arXiv:2507.05216v3 Announce Type: replace-cross Abstract: Many automated decision systems (ADS) are designed to solve prediction problems — where the goal is to learn patterns from a sample of the population and apply them to individuals from the same population. In reality, these prediction systems operationalize holistic policy interventions in deployment. Once deployed, ADS can shape impacted population outcomes through an effective policy change in how decision-makers operate, while also being defined by past and present interactions between stakeholders […]

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In this month’s episode of Functional Futures, our guest is David Christiansen, the Executive Director of the Haskell Foundation, a contributor to a number of dependently typed languages, and a dependent type advocate that has managed to introduce many people to the topic today through his work, talks, and texts. We cover topics such as dependent types, theorem proving, metaprogramming, and many more. We also discuss the book he co-authored with Daniel P. Friedman, The Little Typer, and […]

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Hey hackers!  If you’ve ever wondered why some of your submissions take a while before they get published, it’s probably because you’re submitting incomplete articles.  While most contributors focus on the content and the headline, we often receive submissions that are missing their meta, TL;dr, and keyword sections. Worse yet, sometimes contributors will add some keywords but not maximize the space for some of that SEO goodness.  As we start the new year, the editorial team thinks it’s […]

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arXiv:2604.13045v1 Announce Type: new Abstract: Natural Language to MongoDB Query Language (NL2MQL) is essential for democratizing access to modern document-centric databases. Unlike Text-to-SQL, NL2MQL faces unique challenges from MQL’s procedural aggregation pipelines, deeply nested schemas, and ambiguous value grounding. Existing approaches use static prompting or one-shot refinement, which inadequately model these complex contexts and fail to systematically leverage execution feedback for persistent improvement. We propose EvoMQL, a self-evolved framework that unifies evidence-grounded context construction with execution-driven learning through […]

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arXiv:2602.07147v1 Announce Type: new Abstract: Teaching microservice architectures is challenging due to distributed complexity and the gap between academia and industry. Understanding the quality issues students introduce in MSAs is essential to improve education. This study analyzes student-developed microservices using an established anti-pattern taxonomy and derives lessons learned with actionable teaching recommendations. We conducted a longitudinal, project-based course (2023-2025) involving 216 Master’s students (67 teams) who designed and deployed a realistic, containerized MSA for a gamified testing platform. […]

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arXiv:2605.12506v1 Announce Type: new Abstract: Realizing on-device ML-based gesture detection under tight real-time performance, energy and memory constraints is challenging, especially when considering mobile devices with varying battery-power levels. Existing EdgeAI deployments typically rely on a single fixed detector, limiting optimization opportunities. We present Scale-Gest, a novel run-time adaptive gesture detection framework that expands the detector space into a dense family of tiny-YOLO architectures. We introduce multiple novel device-calibrated ACE (Accuracy-Complexity-Energy) profiles by analyzing different model-resolution-stride operating points. […]

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A practitioner’s deep dive into whether CNN progress came from better architecture or better scaling and training. 1. The Wrong Question We Keep Asking Here’s something I kept running into when benchmarking models for a production pipeline: swap ResNet18 for ConvNeXt-Tiny, train both with identical hyperparameters, and ConvNeXt wins — but only by a slim margin. Then update the training recipe (AdamW, longer schedule, stronger augmentation), and suddenly ResNet itself closes much of that gap. That observation led me down a rabbit […]

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