The factor bias of technical change and technology adoption under uncertainty

This dissertation examines the impact of uncertainty on the factor bias of technical change and technological adoption behavior. An Ito stochastic control model, which is characterized by endogenous factor-augmenting technical change, is developed to investigate the relationship between uncertainty and the bias of technical change;The results show that, if a risk-averse firm faces input price uncertainty, technical change will be biased toward the input that has the more certain price. Output price uncertainty does not affect the direction of technical change bias but does affect the degree of bias. Under output price uncertainty and an input price uncertainty, technical change may be biased toward the input that has a certain price if the contemporaneous correlation coefficient between the two processes is negative or insignificantly positive. On the contrary, if the coefficient is significantly positive, technical change may be biased toward the input that has an uncertain price;It is also shown that, under production uncertainty, technical progress will be biased toward risk-reducing inputs and against risk-increasing inputs. The degree of technical change bias would be increased as the riskiness increases or as the firm becomes more risk averse;The model is integrated to incorporate hedging or forward contracts. Under output price uncertainty, the existence of forward markets has no effect on the direction of technical change bias but has an effect on the degree of bias. Under output price uncertainty and an input price uncertainty, if the forward market is unbiased, technical change will be biased toward the input that has a certain price;This dissertation also examines the effect of price uncertainty on technology adoption patterns and technological change. The results indicate that a reduction in the variance of output price will increase the rate of technology adoption and the intrafirm diffusion speed of yield-increasing technologies. The opposite is true for cost-reducing technologies

Recovering 6D Object Pose and Predicting Next-Best-View in the Crowd

Object detection and 6D pose estimation in the crowd (scenes with multiple object instances, severe foreground occlusions and background distractors), has become an important problem in many rapidly evolving technological areas such as robotics and augmented reality. Single shot-based 6D pose estimators with manually designed features are still unable to tackle the above challenges, motivating the research towards unsupervised feature learning and next-best-view estimation. In this work, we present a complete framework for both single shot-based 6D object pose estimation and next-best-view prediction based on Hough Forests, the state of the art object pose estimator that performs classification and regression jointly. Rather than using manually designed features we a) propose an unsupervised feature learnt from depth-invariant patches using a Sparse Autoencoder and b) offer an extensive evaluation of various state of the art features. Furthermore, taking advantage of the clustering performed in the leaf nodes of Hough Forests, we learn to estimate the reduction of uncertainty in other views, formulating the problem of selecting the next-best-view. To further improve pose estimation, we propose an improved joint registration and hypotheses verification module as a final refinement step to reject false detections. We provide two additional challenging datasets inspired from realistic scenarios to extensively evaluate the state of the art and our framework. One is related to domestic environments and the other depicts a bin-picking scenario mostly found in industrial settings. We show that our framework significantly outperforms state of the art both on public and on our datasets.Comment: CVPR 2016 accepted paper, project page: http://www.iis.ee.ic.ac.uk/rkouskou/6D_NBV.htm