We study dynamic matching in a spatial setting. Drivers are distributed at random on some interval. Riders arrive in some (possibly adversarial) order at randomly drawn points. The platform observes the location of the drivers and can match newly arrived riders immediately or can wait for more riders to arrive. Unmatched riders incur a waiting cost of c per period. Furthermore, the platform can match riders and drivers irrevocably, and the cost of matching a driver to a rider is equal to the distance between them.

Abstract Visual object tracking has been a fundamental topic in computer vision and many convolutional neural network (CNN) based trackers proposed in recent years have achieved state‐of‐the‐art performance, multi‐domain convolutional neural network (MDNet) is one of the most representative CNN‐based algorithms with high performance. In order to significantly improve the robustness and accuracy of the MDNet algorithm, a multi‐domain convolutional neural network based on spatial self‐attention information learning (SAMDNet) is proposed in this study. The authors use the spatial self‐attention module for the spatial information learning of the model. The spatial self‐attention module selectively aggregates the feature at each position by a weighted sum of the features at all positions. Under the control of self‐learning parameters in this module, spatial attention information can be flexibly acquired. The authors also propose a novel interval loss term to solve the problem of different classes with the same semantics in the training data. Finally, an anomaly detection module is carefully designed for relocation after the algorithm completely lost the target. Extensive experiments on the object tracking benchmark (OTB) and the visual object tracking challenge (VOT) benchmarks show that the proposed tracker outperforming most of the state‐of‐art trackers.

In many real-world resource allocation problems, optimization is computationally intractable, so any practical allocation mechanism must be based on an approximation algorithm. We study investment incentives in strategy-proof mechanisms that use such approximations. In sharp contrast with the Vickrey-Clark-Groves mechanism, for which individual returns on investments are aligned with social welfare, we find that some algorithms that approximate efficient allocation arbitrarily well can nevertheless create misaligned investment incentives that lead to arbitrarily bad overall outcomes. However, if a near-efficient algorithm "excludes bossy negative externalities," then its outcomes remain near-efficient even after accounting for investments. A weakening of this "XBONE" condition is necessary and sufficient for the result.

Consider an extensive-form mechanism, run by an auctioneer who communicates sequentially and privately with agents. Suppose the auctioneer can make any deviation that no single agent can detect. We study the mechanisms such that it is incentive-compatible for the auctioneer not to deviate - the credible mechanisms. Consider the optimal auctions in which only winners make transfers. The first-price auction is the unique credible static mechanism. The ascending auction is the unique credible strategy-proof mechanism.

We introduce a simple benchmark model of dynamic matching in networked markets, where agents arrive and depart stochastically and the network of acceptable transactions between agents forms a random graph. We analyze our model from three perspectives: waiting time, optimization, and information. The main insight of our analysis is that waiting to thicken the market can be substantially more important than increasing the speed of transactions, and this is quite robust to the presence of waiting costs. From an optimization perspective, naive local algorithms, that choose the right time to match agents but do not exploit global network structure, can perform very close to optimal algorithms. From an information perspective, algorithms that employ even partial information on agents' departure times perform substantially better than those that lack such information. Information and waiting are complements; information about departure times is necessary for waiting to yield large gains. To elicit agents' departure times, we design an incentive-compatible continuous-time dynamic mechanism without transfers. LINK: www.ssrn.com/abstract=2394319