Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks

Prediction of popularity has profound impact for social media, since it offers opportunities to reveal individual preference and public attention from evolutionary social systems. Previous research, although achieves promising results, neglects one distinctive characteristic of social data, i.e., sequentiality. For example, the popularity of online content is generated over time with sequential post streams of social media. To investigate the sequential prediction of popularity, we propose a novel prediction framework called Deep Temporal Context Networks (DTCN) by incorporating both temporal context and temporal attention into account. Our DTCN contains three main components, from embedding, learning to predicting. With a joint embedding network, we obtain a unified deep representation of multi-modal user-post data in a common embedding space. Then, based on the embedded data sequence over time, temporal context learning attempts to recurrently learn two adaptive temporal contexts for sequential popularity. Finally, a novel temporal attention is designed to predict new popularity (the popularity of a new user-post pair) with temporal coherence across multiple time-scales. Experiments on our released image dataset with about 600K Flickr photos demonstrate that DTCN outperforms state-of-the-art deep prediction algorithms, with an average of 21.51% relative performance improvement in the popularity prediction (Spearman Ranking Correlation).Comment: accepted in IJCAI-1

Do Large Language Models Know What They Don't Know?

Large language models (LLMs) have a wealth of knowledge that allows them to excel in various Natural Language Processing (NLP) tasks. Current research focuses on enhancing their performance within their existing knowledge. Despite their vast knowledge, LLMs are still limited by the amount of information they can accommodate and comprehend. Therefore, the ability to understand their own limitations on the unknows, referred to as self-knowledge, is of paramount importance. This study aims to evaluate LLMs' self-knowledge by assessing their ability to identify unanswerable or unknowable questions. We introduce an automated methodology to detect uncertainty in the responses of these models, providing a novel measure of their self-knowledge. We further introduce a unique dataset, SelfAware, consisting of unanswerable questions from five diverse categories and their answerable counterparts. Our extensive analysis, involving 20 LLMs including GPT-3, InstructGPT, and LLaMA, discovering an intrinsic capacity for self-knowledge within these models. Moreover, we demonstrate that in-context learning and instruction tuning can further enhance this self-knowledge. Despite this promising insight, our findings also highlight a considerable gap between the capabilities of these models and human proficiency in recognizing the limits of their knowledge.Comment: 10 pages, 9 figures, accepted by Findings of ACL202

Increased expression of the pluripotency markers sex-determining region Y-box 2 and Nanog homeobox in ovarian endometriosis

BACKGROUND: The precise etiology of endometriosis is not fully understood; the involvement of stem cells theory is a new hypothesis. Related studies mainly focus on stemness-related genes, and pluripotency markers may play a role in the etiology of endometriosis. We aimed to analyze the transcription pluripotency factors sex-determining region Y-box 2 (SOX2), Nanog homeobox (NANOG), and octamer-binding protein 4 (OCT4) in the endometrium of reproductive-age women with and without ovarian endometriosis. METHODS: We recruited 26 women with laparoscopy-diagnosed ovarian endometriosis (endometriosis group) and 16 disease-free women (control group) to the study. Endometrial and endometriotic samples were collected. SOX2, NANOG, and OCT4 expression were analyzed with quantitative real-time polymerase chain reaction, western blotting, and immunohistochemistry. RESULTS: Compared to the control group, SOX2 mRNA and protein expression was significantly higher in the eutopic endometrium of participants in the endometriosis group. In the endometriosis group, SOX2 and NANOG mRNA and protein expression were significantly increased in ectopic endometrium compared with eutopic endometrium; there was a trend towards lower OCT4 mRNA expression and higher OCT4 protein expression in ectopic endometrium. CONCLUSIONS: The transcription pluripotency factors SOX2 and NANOG were overexpression in ovarian endometriosis, their role in pathogenesis of endometriosis should be further studied

DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuning

We propose Multiple Experts Fine-tuning Framework to build a financial large language model (LLM), DISC-FinLLM. Our methodology improves general LLMs by endowing them with multi-turn question answering abilities, domain text processing capabilities, mathematical computation skills, and retrieval-enhanced generation capabilities. We build a financial instruction-tuning dataset named DISC-FIN-SFT, including instruction samples of four categories (consulting, NLP tasks, computing and retrieval-augmented generation). Evaluations conducted on multiple benchmarks demonstrate that our model performs better than baseline models in various financial scenarios. Further resources can be found at https://github.com/FudanDISC/DISC-FinLLM.Comment: 18 pages, 13 figures, 7 table

Dichroism of x-­ray fluorescence under standing waves regime in magnetic periodic multilayers

We present the first test of the implementation of a characterization method whose aim is to study the interfaces of magnetic and periodic hetero-structures. The methodology relies on the combination of two techniques, generation of x-ray standing waves and dichroism in x-ray emission. The first one gives the depth selectivity since the maximum of the electric field can be put in specific locations of the stack, the centre of layers or their interfaces, while the second one enables being sensitive to the magnetic character of the atoms present within the stack. To concentrate on the methodology, the well-studied Mg/Co multilayer is analysed by using incident photon of monochromatic energies across the Co L2,3 absorption edge and measuring the intensity of the Co Lαβ emission. Despite large dispersive effects preventing the maxima of the electric field to reach the interfaces of the stack, it has been possible to observe the dichroic signal in the angular distribution of the Co emission intensity, i.e. in the so-called x-ray standing wave curve