Feature-aware conditional GAN for category text generation

Category text generation receives considerable attentions since it is beneficial for various natural language processing tasks. Recently, the generative adversarial network (GAN) has attained promising performance in text generation, attributed to its adversarial training process. However, there are several issues in text GANs, including discreteness, training instability, mode collapse, lack of diversity and controllability etc. To address these issues, this paper proposes a novel GAN framework, the feature-aware conditional GAN (FA-GAN), for controllable category text generation. In FA-GAN, the generator has a sequence-to-sequence structure for improving sentence diversity, which consists of three encoders including a special feature-aware encoder and a category-aware encoder, and one relational-memory-core-based decoder with the Gumbel SoftMax activation function. The discriminator has an additional category classification head. To generate sentences with specified categories, the multi-class classification loss is supplemented in the adversarial training. Comprehensive experiments have been conducted, and the results show that FA-GAN consistently outperforms 10 state-of-the-art text generation approaches on 6 text classification datasets. The case study demonstrates that the synthetic sentences generated by FA-GAN can match the required categories and are aware of the features of conditioned sentences, with good readability, fluency, and text authenticity.Comment: 27 pages, 8 figure

Pathological Evidence Exploration in Deep Retinal Image Diagnosis

Though deep learning has shown successful performance in classifying the label and severity stage of certain disease, most of them give few evidence on how to make prediction. Here, we propose to exploit the interpretability of deep learning application in medical diagnosis. Inspired by Koch's Postulates, a well-known strategy in medical research to identify the property of pathogen, we define a pathological descriptor that can be extracted from the activated neurons of a diabetic retinopathy detector. To visualize the symptom and feature encoded in this descriptor, we propose a GAN based method to synthesize pathological retinal image given the descriptor and a binary vessel segmentation. Besides, with this descriptor, we can arbitrarily manipulate the position and quantity of lesions. As verified by a panel of 5 licensed ophthalmologists, our synthesized images carry the symptoms that are directly related to diabetic retinopathy diagnosis. The panel survey also shows that our generated images is both qualitatively and quantitatively superior to existing methods.Comment: to appear in AAAI (2019). The first two authors contributed equally to the paper. Corresponding Author: Feng L

The temporal lagged association between meteorological factors and malaria in 30 counties in south-west China: a multilevel distributed lag non-linear analysis

BACKGROUND: The association between malaria and meteorological factors is complex due to the lagged and non-linear pattern. Without fully considering these characteristics, existing studies usually concluded inconsistent findings. Investigating the lagged correlation pattern between malaria and climatic variables may improve the understanding of the association and generate possible better prediction models. This is especially beneficial to the south-west China, which is a high-incidence area in China. METHODS: Thirty counties in south-west China were selected, and corresponding weekly malaria cases and four weekly meteorological variables were collected from 2004 to 2009. The Multilevel Distributed Lag Non-linear Model (MDLNM) was used to study the temporal lagged correlation between weekly malaria and weekly meteorological factors. The counties were divided into two groups, hot and cold weathers, in order to compare the difference under different climatic conditions and improve reliability and generalizability within similar climatic conditions. RESULTS: Rainfall was associated with malaria cases in both hot and cold weather counties with a lagged correlation, and the lag range was relatively longer than those of other meteorological factors. Besides, the lag range was longer in hot weather counties compared to cold weather counties. Relative humidity was correlated with malaria cases at early and late lags in hot weather counties. Minimum temperature had a longer lag range and larger correlation coefficients for hot weather counties compared to cold weather counties. Maximum temperature was only associated with malaria cases at early lags. CONCLUSION: Using weekly malaria cases and meteorological information, this work studied the temporal lagged association pattern between malaria cases and meteorological information in south-west China. The results suggest that different meteorological factors show distinct patterns and magnitudes for the lagged correlation, and the patterns will depend on the climatic condition. Existing inconsistent findings for climatic factors’ lags could be due to either the invalid assumption of a single fixed lag or the distinct temperature conditions from different study sites. The lag pattern for meteorological factors should be considered in the development of malaria early warning system

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

UniDoc: A Universal Large Multimodal Model for Simultaneous Text Detection, Recognition, Spotting and Understanding

In the era of Large Language Models (LLMs), tremendous strides have been made in the field of multimodal understanding. However, existing advanced algorithms are limited to effectively utilizing the immense representation capabilities and rich world knowledge inherent to these large pre-trained models, and the beneficial connections among tasks within the context of text-rich scenarios have not been sufficiently explored. In this work, we introduce UniDoc, a novel multimodal model equipped with text detection and recognition capabilities, which are deficient in existing approaches. Moreover, UniDoc capitalizes on the beneficial interactions among tasks to enhance the performance of each individual task. To implement UniDoc, we perform unified multimodal instruct tuning on the contributed large-scale instruction following datasets. Quantitative and qualitative experimental results show that UniDoc sets state-of-the-art scores across multiple challenging benchmarks. To the best of our knowledge, this is the first large multimodal model capable of simultaneous text detection, recognition, spotting, and understanding

Topical Digitoxigenin for Wound Healing: A Feasibility Study

(1) Background: Cardiotonic steroids have been found to stimulate collagen synthesis and might be potential wound healing therapeutics. The objective of this study was to evaluate the feasibility of digitoxigenin and its topical formulation for wound healing; (2) Methods: In the in vitro study, the human dermal fibroblast cells were treated with digitoxigenin and collagen synthesis was assessed. In the in vivo study, digitoxigenin was applied to excisional full-thickness wounds in rats immediately after wounding and remained for three days, and wound open was evaluated over 10 days. A digitoxigenin formulation for topical administration was prepared, and the in vitro release and in vivo wound healing effect were investigated; (3) Results: The expression of procollagen in human dermal fibroblast was significantly increased with the exposure to 0.1 nM digitoxigenin. Topical application of digitoxigenin in olive oil or alginate solution for three days significantly decreased the wound open in rats. Similarly, topical administration of the developed digitoxigenin formulation for three days also significantly increased wound healing. No wound healing effects were observed at days 7 and 10 after wounding when digitoxigenin was not applied; and, (4) Conclusions: It was possible to deliver digitoxigenin using the developed formulation. However, the wound healing effect of digitoxigenin and its mechanisms need to be further investigated in future studies

Dynamic Memory-based Curiosity: A Bootstrap Approach for Exploration

The sparsity of extrinsic rewards poses a serious challenge for reinforcement learning (RL). Currently, many efforts have been made on curiosity which can provide a representative intrinsic reward for effective exploration. However, the challenge is still far from being solved. In this paper, we present a novel curiosity for RL, named DyMeCu, which stands for Dynamic Memory-based Curiosity. Inspired by human curiosity and information theory, DyMeCu consists of a dynamic memory and dual online learners. The curiosity arouses if memorized information can not deal with the current state, and the information gap between dual learners can be formulated as the intrinsic reward for agents, and then such state information can be consolidated into the dynamic memory. Compared with previous curiosity methods, DyMeCu can better mimic human curiosity with dynamic memory, and the memory module can be dynamically grown based on a bootstrap paradigm with dual learners. On multiple benchmarks including DeepMind Control Suite and Atari Suite, large-scale empirical experiments are conducted and the results demonstrate that DyMeCu outperforms competitive curiosity-based methods with or without extrinsic rewards. We will release the code to enhance reproducibility

Research and Application of Geotechnical Data Consistency in Marine Site Exploration

[Introduction] The current marine geological exploration, particularly the geotechnical investigation, often follows traditional experiences without a clear objective, and lacks the evaluations of soil sample disturbance and test results. Therefore, it is necessary to conduct in-depth research on marine geotechnical investigation technology. [Method] Based on the domestic and international research on marine geological exploration technology and multi-year working experience in different sea areas, we developed a new accurate marine geological exploration technology with emphasis on the principle of consistency. The geotechnical investigation part of the new method included two basic aspects: (1) comprehensive evaluation of soil sample disturbance; (2) comprehensive evaluation of consistency of soil strength test results. [Result] The three-dimensional cross-consistency analysis of geotechnical, geophysical, and geological models has established an accurate geological exploration system. Several examples of marine engineering projects showed that the new technology can screen good acquisition methods and data through comprehensive evaluation of clay soil sample disturbance, and conduct consistency analysis of various data by combining the laboratory geotechnical tests with in-situ tests and the routine geotechnical tests with advanced geotechnical tests to obtain richer and more continuous data at the borehole. Compared with the traditional single geotechnical test, our new technology demonstrated the improved reliability and accuracy of the data, and the missing data between the sampling points are compensated. [Conclusion] The proposed new method can reinforce the application of geological exploration data, reduce investigation costs, and provide more geotechnical parameters that are more reliable for marine engineering projects including offshore wind power

Research on the Engineering Geological Model and Its Application for Offshore Wind Power Development and Construction

[Introduction] Speeding up the offshore wind power construction and development is of great significance to promoting the adjustment of China's energy structure. China is accelerating the process of its wind power development in the entire offshore area, and the geological survey is a vital foundation and key technology of offshore wind power development. [Method] By studying the marine geological survey technologies at home and abroad and combining years of experience in different sea areas, this paper proposed a new technology of engineering geological modeling for offshore wind power development based on the principle of consistency. Firstly, the survey equipment selection and survey line layout started from a three-dimensional initial model to evaluate the impact of geological changes and geohazards on the offshore wind power engineering construction and took into full account the ship selection, field in situ and laboratory tests, and correlation of geophysical prospecting and geotechnical data. Then, with abundant and continuous data obtained from the boreholes and the whole wind farm during the feasibility study and detailed survey, the methods of combination of indoor geotechnical tests with the in-situ tests, the combination of multiple geophysical prospecting devices and the combination of geotechnical and geophysical prospecting methods were used to conduct a consistency analysis of various data, manage the geological survey data platform, update the model and build the final model, and the engineering geological model was continuously optimized and iterated in the subsequent stages. The engineering geological model provided comprehensive engineering geological information for the entire life cycle of offshore wind farm design, installation, operation, maintenance and decommissioning. [Result] The research results and offshore wind power geological survey examples show that by conducting consistent comprehensive layout and data analysis, effectively connecting geotechnical investigation with engineering geophysical prospecting and constructing a three-dimensional crossing engineering geological model, it can effectively solve the problem of "ambiguities of geological survey data" and improve the reliability, accuracy and application of geological survey data. [Conclusion] The new method proposed is one of the effective methods to reduce cost and increase efficiency in offshore engineering as well as the embryonic form of digital twin of offshore wind power geological survey and the foundation for the construction of a geological survey big data base