The emergence and progression of multiple chronic conditions (MCC) over time often form a dynamic network that depends on patient's modifiable risk factors and their interaction with non-modifiable risk factors and existing conditions. Continuous time Bayesian networks (CTBNs) are effective methods for modeling the complex network of MCC relationships over time. However, CTBNs are not able to effectively formulate the dynamic impact of patient's modifiable risk factors on the emergence and progression of MCC. Considering a functional CTBN (FCTBN) to represent the underlying structure of the MCC relationships with respect to individuals' risk factors and existing conditions, we propose a nonlinear state-space model based on Extended Kalman filter (EKF) to capture the dynamics of the patients' modifiable risk factors and existing conditions on the MCC evolution over time. We also develop a tensor control chart to dynamically monitor the effect of changes in the modifiable risk factors of individual patients on the risk of new chronic conditions emergence. We validate the proposed approach based on a combination of simulation and real data from a dataset of 385 patients from Cameron County Hispanic Cohort (CCHC) over multiple years. The dataset examines the emergence of 5 chronic conditions (Diabetes, Obesity, Cognitive Impairment, Hyperlipidemia, and Hypertension) based on 4 modifiable risk factors representing lifestyle behaviors (Diet, Exercise, Smoking Habit, and Drinking Habit) and 3 non-modifiable risk factors, including demographic information (Age, Gender, Education). The results demonstrate the effectiveness of the proposed methodology for dynamic prediction and monitoring of the risk of MCC emergence in individual patients.

### 相关内容

ACM/IEEE第23届模型驱动工程语言和系统国际会议，是模型驱动软件和系统工程的首要会议系列，由ACM-SIGSOFT和IEEE-TCSE支持组织。自1998年以来，模型涵盖了建模的各个方面，从语言和方法到工具和应用程序。模特的参加者来自不同的背景，包括研究人员、学者、工程师和工业专业人士。MODELS 2019是一个论坛，参与者可以围绕建模和模型驱动的软件和系统交流前沿研究成果和创新实践经验。今年的版本将为建模社区提供进一步推进建模基础的机会，并在网络物理系统、嵌入式系统、社会技术系统、云计算、大数据、机器学习、安全、开源等新兴领域提出建模的创新应用以及可持续性。 官网链接：http://www.modelsconference.org/

In this paper, we enable automated property verification of deliberative components in robot control architectures. We focus on formalizing the execution context of Behavior Trees (BTs) to provide a scalable, yet formally grounded, methodology to enable runtime verification and prevent unexpected robot behaviors. To this end, we consider a message-passing model that accommodates both synchronous and asynchronous composition of parallel components, in which BTs and other components execute and interact according to the communication patterns commonly adopted in robotic software architectures. We introduce a formal property specification language to encode requirements and build runtime monitors. We performed a set of experiments, both on simulations and on the real robot, demonstrating the feasibility of our approach in a realistic application and its integration in a typical robot software architecture. We also provide an OS-level virtualization environment to reproduce the experiments in the simulated scenario.

In randomized experiments, interactions between units might generate a treatment diffusion process. This is common when the treatment of interest is an actual object or product that can be shared among peers (e.g., flyers, booklets, videos). For instance, if the intervention of interest is an information campaign realized through the distribution of a video to targeted individuals, some of these treated individuals might share the video they received with their friends. Such a phenomenon is usually unobserved, causing a misallocation of individuals in the two treatment arms: some of the initially untreated units might have actually received the treatment by diffusion. Treatment misclassification can, in turn, introduce a bias in the estimation of the causal effect. Inspired by a recent field experiment on the effect of different types of school incentives aimed at encouraging students to attend cultural events, we present a novel approach to deal with a hidden diffusion process on observed or partially observed networks.Specifically, we develop a simulation-based sensitivity analysis that assesses the robustness of the estimates against the possible presence of a treatment diffusion. We simulate several diffusion scenarios within a plausible range of sensitivity parameters and we compare the treatment effect which is estimated in each scenario with the one that is obtained while ignoring the diffusion process. Results suggest that even a treatment diffusion parameter of small size may lead to a significant bias in the estimation of the treatment effect.

A Bayes factor is proposed for testing whether the effect of a key predictor variable on the dependent variable is linear or nonlinear, possibly while controlling for certain covariates. The test can be used (i) when one is interested in quantifying the relative evidence in the data of a linear versus a nonlinear relationship and (ii) to quantify the evidence in the data in favor of a linear relationship (useful when building linear models based on transformed variables). Under the nonlinear model, a Gaussian process prior is employed using a parameterization similar to Zellner's $g$ prior resulting in a scale-invariant test. Moreover a Bayes factor is proposed for one-sided testing of whether the nonlinear effect is consistently positive, consistently negative, or neither. Applications are provides from various fields including social network research and education.

Nowadays, colleges and universities use predictive analytics in a variety of ways to increase student success rates. Despite the potentials for predictive analytics, there exist two major barriers to their adoption in higher education: (a) the lack of democratization in deployment, and (b) the potential to exacerbate inequalities. Education researchers and policymakers encounter numerous challenges in deploying predictive modeling in practice. These challenges present in different steps of modeling including data preparation, model development, and evaluation. Nevertheless, each of these steps can introduce additional bias to the system if not appropriately performed. Most large-scale and nationally representative education data sets suffer from a significant number of incomplete responses from the research participants. Missing Values are the frequent latent causes behind many data analysis challenges. While many education-related studies addressed the challenges of missing data, little is known about the impact of handling missing values on the fairness of predictive outcomes in practice. In this paper, we set out to first assess the disparities in predictive modeling outcome for college-student success, then investigate the impact of imputation techniques on the model performance and fairness using a comprehensive set of common metrics. The comprehensive analysis of a real large-scale education dataset reveals key insights on the modeling disparity and how different imputation techniques fundamentally compare to one another in terms of their impact on the fairness of the student-success predictive outcome.

As robotic systems move from highly structured environments to open worlds, incorporating uncertainty from dynamics learning or state estimation into the control pipeline is essential for robust performance. In this paper we present a nonlinear particle model predictive control (PMPC) approach to control under uncertainty, which directly incorporates any particle-based uncertainty representation, such as those common in robotics. Our approach builds on scenario methods for MPC, but in contrast to existing approaches, which either constrain all or only the first timestep to share actions across scenarios, we investigate the impact of a \textit{partial consensus horizon}. Implementing this optimization for nonlinear dynamics by leveraging sequential convex optimization, our approach yields an efficient framework that can be tuned to the particular information gain dynamics of a system to mitigate both over-conservatism and over-optimism. We investigate our approach for two robotic systems across three problem settings: time-varying, partially observed dynamics; sensing uncertainty; and model-based reinforcement learning, and show that our approach improves performance over baselines in all settings.

This paper describes Ariel Team's autonomous racing controller for the Indy Autonomous Challenge (IAC) simulation race \cite{INDY}. IAC is the first multi-vehicle autonomous head-to-head competition, reaching speeds of 300 km/h along an oval track, modeled after the Indianapolis Motor Speedway (IMS). Our racing controller attempts to maximize progress along the track while avoiding collisions with opponent vehicles and obeying the race rules. To this end, the racing controller first computes a race line offline. Then, it repeatedly computes online a small set of dynamically feasible maneuver candidates, each tested for collision with the opponent vehicles. Finally, it selects the maneuver that maximizes progress along the track, taking into account the race line. The maneuver candidates, as well as the predicted trajectories of the opponent vehicles, are approximated using a point mass model. Despite the simplicity of this racing controller, it managed to drive competitively and with no collision with any of the opponent vehicles in the IAC final simulation race.

Gaussian process (GP) regression in large-data contexts, which often arises in surrogate modeling of stochastic simulation experiments, is challenged by cubic runtimes. Coping with input-dependent noise in that setting is doubly so. Recent advances target reduced computational complexity through local approximation (e.g., LAGP) or otherwise induced sparsity. Yet these do not economically accommodate a common design feature when attempting to separate signal from noise. Replication can offer both statistical and computational efficiencies, motivating several extensions to the local surrogate modeling toolkit. Introducing a nugget into a local kernel structure is just the first step. We argue that a new inducing point formulation (LIGP), already preferred over LAGP on the speed-vs-accuracy frontier, conveys additional advantages when replicates are involved. Woodbury identities allow local kernel structure to be expressed in terms of unique design locations only, increasing the amount of data (i.e., the neighborhood size) that may be leveraged without additional flops. We demonstrate that this upgraded LIGP provides more accurate prediction and uncertainty quantification compared to several modern alternatives. Illustrations are provided on benchmark data, real-world simulation experiments on epidemic management and ocean oxygen concentration, and in an options pricing control framework.

This paper develops an asymptotic theory for estimating the time-varying characteristics of locally stationary functional time series. We introduce a kernel-based method to estimate the time-varying covariance operator and the time-varying mean function of a locally stationary functional time series. Subsequently, we derive the convergence rate of the kernel estimator of the covariance operator and associated eigenvalue and eigenfunctions. We also establish a central limit theorem for the kernel-based locally weighted sample mean. As applications of our results, we discuss the prediction of locally stationary functional time series and methods for testing the equality of time-varying mean functions in two functional samples.

This paper proposes a neural sequence-to-sequence text-to-speech (TTS) model which can control latent attributes in the generated speech that are rarely annotated in the training data, such as speaking style, accent, background noise, and recording conditions. The model is formulated as a conditional generative model based on the variational autoencoder (VAE) framework, with two levels of hierarchical latent variables. The first level is a categorical variable, which represents attribute groups (e.g. clean/noisy) and provides interpretability. The second level, conditioned on the first, is a multivariate Gaussian variable, which characterizes specific attribute configurations (e.g. noise level, speaking rate) and enables disentangled fine-grained control over these attributes. This amounts to using a Gaussian mixture model (GMM) for the latent distribution. Extensive evaluation demonstrates its ability to control the aforementioned attributes. In particular, we train a high-quality controllable TTS model on real found data, which is capable of inferring speaker and style attributes from a noisy utterance and use it to synthesize clean speech with controllable speaking style.

Modeling user-item interaction patterns is an important task for personalized recommendations. Many recommender systems are based on the assumption that there exists a linear relationship between users and items while neglecting the intricacy and non-linearity of real-life historical interactions. In this paper, we propose a neural network based recommendation model (NeuRec) that untangles the complexity of user-item interactions and establishes an integrated network to combine non-linear transformation with latent factors. We further design two variants of NeuRec: user-based NeuRec and item-based NeuRec, by concentrating on different aspects of the interaction matrix. Extensive experiments on four real-world datasets demonstrated their superior performances on personalized ranking task.

Michele Colledanchise,Giuseppe Cicala,Daniele E. Domenichelli,Lorenzo Natale,Armando Tacchella
0+阅读 · 9月16日
Costanza Tortú,Irene Crimaldi,Fabrizia Mealli,Laura Forastiere
0+阅读 · 9月15日
Hadis Anahideh,Nazanin Nezami,Denisa G`andara
0+阅读 · 9月13日
Robert Dyro,James Harrison,Apoorva Sharma,Marco Pavone
0+阅读 · 9月13日
Gabriel Hartmann,Zvi Shiller,Amos Azaria
0+阅读 · 9月12日
D Austin Cole,Robert B Gramacy,Mike Ludkovski
0+阅读 · 9月11日
Wei-Ning Hsu,Yu Zhang,Ron J. Weiss,Heiga Zen,Yonghui Wu,Yuxuan Wang,Yuan Cao,Ye Jia,Zhifeng Chen,Jonathan Shen,Patrick Nguyen,Ruoming Pang
3+阅读 · 2018年12月27日
Shuai Zhang,Lina Yao,Aixin Sun,Sen Wang,Guodong Long,Manqing Dong
5+阅读 · 2018年6月3日

6+阅读 · 2020年6月15日
CreateAMind
29+阅读 · 2019年1月3日
CreateAMind
7+阅读 · 2018年12月10日
CreateAMind
23+阅读 · 2018年9月12日
CreateAMind
10+阅读 · 2018年9月6日
CreateAMind
3+阅读 · 2018年4月15日

29+阅读 · 2017年12月10日

7+阅读 · 2017年9月24日
Top