Robust joint modeling of sparsely observed paired functional data

A reduced-rank mixed effects model is developed for robust modeling of sparsely observed paired functional data. In this model, the curves for each functional variable are summarized using a few functional principal components, and the association of the two functional variables is modeled through the association of the principal component scores. Multivariate scale mixture of normal distributions is used to model the principal component scores and the measurement errors in order to handle outlying observations and achieve robust inference. The mean functions and principal component functions are modeled using splines and roughness penalties are applied to avoid overfitting. An EM algorithm is developed for computation of model fitting and prediction. A simulation study shows that the proposed method outperforms an existing method which is not designed for robust estimation. The effectiveness of the proposed method is illustrated in an application of fitting multi-band light curves of Type Ia supernovae

Optimal subsampling for large scale Elastic-net regression

Datasets with sheer volume have been generated from fields including computer vision, medical imageology, and astronomy whose large-scale and high-dimensional properties hamper the implementation of classical statistical models. To tackle the computational challenges, one of the efficient approaches is subsampling which draws subsamples from the original large datasets according to a carefully-design task-specific probability distribution to form an informative sketch. The computation cost is reduced by applying the original algorithm to the substantially smaller sketch. Previous studies associated with subsampling focused on non-regularized regression from the computational efficiency and theoretical guarantee perspectives, such as ordinary least square regression and logistic regression. In this article, we introduce a randomized algorithm under the subsampling scheme for the Elastic-net regression which gives novel insights into L1-norm regularized regression problem. To effectively conduct consistency analysis, a smooth approximation technique based on alpha absolute function is firstly employed and theoretically verified. The concentration bounds and asymptotic normality for the proposed randomized algorithm are then established under mild conditions. Moreover, an optimal subsampling probability is constructed according to A-optimality. The effectiveness of the proposed algorithm is demonstrated upon synthetic and real data datasets.Comment: 28 pages, 7 figure

MSCET : a multi-scenario offloading schedule for biomedical data processing and analysis in cloud-edge-terminal collaborative vehicular networks

With the rapid development of Artificial Intelligence (AI) and Internet of Things (IoTs), an increasing number of computation intensive or delay sensitive biomedical data processing and analysis tasks are produced in vehicles, bringing more and more challenges to the biometric monitoring of drivers. Edge computing is a new paradigm to solve these challenges by offloading tasks from the resource-limited vehicles to Edge Servers (ESs) in Road Side Units (RSUs). However, most of the traditional offloading schedules for vehicular networks concentrate on the edge, while some tasks may be too complex for ESs to process. To this end, we consider a collaborative vehicular network in which the cloud, edge and terminal can cooperate with each other to accomplish the tasks. The vehicles can offload the computation intensive tasks to the cloud to save the resource of edge. We further construct the virtual resource pool which can integrate the resource of multiple ESs since some regions may be covered by multiple RSUs. In this paper, we propose a Multi-Scenario offloading schedule for biomedical data processing and analysis in Cloud-Edge-Terminal collaborative vehicular networks called MSCET. The parameters of the proposed MSCET are optimized to maximize the system utility. We also conduct extensive simulations to evaluate the proposed MSCET and the results illustrate that MSCET outperforms other existing schedules. © 2004-2012 IEEE

The transcription factor Ste12-like increases the mycelial abiotic stress tolerance and regulates the fruiting body development of Flammulina filiformis

IntroductionFlammulina filiformis is one of the most commercially important edible fungi worldwide, with its nutritional value and medicinal properties. It becomes a good model species to study the tolerance of abiotic stress during mycelia growth in edible mushroom cultivation. Transcription factor Ste12 has been reported to be involved in the regulation of stress tolerance and sexual reproduction in fungi.MethodsIn this study, identification and phylogenetic analysis of ste12-like was performed by bioinformatics methods. Four ste12-like overexpression transformants of F. filiformis were constructed by Agrobacterium tumefaciens-mediated transformation.Results and DiscussionPhylogenetic analysis showed that Ste12-like contained conserved amino acid sequences. All the overexpression transformants were more tolerant to salt stress, cold stress and oxidative stress than wild-type strains. In the fruiting experiment, the number of fruiting bodies of overexpression transformants increased compared with wild-type strains, but the growth rate of stipes slowed down. It suggested that gene ste12-like was involved in the regulation of abiotic stress tolerance and fruiting body development in F. filiformis

Wafer-scale heterogeneous integration InP on trenched Si with a bubble-free interface

Heterogeneous integration of compound semiconductors on a Si platform leads to advanced device applications in the field of Si photonics and high frequency electronics. However, the unavoidable bubbles formed at the bonding interface are detrimental for achieving a high yield of dissimilar semiconductor integration by the direct wafer bonding technology. In this work, lateral outgassing surface trenches (LOTs) are introduced to efficiently inhibit the bubbles. It is found that the chemical reactions in InP-Si bonding are similar to those in Si-Si bonding, and the generated gas can escape via the LOTs. The outgassing efficiency is dominated by LOTs\u27 spacing, and moreover, the relationship between bubble formation and the LOT\u27s structure is well described by a thermodynamic model. With the method explored in this work, a 2-in. bubble-free crystalline InP thin film integrated on the Si substrate with LOTs is obtained by the ion-slicing and wafer bonding technology. The quantum well active region grown on this Si-based InP film shows a superior photoemission efficiency, and it is found to be 65% as compared to its bulk counterpart