Simulations of the Aquila Rift show uneven clumps accreting gas and merging along filaments to form a fractal cluster whose velocity anisotropies, rotation, and expansion record the assembly history even after gas removal.
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EnergyTrackr detects statistically significant energy regressions in Java commits from 3,232 changes across three projects and identifies recurring code anti-patterns such as missing early exits.
Energy production data falsifies the Kardashev 1% exponential model and yields an unphysical 1.6E15-year timescale to solar luminosity; the proposed KSN renormalization B(t) = P(t)/H(t) using Bitcoin hashrate spans 14 orders of magnitude from 2009-2024.
A reproducible grid-based pipeline converts Austin e-scooter trips into spatiotemporal demand images; a correlation-plus-error method plus ablation study on UNET selects temporal inputs that cut next-hour MSE by up to 37% and next-24-hour MSE by up to 35% versus simple baselines.
AI-generated UI prototypes rate high on usability and efficiency but low on originality and innovation compared to human designs.
citing papers explorer
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Simulating Star Formation and Star Cluster Assembly in the Aquila Rift Using Archival Observations
Simulations of the Aquila Rift show uneven clumps accreting gas and merging along filaments to form a fractal cluster whose velocity anisotropies, rotation, and expansion record the assembly history even after gas removal.
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Systematic Detection of Energy Regression and Corresponding Code Patterns in Java Projects
EnergyTrackr detects statistically significant energy regressions in Java commits from 3,232 changes across three projects and identifies recurring code anti-patterns such as missing early exits.
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Kardashev's Conundrum: Statistical Falsification of the Standard Kardashev Model and the Kardashev--Sagan--Nakamoto Resolution
Energy production data falsifies the Kardashev 1% exponential model and yields an unphysical 1.6E15-year timescale to solar luminosity; the proposed KSN renormalization B(t) = P(t)/H(t) using Bitcoin hashrate spans 14 orders of magnitude from 2009-2024.
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A Grid-Based Framework for E-Scooter Demand Representation and Temporal Input Design for Deep Learning: Evidence from Austin, Texas
A reproducible grid-based pipeline converts Austin e-scooter trips into spatiotemporal demand images; a correlation-plus-error method plus ablation study on UNET selects temporal inputs that cut next-hour MSE by up to 37% and next-24-hour MSE by up to 35% versus simple baselines.
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Usable but Conventional: An Empirical Study on the UX of AI-Generated Interface Prototypes
AI-generated UI prototypes rate high on usability and efficiency but low on originality and innovation compared to human designs.