Cold start knowledge base population (KBP) is the problem of populating a knowledge base from unstructured documents. While artificial neural networks have led to significant improvements in the different tasks that are part of KBP, the overall F1 of the end-to-end system remains quite low. This problem is more acute in personal knowledge bases, which present additional challenges with regard to data protection, fairness and privacy. In this work, we present a system that uses rule based annotators and a graph neural network for missing link prediction, to populate a more complete, fair and diverse knowledge base from the TACRED dataset.
SeqUDA-Rec: Sequential User Behavior Enhanced Recommendation via Global Unsupervised Data Augmentation for Personalized Content Marketing
Southwest University of Finance and Economics（西南财经大学）；Columbia Business School, Columbia University（哥伦比亚大学商学院）；School of Humanities and Sciences, Stanford University（斯坦福大学人文与科学学院）
Deep Learning-Based Detection of Cognitive Impairment from Passive Smartphone Sensing with Routine-Aware Augmentation and Demographic Personalization
Department of Electrical and Computer Engineering, Cockrell School of Engineering, The University of Texas at Austin（电气与计算机工程系，Cockrell工程学院，德克萨斯大学奥斯汀分校）；Department of Neurology, Dell Medical School, The University of Texas at Austin（神经病学系，德克萨斯医学学院，德克萨斯大学奥斯汀分校）；Department of Neurology, University of Texas Southwestern Medical Center（神经病学系，德克萨斯西南医学中心）；Peter O’Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center（彼得·奥·唐纳德·杰罗姆脑研究所，德克萨斯西南医学中心）；Department of Speech, Language, and Hearing Sciences, Moody College of Communication, The University of Texas at Austin（语言病理学系，摩依学院，德克萨斯大学奥斯汀分校）
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Heidelberg University（海德堡大学）；University Hospital Essen (AöR)（埃森大学医院）；University Medicine Essen（埃森大学医学院）；German Cancer Consortium (DKTK)（德国癌症联盟）
With the rise in importance of personalized medicine, we trained personalized neural networks to detect tumor progression in longitudinal datasets. The model was evaluated on two datasets with a total of 64 scans from 32 patients diagnosed with glioblastoma multiforme (GBM). Contrast-enhanced T1w sequences of brain magnetic resonance imaging (MRI) images were used in this study. For each patient, we trained their own neural network using just two images from different timepoints. Our approach uses a Wasserstein-GAN (generative adversarial network), an unsupervised network architecture, to map …
Enhancing Lesion Segmentation in PET/CT Imaging with Deep Learning and Advanced Data Preprocessing Techniques
Shanghai United Imaging Healthcare Advanced Technology Research Institute Co., Ltd.（上海联影医疗科技先进技术研究院有限公司）；Shanghai Jiao Tong University（上海交通大学）；East China Normal University（华东师范大学）
Data-driven predictive solutions predominant in commercial applications tend to suffer from biases and stereotypes, which raises equity concerns. Prediction models may discover, use, or amplify spurious correlations based on gender or other protected personal characteristics, thus discriminating against marginalized groups. Mitigating gender bias has become an important research focus in natural language processing (NLP) and is an area where annotated corpora are available. Data augmentation reduces gender bias by adding counterfactual examples to the training dataset. In this work, we show th…
