https://www.math.aau.at/talks/84/pdf

In this talk, Megan Medeiros discusses the real-world application of Chicanx, Latinx, and Caribbean Studies through digital humanities with a special focus on the narratives of undocumented Americans. Her digital essay, “I am Undocumented”: Mediation & Self-Mediation in Undocumented Narratives analyzes the mediation and bias present in a generically diverse collection of narratives to contextualize the narratives within their genres and subgenres. The multi-media essay strives to resist imposed, harmful monolithic narratives on the Chicanx, Latinx, and undocumented communities by emphasizing the agenda present in mainstream media. In addition to her own project, Medeiros highlights several other digital humanities projects that are bridging the gap between academia and community and bringing Chicanx, Latinx, and Caribbean Studies into the public sphere.

Injustice images are photographs or video, which depict situations of injustice, usually in the form of violent encounters between authorities and citizens. Some of the most conspicuous examples from recent years are the deaths of Eric Garner and George Floyd. The category is, however, much broader and thus in need of further definition. In this presentation, I present a typology of injustice images. I argue that such a typology has implications for the way we can theorize these images, as well as for how we approach them from a methodological point of view.

Content delivery networks (CDNs) distribute much of the Internet content by caching and serving the objects requested by users. A major goal of a CDN is to maximize the hit rates of its caches, thereby enabling faster content downloads to the users. Content caching involves two components: an admission algorithm to decide whether to cache an object and an eviction algorithm to decide which object to evict from the cache when it is full. In this paper, we focus on cache admission and propose an algorithm called RL-Cache that uses model-free reinforcement learning (RL) to decide whether or not to admit a requested object into the CDN’s cache. Unlike prior approaches that use a small set of criteria for decision making, RL-Cache weights a large set of features that include the object size, recency, and frequency of access. We develop a publicly available implementation of RL-Cache and perform an evaluation using production traces for the image, video, and web traffic classes from Akamai’s CDN. The evaluation shows that RL-Cache improves the hit rate in comparison with the state of the art and imposes only a modest resource overhead on the CDN servers. Further, RL-Cache is robust enough that it can be trained in one location and executed on request traces of the same or different traffic classes in other locations of the same geographic region.