Lifelong Embedding Learning and Transfer for Growing Knowledge Graphs

Existing knowledge graph (KG) embedding models have primarily focused on static KGs. However, real-world KGs do not remain static, but rather evolve and grow in tandem with the development of KG applications. Consequently, new facts and previously unseen entities and relations continually emerge, necessitating an embedding model that can quickly learn and transfer new knowledge through growth. Motivated by this, we delve into an expanding field of KG embedding in this paper, i.e., lifelong KG embedding. We consider knowledge transfer and retention of the learning on growing snapshots of a KG without having to learn embeddings from scratch. The proposed model includes a masked KG autoencoder for embedding learning and update, with an embedding transfer strategy to inject the learned knowledge into the new entity and relation embeddings, and an embedding regularization method to avoid catastrophic forgetting. To investigate the impacts of different aspects of KG growth, we construct four datasets to evaluate the performance of lifelong KG embedding. Experimental results show that the proposed model outperforms the state-of-the-art inductive and lifelong embedding baselines.Comment: Accepted in the 37th AAAI Conference on Artificial Intelligence (AAAI 2023

Modeling Occupant Window Behavior in Hospitals—A Case Study in a Maternity Hospital in Beijing, China

Nowadays, relevant data collected from hospital buildings remain insufficient because hospital buildings often have stricter environmental requirements resulting in more limited data access than other building types. Additionally, existing window-opening behavior models were mostly developed and validated using data measured from the experimental building itself. Hence, their accuracy is only assessed by the algorithm’s evaluation index, which limits the model’s applicability, given that it is not tested by the actual cases nor cross-verified with other buildings. Based on the aforementioned issues, this study analyzes the window-opening behavior of doctors and patients in spring in a maternity hospital in Beijing and develops behavioral models using logistic regression. The results show that the room often has opened windows in spring when the outdoor temperature exceeds 20 °C. Moreover, the ward windows’ use frequency is more than 10 times higher than those of doctors’ office. The window-opening behavior in wards is more susceptible to the influence of outdoor temperature, while in the doctors’ office, more attention is paid to indoor air quality. Finally, by embedding the logistic regression model of each room into the EnergyPlus software to simulate the CO2 concentration of the room, it was found that the model has better applicability than the fixed schedule model. However, by performing cross-validation with different building types, it was found that, due to the particularity of doctors’ offices, the models developed for other building types cannot accurately reproduce the window-opening behavior of doctors. Therefore, more data are still needed to better understand window usage in hospital buildings and support the future building performance simulations of hospital buildings

Multi-dimension unified Swin Transformer for 3D Lesion Segmentation in Multiple Anatomical Locations

In oncology research, accurate 3D segmentation of lesions from CT scans is essential for the modeling of lesion growth kinetics. However, following the RECIST criteria, radiologists routinely only delineate each lesion on the axial slice showing the largest transverse area, and delineate a small number of lesions in 3D for research purposes. As a result, we have plenty of unlabeled 3D volumes and labeled 2D images, and scarce labeled 3D volumes, which makes training a deep-learning 3D segmentation model a challenging task. In this work, we propose a novel model, denoted a multi-dimension unified Swin transformer (MDU-ST), for 3D lesion segmentation. The MDU-ST consists of a Shifted-window transformer (Swin-transformer) encoder and a convolutional neural network (CNN) decoder, allowing it to adapt to 2D and 3D inputs and learn the corresponding semantic information in the same encoder. Based on this model, we introduce a three-stage framework: 1) leveraging large amount of unlabeled 3D lesion volumes through self-supervised pretext tasks to learn the underlying pattern of lesion anatomy in the Swin-transformer encoder; 2) fine-tune the Swin-transformer encoder to perform 2D lesion segmentation with 2D RECIST slices to learn slice-level segmentation information; 3) further fine-tune the Swin-transformer encoder to perform 3D lesion segmentation with labeled 3D volumes. The network's performance is evaluated by the Dice similarity coefficient (DSC) and Hausdorff distance (HD) using an internal 3D lesion dataset with 593 lesions extracted from multiple anatomical locations. The proposed MDU-ST demonstrates significant improvement over the competing models. The proposed method can be used to conduct automated 3D lesion segmentation to assist radiomics and tumor growth modeling studies. This paper has been accepted by the IEEE International Symposium on Biomedical Imaging (ISBI) 2023

Spatiotemporal patterns and driving mechanism of tourism ecological security in Guangxi, China

Tourism ecological security (TES) is an important index reflecting the sustainable development of the regional economy. The construction of the China and ASEAN Free Trade Area has increased the total tourist consumption of Guangxi province by 36.48%. Unfortunately, overconsumption of resources, air pollution, disturbance of indigenous life, and other environmental degradation problems emerged due to the significant increase in tourists. Measuring the resilience of the tourism ecosystem is an urgent need to promote the high-quality development of tourism in Guangxi. To explore the dynamic changes in TES and its driving mechanism, the DPSIR (driver–pressure–state–impact–response) model for the tourism ecosystem was developed. The dynamic changes in TES and its driving mechanism from 2010 to 2019 were analyzed using fuzzy matter-element analysis, Markov chains, Geodetector, and other methods. The results show that: (1) the TES value increased steadily by 72.73%; the improvement speed was Northeast > Southwest > Southeast > Northwest; (2) TES was negatively correlated with location, 14 cities developed independently; (3) the TES has a smaller probability to shift the lower level; (4) urbanization, water consumption, green area, tourism revenue, and the number of students in colleges had significant effects on TES. Four policies were proposed to improve TES: (1) developing forest tourism; (2) implementing greening projects in abandoned mining areas; (3) increasing tourism technical personnel; and (4) reducing clearance time for inbound tourists

KwaiYiiMath: Technical Report

Recent advancements in large language models (LLMs) have demonstrated remarkable abilities in handling a variety of natural language processing (NLP) downstream tasks, even on mathematical tasks requiring multi-step reasoning. In this report, we introduce the KwaiYiiMath which enhances the mathematical reasoning abilities of KwaiYiiBase1, by applying Supervised Fine-Tuning (SFT) and Reinforced Learning from Human Feedback (RLHF), including on both English and Chinese mathematical tasks. Meanwhile, we also constructed a small-scale Chinese primary school mathematics test set (named KMath), consisting of 188 examples to evaluate the correctness of the problem-solving process generated by the models. Empirical studies demonstrate that KwaiYiiMath can achieve state-of-the-art (SOTA) performance on GSM8k, CMath, and KMath compared with the similar size models, respectively.Comment: technical report. arXiv admin note: text overlap with arXiv:2306.16636 by other author

Cost-effectiveness of HBV and HCV screening strategies:a systematic review of existing modelling techniques

Introduction: Studies evaluating the cost-effectiveness of screening for Hepatitis B Virus (HBV) and Hepatitis C Virus (HCV) are generally heterogeneous in terms of risk groups, settings, screening intervention, outcomes and the economic modelling framework. It is therefore difficult to compare cost-effectiveness results between studies. This systematic review aims to summarise and critically assess existing economic models for HBV and HCV in order to identify the main methodological differences in modelling approaches. Methods: A structured search strategy was developed and a systematic review carried out. A critical assessment of the decision-analytic models was carried out according to the guidelines and framework developed for assessment of decision-analytic models in Health Technology Assessment of health care interventions. Results: The overall approach to analysing the cost-effectiveness of screening strategies was found to be broadly consistent for HBV and HCV. However, modelling parameters and related structure differed between models, producing different results. More recent publications performed better against a performance matrix, evaluating model components and methodology. Conclusion: When assessing screening strategies for HBV and HCV infection, the focus should be on more recent studies, which applied the latest treatment regimes, test methods and had better and more complete data on which to base their models. In addition to parameter selection and associated assumptions, careful consideration of dynamic versus static modelling is recommended. Future research may want to focus on these methodological issues. In addition, the ability to evaluate screening strategies for multiple infectious diseases, (HCV and HIV at the same time) might prove important for decision makers