Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction

The availability of a large amount of electronic health records (EHR) provides huge opportunities to improve health care service by mining these data. One important application is clinical endpoint prediction, which aims to predict whether a disease, a symptom or an abnormal lab test will happen in the future according to patients' history records. This paper develops deep learning techniques for clinical endpoint prediction, which are effective in many practical applications. However, the problem is very challenging since patients' history records contain multiple heterogeneous temporal events such as lab tests, diagnosis, and drug administrations. The visiting patterns of different types of events vary significantly, and there exist complex nonlinear relationships between different events. In this paper, we propose a novel model for learning the joint representation of heterogeneous temporal events. The model adds a new gate to control the visiting rates of different events which effectively models the irregular patterns of different events and their nonlinear correlations. Experiment results with real-world clinical data on the tasks of predicting death and abnormal lab tests prove the effectiveness of our proposed approach over competitive baselines.Comment: 8 pages, this paper has been accepted by AAAI 201

Physical properties of CO-dark molecular gas traced by C+^+

Neither HI nor CO emission can reveal a significant quantity of so-called dark gas in the interstellar medium (ISM). It is considered that CO-dark molecular gas (DMG), the molecular gas with no or weak CO emission, dominates dark gas. We identified 36 DMG clouds with C$^+$ emission (data from Galactic Observations of Terahertz C+ (GOT C+) project) and HINSA features. Based on uncertainty analysis, optical depth of HI $\tau\rm_{HI}$ of 1 is a reasonable value for most clouds. With the assumption of $\tau\rm_{HI}=1$, these clouds were characterized by excitation temperatures in a range of 20 K to 92 K with a median value of 55 K and volume densities in the range of $6.2\times10^1$ cm$^{-3}$ to $1.2\times 10^3$ cm$^{-3}$ with a median value of $2.3\times 10^2$ cm$^{-3}$. The fraction of DMG column density in the cloud ($f\rm_{DMG}$) decreases with increasing excitation temperature following an empirical relation $f\rm_{DMG}=-2.1\times 10^{-3}T_(ex,\tau_{HI}=1)$+1.0. The relation between $f\rm_{DMG}$ and total hydrogen column density $N_H$ is given by $f\rm_{DMG}$=$1.0-3.7\times 10^{20}/N_H$. The values of $f\rm_{DMG}$ in the clouds of low extinction group ($A\rm_V \le 2.7$ mag) are consistent with the results of the time-dependent, chemical evolutionary model at the age of ~ 10 Myr. Our empirical relation cannot be explained by the chemical evolutionary model for clouds in the high extinction group ($A\rm_V > 2.7$ mag). Compared to clouds in the low extinction group ($A\rm_V \le 2.7$ mag), clouds in the high extinction group ($A\rm_V > 2.7$ mag) have comparable volume densities but excitation temperatures that are 1.5 times lower. Moreover, CO abundances in clouds of the high extinction group ($A\rm_V > 2.7$ mag) are $6.6\times 10^2$ times smaller than the canonical value in the Milky Way. #[Full version of abstract is shown in the text.]#Comment: Accepted for publishing in Astronomy & Astrophysics. 13 pages, 8 figure

Event-triggered distributed H∞ state estimation with packet dropouts through sensor networks

This study is concerned with the event-triggered distributed H∞ state estimation problem for a class of discrete-time stochastic non-linear systems with packet dropouts in a sensor network. An event-triggered communication mechanism is adopted over the sensor network with hope to reduce the communication burden and the energy consumption, where the measurements on each sensor are transmitted only when a certain triggering condition is violated. Furthermore, a novel distributed state estimator is designed where the available innovations are not only from the individual sensor, but also from its neighbouring ones according to the given topology. The purpose of the problem under consideration is to design a set of distributed state estimators such that the dynamics of estimation errors is exponentially mean-square stable and also the prespecified H∞ disturbance rejection attenuation level is guaranteed. By utilising the property of the Kronecker product and the stochastic analysis approaches, sufficient conditions are established under which the addressed state estimation problem is recast as a convex optimisation one that can be easily solved via available software packages. Finally, a simulation example is utilised to illustrate the usefulness of the proposed design scheme of event-triggered distributed state estimators.This work was supported in part by Royal Society of the UK, the National Natural Science Foundation of China under Grants 61329301, 61203139, 61473076, 61374127 and 61422301, the Shanghai Rising-Star Program of China under Grant 13QA1400100, the ShuGuang project of Shanghai Municipal Education Commission and Shanghai Education Development Foundation under Grant 13SG34, the Fundamental Research Funds for the Central Universities, DHU Distinguished Young Professor Program, and the Alexander von Humboldt Foundation of Germany

Interval Estimation Methods for Discrete-Time Linear Time-Invariant Systems

International audienc

Can Marketization Improve Sustainable Development in Northeastern China? Evidence from the Perspective of Coupling Coordination Degree Model

With the acquisition of sustainable development, pursuing the coordinated development of social economy and ecological environment has been a critical approach of Northeastern China. Since the reform and opening-up, marketization has profoundly affected the regional social economy. However, what role will marketization play in regional sustainable development? Can marketization improve sustainable development measured by the coupling coordination degree? This paper adopts the coupling coordination degree model (CCDM) to estimate the coupling coordination degree and further explore the impact of marketization on the coupling coordination degree in Northeastern China from 1994 to 2019. The results show that the average values of the static coupling degree (SCD), dynamic coupling degree (DCD), and coupling coordination degree (CCD) remained in a tiny coordinated state, which indicated that the level of sustainable development in Northeastern China is presented still primary from 1990 to 2019. Marketization has a significant negative impact on the coupling coordination degree, which indicates that marketization should be considered in the process of sustainable development in Northeastern China