finding a representative fluid in a hydrocarbon reservoir is crucial for integrated reservoir management. in this study, a systematic approach for screening and selecting consistent fluid samples in a reservoir was developed. the model integrated quality control (qc) of well conditioning before sampling, qc of pvt data, thermodynamic modeling and compositional gradient within a reservoir. well ...

This work studies constrained stochastic optimization problems where the objective and constraint functions are convex expressed as compositions of functions. The problem arises in context fair classification, regression, design queuing systems. Of particular interest is large-scale setting an oracle provides gradients constituent functions, goal to solve with a minimal number calls oracle. Owi...

2 Method 1 2.1 Optical Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2.2 Lucas Kanade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.3 Gradient Descent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.4 Conjugate Gradient Descent . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.5 Newton’s Method . . . . . . ...

Neural networks are very powerful learning systems, but they do not readily generalize from one task to the other. This is partly due to the fact that they do not learn in a compositional way, that is, by discovering skills that are shared by different tasks, and recombining them to solve new problems. In this paper, we explore the compositional generalization capabilities of recurrent neural n...

Chemical vapor deposition (CVD) co-polymerization was used to fabricate polymer coatings, which comprise of reactive surface composition gradients. Two functionalized derivatives of [2.2]paracyclophane were fed into a two-source CVD system at a 180 ° angle, then copolymerized and deposited as a polymer gradient. Infrared and X-ray photoelectron spectroscopy (XPS) confirmed the compositional cha...

The stochastic composition optimization proposed recently by Wang et al. [2014] minimizes the objective with the compositional expectation form: minx (EiFi ◦ EjGj)(x). It summarizes many important applications in machine learning, statistics, and finance. In this paper, we consider the finite-sum scenario for composition optimization: min x f (x) := 1 n n ∑ i=1 Fi ( 1 m m ∑ j=1 Gj(x) ) . We pro...