We propose a new matching algorithm -- Unpaired kidney exchange -- to tackle the problem of double coincidence of wants without using money. The fundamental idea is that "memory" can serve as a medium of exchange. In a dynamic matching model with heterogeneous agents, we prove that average waiting time under the Unpaired algorithm is close to optimal, substantially less than the standard pairwise and chain exchange algorithms. We evaluate this algorithm using a rich dataset of kidney patients in France. Counterfactual simulations show that the Unpaired algorithm can match 57% of the patients, with an average waiting time of 440 days (state-of-the-art algorithms match about 34% with an average waiting time of 695 days). The optimal algorithm, which is practically infeasible, performs only slightly better: it matches 58% of the patients and leads to an average waiting time of 426 days. The Unpaired algorithm confronts two incentive-related practical challenges. We address those challenges via a modified version of the Unpaired algorithm that employs kidneys from the deceased donors waiting list. It can match 86% of the patients, while reducing the average waiting time to about 155 days.

Self-organizing linked lists perform well for handling requests with strong locality. Non-blocking shared data structures are robust and reliable. We present the first non-blocking self-organizing linked list algorithm which supports linearizable search, insert and remove operations. The experimental results show that our algorithm performs well for long linked lists with high percentages of search operations.

Road detection is a key issue for autonomous driving in urban traffic. In this paper, after a brief overview of existing methods, we present a road-area detection algorithm based on color images. This algorithm is composed of two modules: boundaries are first estimated based on the intensity image and road areas are subsequently detected based on the full color image. In the first module, an edge image of the scene is analyzed to obtain the candidates for the left and right road borders and to delimit the area that will subsequently be used to compute the mean and variance of the Gaussian distribution, assumed to be obeyed by the color components of road surfaces. The second module effectively extracts the road area and reinforces boundaries that most appropriately fit the road-extraction result. The combination of these modules can overcome basic problems due to inaccuracies in edge detection based on the intensity image alone and due to the computational complexity of segmentation algorithms based on color images. Experimental results on real road scenes have substantiated the effectiveness of the proposed method.