technocracy

Table of Links Abstract and 1. Introduction Methods Quantitative Results and Creativity Support Index Qualitative Results from Focus Group Discussions Discussion Mitigations and Conclusion and Acknowledgments Ethical Guidance References A. Related Work on Computational Humour, AI and Comedy B. Participant Questionaire C. Focus B PARTICIPANT QUESTIONNAIRE B.1 Your past experience with AI systems in general. Please answer based on your past experience, before this workshop. • Have you used AI systems to write comedy material in the past? […]

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arXiv:2604.18632v1 Announce Type: new Abstract: Stomata play a crucial role in regulating plant physiological processes and reflecting environmental responses. However, accurate and high-throughput stomatal phenotyping remains challenging, as conventional approaches rely on destructive sampling and manual annotation, restricting large-scale and field deployment. To overcome these limitations, a noninvasive restoration-detection integrated framework, termed StomaD2, is developed to achieve accurate and fast stomatal phenotyping under complex imaging conditions. The framework incorporates a diffusion-based restoration module to recover degraded images and […]

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arXiv:2603.26704v1 Announce Type: new Abstract: We investigate three distinct methods of incorporating all-sky imager (ASI) images into deep learning (DL) irradiance nowcasting. The first method relies on a convolutional neural network (CNN) to extract features directly from raw RGB images. The second method uses state-of-the-art algorithms to engineer 2D feature maps informed by domain knowledge, e.g., cloud segmentation, the cloud motion vector, solar position, and cloud base height. These feature maps are then passed to a CNN to […]

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Reconfigurable intelligent surfaces (RISs) modify signal reflections to enhance wireless communication capabilities. Classical RIS phase optimization is highly non convex and challenging in dynamic environments due to high interference and user mobility. Here we propose a hierarchical multi-objective quantum metalearning algorithm that switches among specific quantum paths based on historical success, energy cost, and current data rate. Candidate RIS control directions are arranged as switch paths between quantum neural network layers to minimize inference, and a scoring mechanism […]

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arXiv:2602.20366v1 Announce Type: new Abstract: In recent work, it has been shown that maintaining reliability in analog vector–matrix multipliers can be modeled as the following coding problem. Vectors in $mathbb{R}^k$ are encoded into codewords of a linear $[n,k,d]$ code $C$ over $mathbb{R}$. For prescribed positive reals $delta < Delta$, additive errors of magnitude at most $delta$ are tolerable and need no handling, yet outlying errors of magnitude greater than $Delta$ are to be located or detected. The trade-off […]

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Understanding the causal relationships between data variables can provide crucial insights into the construction of tabular datasets. Most existing causality learning methods typically focus on applying a single identifiable causal model, such as the Additive Noise Model (ANM) or the Linear non-Gaussian Acyclic Model (LiNGAM), to discover the dependencies exhibited in observational data. We improve on this approach by introducing a novel dual-step framework capable of performing both causal structure learning and tabular data synthesis under multiple causal […]

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arXiv:2605.06883v1 Announce Type: new Abstract: The Maximum Mean Discrepancy (MMD) is a cornerstone statistic for nonparametric two-sample testing, but its test power is dictated entirely by the chosen kernel. Because any fixed kernel inherently fails to distinguish certain distributions, the kernel must be dynamically optimized. However, data-driven optimization violates the foundational i.i.d. assumption, forcing a strict trade-off in existing frameworks. Ratio criteria ignore this dependence, inducing overfitting and variance collapse on rich kernel classes. Conversely, aggregation methods bypass […]

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