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In this paper we introduce a framework that utilizes an architecture based on the Tsetlin Machine to output explain- able rules for the prediction of political violence. The framework includes a data processing pipeline, modeling architecture, and visualization tools for early warning about notable events. We conducted an experimental study to explain and predict a one of the most notable events, - a civil war. We observed that the rules that we produced are consistent with theories that emphasize the continuing risks that accumulate from a history of conflict as well as the stickiness of civil war. 

The Diversity, Equity and Inclusion (DEI) initiative started as the Diversity/Inclusion initiative in 2020 [4]. The current report summarizes our activities in 2023. 

Recent studies in urban navigation have revealed new demands (e.g., diversity, safety, happiness, serendipity) for the navigation services that are critical to providing useful recommendations to travelers. This exposes the need to design next-generation navigation services that accommodate these newly emerging aspects. In this paper, we present a prototype system, namely, EPUI (an Experimental Platform of Urban Informatics), which provides a testbed for exploring and evaluating venues and route recommendation solutions that balance between different objectives (i.e., demands) including the newly discovered ones. In addition, EPUI incorporates a modularized design, enabling researchers to upload their own algorithms and compare them to well-known algorithms using different performance metrics. Its user interface makes it easily usable by both end-user and experienced researchers. 

The increasing popularity and ubiquity of various large graph datasets has caused renewed interest for graph partitioning. Existing graph partitioners either scale poorly against large graphs or disregard the impact of the underlying hardware topology. A few solutions have shown that the nonuniform network communication costs may affect the performance greatly. However, none of them considers the impact of resource contention on the memory subsystems (e.g., LLC and Memory Controller) of modern multicore clusters. They all neglect the fact that the bandwidth of modern high-speed networks (e.g., Infiniband) has become comparable to that of the memory subsystems. In this paper, we provide an in-depth analysis, both theoretically and experimentally, on the contention issue for distributed workloads. We found that the slowdown caused by the contention can be as high as 11x. We then design an architecture-aware graph partitioner, ARGO , to allow the full use of all cores of multicore machines without suffering from either the contention or the communication heterogeneity issue. Our experimental study showed (1) the effectiveness of ARGO , achieving up to 12x speedups on three classic workloads: Breadth First Search, Single Source Shortest Path, and PageRank; and (2) the scalability of ARGO in terms of both graph size and the number of partitions on two billion-edge real-world graphs.