Likelihood Adaptively Modified Penalties

A new family of penalty functions, adaptive to likelihood, is introduced for model selection in general regression models. It arises naturally through assuming certain types of prior distribution on the regression parameters. To study stability properties of the penalized maximum likelihood estimator, two types of asymptotic stability are defined. Theoretical properties, including the parameter estimation consistency, model selection consistency, and asymptotic stability, are established under suitable regularity conditions. An efficient coordinate-descent algorithm is proposed. Simulation results and real data analysis show that the proposed method has competitive performance in comparison with existing ones.Comment: 42 pages, 4 figure

Experimental And Cfd Investigations Of Lifted Tribrachial Flames

Experimental measurements of the lift-off velocity and lift-off height, and numerical simulations were conducted on the liftoff and stabilization phenomena of laminar jet diffusion flames of inert-diluted C3H8 and CH4 fuels. Both non-reacting and reacting jets were investigated, including effects of multi-component diffusivities and heat release (buoyancy and gas expansion). The role of Schmidt number for non-reacting jets was investigated, with no conclusive Schmidt number criterion for liftoff previously known in similarity solutions. The cold-flow simulation for He-diluted CH4 fuel does not predict flame liftoff; however, adding heat release reaction leads to the prediction of liftoff, which is consistent with experimental observations. Including reaction was also found to improve liftoff height prediction for C3H8 flames, with the flame base location differing from that in the similarity solution - the intersection of the stoichiometric and iso-velocity contours is not necessary for flame stabilization (and thus lift-off). Possible mechanisms other than that proposed for similarity solution may better help to explain the stabilization and liftoff phenomena. The stretch rate at a wide range of isotherms near the base of the lifted tribrachial flame were also quantitatively plotted and analyzed

Application of indirect immunofluorescence on the diagnosis of pemphigus

Pemphigus is an autoimmune bullous disease, and although several diagnostic methods are now in use indirect immunofluorescence (IIF) is still considered an important tool for diagnosing pemphigus because of its convenience, repeatability, and reduced pain for patients. The goal of the present study was to evaluate the diagnostic value of IIF on normal human skin (NS), monkey esophagus (ME), and salt-split skin (SS) for better diagnosis of pemphigus. Clinical data of 70 patients with pemphigus and 56 control were collected. IIF on NS, ME, and SS were assessed separately by observing fluorescein deposition and comparing its differentiation to different kinds of pemphigus and its sensitivities and specificities to different substrates. Intercellular deposition of IgG was visible when IIF on NS, ME, and SS were positive in patients with pemphigus. Their corresponding sensitivities and specificities were 30.0%, 84.3%, and 70.0% and 96.4%, 96.4%, and 94.6%, respectively. The differences in sensitivity were statistically significant between NS and ME and between NS and SS (P&lt;0.001) and the specificities among the three substrates were not statistically significantly different (P&gt;0.05). As for different types of pemphigus, the sensitivities between NS and ME and between NS and SS were statistically significantly different in both Dsg1- and Dsg3-positive and only Dsg1-positive patients with pemphigus (P&lt;0.01); the sensitivities between NS and ME were statistically significantly different only in Dsg3-positive patients with pemphigus (P&lt;0.001); there were no statistically significant differences between ME and SS. We therefore propose that ME is a good substrate for pemphigus diagnosis with higher sensitivity and superior to NS, particularly for patients with anti-Dsg3 antibodies. SS is a good alternative substrate to ME with almost identical higher sensitivities and specificities for diagnosis of pemphigus.</p

A Novel Two-Layered Reinforcement Learning for Task Offloading with Tradeoff between Physical Machine Utilization Rate and Delay

Mobile devices could augment their ability via cloud resources in mobile cloud computing environments. This paper developed a novel two-layered reinforcement learning (TLRL) algorithm to consider task offloading for resource-constrained mobile devices. As opposed to existing literature, the utilization rate of the physical machine and the delay for offloaded tasks are taken into account simultaneously by introducing a weighted reward. The high dimensionality of the state space and action space might affect the speed of convergence. Therefore, a novel reinforcement learning algorithm with a two-layered structure is presented to address this problem. First, k clusters of the physical machines are generated based on the k-nearest neighbors algorithm (k-NN). The first layer of TLRL is implemented by a deep reinforcement learning to determine the cluster to be assigned for the offloaded tasks. On this basis, the second layer intends to further specify a physical machine for task execution. Finally, simulation examples are carried out to verify that the proposed TLRL algorithm is able to speed up the optimal policy learning and can deal with the tradeoff between physical machine utilization rate and delay

Towards Sustainable Property Investment: Perspective from Asian Emerging Markets

Ph.DDOCTOR OF PHILOSOPH