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We study how open source communities describe participation and control through version controlled governance documents. Using a corpus of 710 projects with paired snapshots, we parse text into actors, rules, actions, and objects, then group them and measure change with entropy for evenness, richness for diversity, and Jensen Shannon divergence for drift. Projects define more roles and more actions over time, and these are distributed more evenly, while the composition of rules remains stable. These findings indicate that governance grows by expanding and balancing categories of participation without major shifts in prescriptive force. The analysis provides a reproducible baseline for evaluating whether future AI mediated workflows concentrate or redistribute authority. 

We introduce CREStE, a scalable learning-based mapless navigation framework to address the open-world generalization and robustness challenges of outdoor urban navigation. Key to achieving this is learning perceptual representations that generalize to open-set factors (e.g. novel semantic classes, terrains, dynamic entities) and inferring expert-aligned navigation costs from limited demonstrations. CREStE addresses both these issues, introducing 1) a visual foundation model (VFM) distillation objective for learning open-set structured bird's-eye-view perceptual representations, and 2) counterfactual inverse reinforcement learning (IRL), a novel active learning formulation that uses counterfactual trajectory demonstrations to reason about the most important cues when inferring navigation costs. We evaluate CREStE on the task of kilometer-scale mapless navigation in a variety of city, offroad, and residential environments and find that it outperforms all state-of-the-art approaches with 70% fewer human interventions, including a 2-kilometer mission in an unseen environment with just 1 intervention; showcasing its robustness and effectiveness for long-horizon mapless navigation. 

Ground penetrating radar (GPR) is a nondestructive tool for investigating the subsurface. When used in laboratory testing, large boxes to contain the test specimen aggregate or soil are necessary. However, boxes created for GPR testing have some unique requirements such that they do not interfere with the sensitive GPR equipment. This paper presents the design and construction of economical boxes for GPR testing. Key design requirements for this test box were: minimal use of metal, compatible with a wide frequency range from 300 MHz to 1.6 GHz, capable of specimen saturation, capable of efficiently breaking down the specimen, and can be used for the preparation of hundreds of test specimens.. The literature does not contain many examples of test setups that would fulfill these objectives nor does it include specific instructions on how to create a box to achieve them. This paper presents the final economical design for laboratory testing of aggregate using GPR. 

Abstract Optical spectrometers are essential tools for analysing light‒matter interactions, but conventional spectrometers can be complicated and bulky. Recently, efforts have been made to develop miniaturized spectrometers. However, it is challenging to overcome the trade-off between miniaturizing size and retaining performance. Here, we present a complementary metal oxide semiconductor image sensor-based miniature computational spectrometer using a plasmonic nanoparticles-in-cavity microfilter array. Size-controlled silver nanoparticles are directly printed into cavity-length-varying Fabry‒Pérot microcavities, which leverage strong coupling between the localized surface plasmon resonance of the silver nanoparticles and the Fabry‒Pérot microcavity to regulate the transmission spectra and realize large-scale arrayed spectrum-disparate microfilters. Supported by a machine learning-based training process, the miniature computational spectrometer uses artificial intelligence and was demonstrated to measure visible-light spectra at subnanometre resolution. The high scalability of the technological approaches shown here may facilitate the development of high-performance miniature optical spectrometers for extensive applications. 

This paper introduces the concept of Language- Guided World Models (LWMs)—probabilistic models that can simulate environments by read- ing texts. Agents equipped with these models provide humans with more extensive and effi- cient control, allowing them to simultaneously alter agent behaviors in multiple tasks via nat- ural verbal communication. In this work, we take initial steps in developing robust LWMs that can generalize to compositionally novel language descriptions. We design a challenging world modeling benchmark based on the game of MESSENGER (Hanjie et al., 2021), featuring evaluation settings that require varying degrees of compositional generalization. Our exper- iments reveal the lack of generalizability of the state-of-the-art Transformer model, as it of- fers marginal improvements in simulation qual- ity over a no-text baseline. We devise a more robust model by fusing the Transformer with the EMMA attention mechanism (Hanjie et al., 2021). Our model substantially outperforms the Transformer and approaches the perfor- mance of a model with an oracle semantic pars- ing and grounding capability. To demonstrate the practicality of this model in improving AI safety and transparency, we simulate a scenario in which the model enables an agent to present plans to a human before execution, and to re- vise plans based on their language feedback. 

This paper presents a non-resonant vibration energy harvester (VEH) optimized for 0.5-1.0 Hz at 0.2g acceleration, typically associated with human motion in daily activities. Different amounts of water-based and oil-based ferrofluids as liquid bearings have been studied in an experimental setup with a precisely controllable spacing between top and bottom coil plates where the magnet array and ferrofluid bearings reside. The sub-miniature VEH (1.4cc and 3.3gram) steadily generates voltages between 0.5-1.0 Hz and is measured to produce an open-circuit voltage of Vrms = 19.5 - 31.9 mV (or 0.33-0.89 μW into a match load) from 0.2g sub-Hz applied acceleration. The highest figure of merit (FOM) of the VEH at 0.2g at 1.0 Hz is 15.5 μW/cc/g2.