ZOOpt: Toolbox for Derivative-Free Optimization

Recent advances of derivative-free optimization allow efficient approximating the global optimal solutions of sophisticated functions, such as functions with many local optima, non-differentiable and non-continuous functions. This article describes the ZOOpt (https://github.com/eyounx/ZOOpt) toolbox that provides efficient derivative-free solvers and are designed easy to use. ZOOpt provides a Python package for single-thread optimization, and a light-weighted distributed version with the help of the Julia language for Python described functions. ZOOpt toolbox particularly focuses on optimization problems in machine learning, addressing high-dimensional, noisy, and large-scale problems. The toolbox is being maintained toward ready-to-use tool in real-world machine learning tasks

Generating Medical Assessments Using a Neural Network Model: Algorithm Development and Validation

BACKGROUND: Since its inception, artificial intelligence has aimed to use computers to help make clinical diagnoses. Evidence-based medical reasoning is important for patient care. Inferring clinical diagnoses is a crucial step during the patient encounter. Previous works mainly used expert systems or machine learning-based methods to predict the International Classification of Diseases - Clinical Modification codes based on electronic health records. We report an alternative approach: inference of clinical diagnoses from patients\u27 reported symptoms and physicians\u27 clinical observations. OBJECTIVE: We aimed to report a natural language processing system for generating medical assessments based on patient information described in the electronic health record (EHR) notes. METHODS: We processed EHR notes into the Subjective, Objective, Assessment, and Plan sections. We trained a neural network model for medical assessment generation (N2MAG). Our N2MAG is an innovative deep neural model that uses the Subjective and Objective sections of an EHR note to automatically generate an expert-like assessment of the patient. N2MAG can be trained in an end-to-end fashion and does not require feature engineering and external knowledge resources. RESULTS: We evaluated N2MAG and the baseline models both quantitatively and qualitatively. Evaluated by both the Recall-Oriented Understudy for Gisting Evaluation metrics and domain experts, our results show that N2MAG outperformed the existing state-of-the-art baseline models. CONCLUSIONS: N2MAG could generate a medical assessment from the Subject and Objective section descriptions in EHR notes. Future work will assess its potential for providing clinical decision support

Near Real-Time Gravitational Wave Data Analysis of the Massive Black Hole Binary with TianQin

Space-borne gravitational wave detectors can detect sources like the merger of massive black holes. The rapid identification and localization of the source would play a crucial role in multi-messenger observation. The geocentric orbit of the space-borne gravitational wave detector, TianQin, makes it possible to conduct real-time data transmission. In this manuscript, we develop a search and localization pipeline for massive black hole binaries with TianQin, under both regular and real-time data transmission modes. We demonstrate that with real-time data transmission, it is possible to accurately localize the massive black hole binaries on-the-fly. With the approaching of the merger, the localization rapidly shrinks, and the data analysis can be finished at a speed comparable to the data downlink speed

k-semistratifiable spaces and expansions of set-valued mappings

[EN] In this paper, the concept of k-upper semi-continuous set-valued mappings is introduced. Using this concept, we give characterizations of k-semistratifiable and k-MCM spaces, which answers a question posed by Xie and Yan.Yan, P.; Hu, X.; Xie, L. (2018). k-semistratifiable spaces and expansions of set-valued mappings. Applied General Topology. 19(1):145-153. doi:10.4995/agt.2018.7883SWORD14515319