Modeling diurnal hormone profiles by hierarchical state space models.

Adrenocorticotropic hormone (ACTH) diurnal patterns contain both smooth circadian rhythms and pulsatile activities. How to evaluate and compare them between different groups is a challenging statistical task. In particular, we are interested in testing 1) whether the smooth ACTH circadian rhythms in chronic fatigue syndrome and fibromyalgia patients differ from those in healthy controls, and 2) whether the patterns of pulsatile activities are different. In this paper, a hierarchical state space model is proposed to extract these signals from noisy observations. The smooth circadian rhythms shared by a group of subjects are modeled by periodic smoothing splines. The subject level pulsatile activities are modeled by autoregressive processes. A functional random effect is adopted at the pair level to account for the matched pair design. Parameters are estimated by maximizing the marginal likelihood. Signals are extracted as posterior means. Computationally efficient Kalman filter algorithms are adopted for implementation. Application of the proposed model reveals that the smooth circadian rhythms are similar in the two groups but the pulsatile activities in patients are weaker than those in the healthy controls

Integrating Homomorphic Encryption and Trusted Execution Technology for Autonomous and Confidential Model Refining in Cloud

With the popularity of cloud computing and machine learning, it has been a trend to outsource machine learning processes (including model training and model-based inference) to cloud. By the outsourcing, other than utilizing the extensive and scalable resource offered by the cloud service provider, it will also be attractive to users if the cloud servers can manage the machine learning processes autonomously on behalf of the users. Such a feature will be especially salient when the machine learning is expected to be a long-term continuous process and the users are not always available to participate. Due to security and privacy concerns, it is also desired that the autonomous learning preserves the confidentiality of users' data and models involved. Hence, in this paper, we aim to design a scheme that enables autonomous and confidential model refining in cloud. Homomorphic encryption and trusted execution environment technology can protect confidentiality for autonomous computation, but each of them has their limitations respectively and they are complementary to each other. Therefore, we further propose to integrate these two techniques in the design of the model refining scheme. Through implementation and experiments, we evaluate the feasibility of our proposed scheme. The results indicate that, with our proposed scheme the cloud server can autonomously refine an encrypted model with newly provided encrypted training data to continuously improve its accuracy. Though the efficiency is still significantly lower than the baseline scheme that refines plaintext-model with plaintext-data, we expect that it can be improved by fully utilizing the higher level of parallelism and the computational power of GPU at the cloud server.Comment: IEEE INTERNATIONAL CONFERENCE ON CLOUD COMPUTING (CLOUD) 202

Government Responses Matter: Predicting Covid-19 cases in US under an empirical Bayesian time series framework

Since the Covid-19 outbreak, researchers have been predicting how the epidemic will evolve, especially the number in each country, through using parametric extrapolations based on the history. In reality, the epidemic progressing in a particular country depends largely on its policy responses and interventions. Since the outbreaks in some countries are earlier than United States, the prediction of US cases can benefit from incorporating the similarity in their trajectories. We propose an empirical Bayesian time series framework to predict US cases using different countries as prior reference. The resultant forecast is based on observed US data and prior information from the reference country while accounting for different population sizes. When Italy is used as prior in the prediction, which the US data resemble the most, the cases in the US will exceed 300,000 by the beginning of April unless strong measures are adopted

fmixed: A SAS Macro for Smoothing-Spline-Based Functional Mixed Effects Models

In this article we implement the smoothing-spline-based functional mixed effects models (Guo 2002) by a SAS macro by exploiting the connection between mixed effects models and smoothing splines. The macro can handle flexible design matrices and is easy to use. Input parameters and output results are described and explained. A numeric example and a real data example are used for illustration

Weighted Schatten pp-Norm Minimization for Image Denoising and Background Subtraction

Low rank matrix approximation (LRMA), which aims to recover the underlying low rank matrix from its degraded observation, has a wide range of applications in computer vision. The latest LRMA methods resort to using the nuclear norm minimization (NNM) as a convex relaxation of the nonconvex rank minimization. However, NNM tends to over-shrink the rank components and treats the different rank components equally, limiting its flexibility in practical applications. We propose a more flexible model, namely the Weighted Schatten $p$-Norm Minimization (WSNM), to generalize the NNM to the Schatten $p$-norm minimization with weights assigned to different singular values. The proposed WSNM not only gives better approximation to the original low-rank assumption, but also considers the importance of different rank components. We analyze the solution of WSNM and prove that, under certain weights permutation, WSNM can be equivalently transformed into independent non-convex $l_p$-norm subproblems, whose global optimum can be efficiently solved by generalized iterated shrinkage algorithm. We apply WSNM to typical low-level vision problems, e.g., image denoising and background subtraction. Extensive experimental results show, both qualitatively and quantitatively, that the proposed WSNM can more effectively remove noise, and model complex and dynamic scenes compared with state-of-the-art methods.Comment: 13 pages, 11 figure

Analysis of Stiffened Penstock External Pressure Stability Based on Immune Algorithm and Neural Network

The critical external pressure stability calculation of stiffened penstock in the hydroelectric power station is very important work for penstock design. At present, different assumptions and boundary simplification are adopted by different calculation methods which sometimes cause huge differences too. In this paper, we present an immune based artificial neural network model via the model and stability theory of elastic ring, we study effects of some factors (such as pipe diameter, pipe wall thickness, sectional size of stiffening ring, and spacing between stiffening rings) on penstock critical external pressure during huge thin-wall procedure of penstock. The results reveal that the variation of diameter and wall thickness can lead to sharp variation of penstock external pressure bearing capacity and then give the change interval of it. This paper presents an optimizing design method to optimize sectional size and spacing of stiffening rings and to determine penstock bearing capacity coordinate with the bearing capacity of stiffening rings and penstock external pressure stability coordinate with its strength safety. As a practical example, the simulation results illustrate that the method presented in this paper is available and can efficiently overcome inherent defects of BP neural network