains of 2.87% and 2.63%, indicating its effectiveness across different tasks

Decision aids for cancer survivors engagement with survivorship care services after primary treatment: a systematic review.

PURPOSE: To elucidate existing decision aids (DAs) in supporting cancer survivors decisions to engage in cancer survivorship care services after primary treatment. Secondary objectives are to assess the DA acceptability, impact of DAs, and implementation barriers. METHODS: Databases (PubMed, Embase, PsycINFO, CINAHL) were searched to collect publications from inception through September 2021. Studies describing the development or evaluation of DAs used for survivorship care services after primary cancer treatment were included. Article selection and critical appraisal were conducted independently by two authors. RESULTS: We included 16 studies that described 13 DAs and addressed multiple survivorship care domains: prevention of recurrence/new cancers in Hodgkin lymphoma survivors and breast cancer gene mutation carriers, family building options, health insurance plans, health promotion (substance use behavior, cardiovascular disease risk reduction), advanced care planning, and post-treatment follow-up intensity. The electronic format was used to design most DAs for self-administration. The content presentation covered decisional context, options, and value clarification exercises. DAs were acceptable and associated with higher knowledge but presented inconclusive decisional outcomes. Implementation barriers included lack of design features for connectivity to care, low self-efficacy, and low perceived DA usefulness among healthcare professionals. Other survivor characteristics included age, literacy, preferred timing, and setting. CONCLUSIONS: A diverse range of DAs exists in survivorship care services engagement with favorable knowledge outcomes. Future work should clarify the impact of DAs on decisional outcomes. IMPLICATIONS FOR CANCER SURVIVORS: DA characterization and suggestions for prospective developers could enhance support for cancer survivors encountering complex decisions throughout the survivorship continuum

Tailored text augmentation for sentiment analysis

In synonym replacement-based data augmentation techniques for natural language processing tasks, words in a sentence are often sampled randomly with equal probability. In this paper, we propose a novel data augmentation technique named Tailored Text Argumentation (TTA) for sentiment analysis. It has two main operations. The first operation is the probabilistic word sampling for synonym replacement based on the discriminative power and relevance of the word to sentiment. The second operation is the identification of words irrelevant to sentiment but discriminative for the training data, and application of zero masking or contextual replacement to these words. The first operation expands the coverage of discriminative words, while the second operation alleviates the problem of misfitting. Both operations tend to improve the model's generalization capability. Extensive experiments on simulated low-data regimes demonstrate that TTA yields notable improvements over six strong baselines. Finally, TTA is applied to public sentiment analysis on measures against Covid-19, which again proves the effectiveness of the new data augmentation algorithm.National Research Foundation (NRF)This work is an outcome of the Future Resilient Systems project at Singapore-ETH Centre (SEC) supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme

Decision aids for cancer survivors’ engagement with survivorship care services after primary treatment: a systematic review

Purpose: To elucidate existing decision aids (DAs) in supporting cancer survivors’ decisions to engage in cancer survivorship care services after primary treatment. Secondary objectives are to assess the DA acceptability, impact of DAs, and implementation barriers. Methods: Databases (PubMed, Embase, PsycINFO, CINAHL) were searched to collect publications from inception through September 2021. Studies describing the development or evaluation of DAs used for survivorship care services after primary cancer treatment were included. Article selection and critical appraisal were conducted independently by two authors. Results: We included 16 studies that described 13 DAs and addressed multiple survivorship care domains: prevention of recurrence/new cancers in Hodgkin lymphoma survivors and breast cancer gene mutation carriers, family building options, health insurance plans, health promotion (substance use behavior, cardiovascular disease risk reduction), advanced care planning, and post-treatment follow-up intensity. The electronic format was used to design most DAs for self-administration. The content presentation covered decisional context, options, and value clarification exercises. DAs were acceptable and associated with higher knowledge but presented inconclusive decisional outcomes. Implementation barriers included lack of design features for connectivity to care, low self-efficacy, and low perceived DA usefulness among healthcare professionals. Other survivor characteristics included age, literacy, preferred timing, and setting. Conclusions: A diverse range of DAs exists in survivorship care services engagement with favorable knowledge outcomes. Future work should clarify the impact of DAs on decisional outcomes. Implications for Cancer Survivors: DA characterization and suggestions for prospective developers could enhance support for cancer survivors encountering complex decisions throughout the survivorship continuum.</p

The Efficient Method for Calculating the Physical Optics Scattered Fields from the Concave Surfaces

In this work, the numerical steepest descent path (NSDP) method is proposed to compute the highly oscillatory physical optics (PO) scattered fields from the concave surfaces, including both the monostatic and the bistatic cases. Quadratic variations are adopted to approximate the integrands of the PO type integral into the canonical form. Then, on involving the NSDP method, we deform the integration paths of the integrals into several NSDPs on the complex plain, through which the highly oscillatory integrands are converted to exponentially decay integrands. The RCS results of the PO scattered field are calculated and are compared with the high frequency asymptotic (HFA) method and the brute force (BF) method. The results demonstrate that the proposed NSDP method for calculating PO scattered fields from concave surfaces is frequency-independent and error-controllable. Numerical examples are provided to verify the efficiencies of the NSDP method

Study on the Influence of Sowing Rate, Water and Fertilizer Coupling on Water Use Efficiency of Fodder Millet

To study the influence of sowing rate, water and fertilizer (N, P and K) coupling on water use efficiency of fodder millet grown in autumn fallow field, taking “Jigu 18” as the tested material, a orthogonal rotation combination with five factors was designed in pot experiment. Results showed that both water and phosphate fertilizer had important impacts on water use efficiency, in which water had the maximum impact, followed by phosphate fertilizer, and nitrogen fertilizer, potassium fertilizer and sowing rate all had no obvious impact. Significant item of sowing rate, water and fertilizer coupling had the below sequence: potassium fertilizer + sowing rate > nitrogen fertilizer + phosphate fertilizer >water+ phosphate fertilizer >water+ sowing rate >water+ potassium fertilizer, and other items had no obvious impact. Mathematical model was established: y=44.26-1.311x1-2.298x2-3.682x3-6.401x4-34.540x5+0.273x1x3+0.118x1x4+0.843x1x5-1.948x2x3+6.631x4x5. The optimal scheme taking economic benefit as the examining index was cleared, that is, soil water content maintained 10%, and sowing rate of fodder millet was 15 kg/hm2. By the scheme, water use efficiency was 26.24 g/kg, and hay yield was 13980.90 kg/hm2, with economic benefit of 13830.90 yuan/hm2, which was 3063.73 yuan/hm2 more than the optimized combination with the highest hay yield, with increase magnitude of 22.15%, and was 6215.15 yuan/hm2 more than the optimized combination with the highest water use efficiency, with increase magnitude of 44.94%. The research could provide theoretic basis and technical support for production practice of fodder millet grown in autumn fallow field

CD44V3, an Alternatively Spliced Form of CD44, Promotes Pancreatic Cancer Progression

Pancreatic cancer is one of the most lethal malignant tumors. However, the molecular mechanisms responsible for its progression are little known. This study aimed to understand the regulatory role of CD44V3 in pancreatic cancer. A Kaplan&ndash;Meier analysis was performed to reveal the correlation between CD44/CD44V3 expression and the prognosis of pancreatic cancer patients. CD44V3 and U2AF1 were knocked down using shRNAs. The proliferation, migration, invasion, and stemness of two pancreatic cell lines, BxPC-3 and AsPC-1, were examined. The expression of CD44V3, cancer-associated markers, and the activation of AKT signaling were detected by qRT-PCR and Western blot. Both CD44 and CD44V3 expression levels were associated with a poor prognosis in pancreatic cancer patients. Interestingly, the expression of CD44V3, instead of CD44, was greatly increased in tumor tissues. CD44V3 knockdown inhibited the proliferation, migration, invasion, and stemness of cancer cells. CD44V3 splicing was regulated by U2AF1 and downregulation of U2AF1 enhanced CD44V3 expression, which promoted pancreatic cancer progression. CD44V3 is an important cancer-promoting factor, which may serve as a potential candidate for pancreatic cancer intervention

Control Oriented Prediction of Driver Brake Intention and Intensity Using a Composite Machine Learning Approach

Driver perception, decision, and control behaviors are easily affected by traffic conditions and driving style, showing the tendency of randomness and personalization. Brake intention and intensity are integrated and control-oriented parameters that are crucial to the development of an intelligent braking system. In this paper, a composite machine learning approach was proposed to predict driver brake intention and intensity with a proper prediction horizon. Various driving data were collected from Controller Area Network (CAN) bus under a real driving condition, which mainly contained urban and rural road types. ReliefF and RReliefF (they don’t have abbreviations) algorithms were employed as feature subset selection methods and applied in a prepossessing step before the training. The rank importance of selected predictors exhibited different trends or even negative trends when predicting brake intention and intensity. A soft clustering algorithm, Fuzzy C-means, was adopted to label the brake intention into categories, namely slight, medium, intensive, and emergency braking. Data sets with misplaced labels were used for training of an ensemble machine learning method, random forest. It was validated that brake intention could be accurately predicted 0.5 s ahead. An open-loop nonlinear autoregressive with external input (NARX) network was capable of learning the long-term dependencies in comparison to the static neural network and was suggested for online recognition and prediction of brake intensity 1 s in advance. As system redundancy and fault tolerance, a close-loop NARX network could be adopted for brake intensity prediction in the case of possible sensor failure and loss of CAN message

A Simple and High Quality Method for Isolation and Extraction of Total RNA of Pholiota adipose

Analyzing functional values of RNA using RNA-seq technology is a hot spot of researches. In order to study the medicinal value of Pholiota adipose from the transcriptome level, it is necessary to extract and isolate RNA samples of high purity and high quality P. adipose. In this study, liquid nitrogen grinding Trizol one-step method was used to extract the total RNA of P. adipose. Quality test and statistical comparative analysis were carried out for RNA extract of liquid nitrogen grinding treated and untreated P. adipose. The results showed that the concentration of RNA in the samples treated with liquid nitrogen was much higher than that of the samples without grinding treatment. The OD260/280 of both was about 2, indicating that the purity of RNA was very high. Besides, the ratio of fluorescence intensity of 25S and 18S subunit strips of three replicate samples was 1.8, 1.9, and 1.9, close to 2, indicating RNA integrity is good. RIN test results of Agilent 2100 were 9.1, 8.7, and 9.3, higher than the standard value 6.8, further proving the integrity. In sum, liquid nitrogen grinding Trizol one-step method is a very simple and efficient method for extracting high quality total RNA of P. adipose