First Record of a Collapsed Dorsal Fin in a White-beaked Dolphin Lagenorhynchus albirostris, with a Gunshot Wound as a Possible Cause

Collapsed dorsal fins are rare in odontocete cetaceans, having been reported for only a few species. We present the first known case in a White-beaked Dolphin (Lagenorhynchus albirostris Gray, 1846), photographed off northern Newfoundland, Canada in September 2004. The animal also had a wound on the right side of its body, anterior to the dorsal fin, with an estimated average diameter of 20-37 mm. We consider this to be a gunshot wound, most likely a 12-gauge rifled slug. The dolphin appeared to be healthy and with no movement problems, and what was apparently the same animal was seen in the same area on several dates during 2005. There is a long history of hunting small cetaceans off the Labrador coast, and a gunshot wound is the most likely cause of the wound observed. The wound may have caused the dorsal fin to collapse, as noted in other dolphin species

Record Linkage Reconciliation of Arlington Department of Human Services Administrative Data Using Potts Models

Situated at the nexus of federal, state, and local governments, the Arlington Department of Human Services (DHS) receives service utilization data from a multitude of different sources. Because of their “no wrong door” policy, customers can sign up for any DHS service from any DHS department. A practical consequence of this is that a single person can appear as multiple records from multiple databases with no unambiguous key between these records. Merging these records requires a probabilistic linkage approach. Classical approaches to record linkage, such as the method of Felligi and Sunter, consider each possible pair of records between databases and assigning link probabilities to each one. A drawback of considering pairwise links alone is that sometimes the transitive nature of links is violated. In order to better handle such information clashes, we propose a Bayesian linkage method that considers a large set of possible pairs at once. At the heart of this approach is a Potts model representation that tracks which records are assigned to the same individual. This allows us to assign probabilities to the various reconciliations of inconsistent linkage assignments

Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information

Predicting when and where events will occur in cities, like taxi pick-ups, crimes, and vehicle collisions, is a challenging and important problem with many applications in fields such as urban planning, transportation optimization and location-based marketing. Though many point processes have been proposed to model events in a continuous spatio-temporal space, none of them allow for the consideration of the rich contextual factors that affect event occurrence, such as weather, social activities, geographical characteristics, and traffic. In this paper, we propose \textsf{DMPP} (Deep Mixture Point Processes), a point process model for predicting spatio-temporal events with the use of rich contextual information; a key advance is its incorporation of the heterogeneous and high-dimensional context available in image and text data. Specifically, we design the intensity of our point process model as a mixture of kernels, where the mixture weights are modeled by a deep neural network. This formulation allows us to automatically learn the complex nonlinear effects of the contextual factors on event occurrence. At the same time, this formulation makes analytical integration over the intensity, which is required for point process estimation, tractable. We use real-world data sets from different domains to demonstrate that DMPP has better predictive performance than existing methods.Comment: KDD 1