Agritech Startup MazaoHub Raises $2M Pre-Seed to Scale AI-Driven, Climate-Smart Farming in Tanzania - Tech In Africa
<a href="https://news.google.com/rss/articles/CBMixgFBVV95cUxOQkVqUVphUGlubDhxY2ZrNnBNWWRXMmI5eUx4SHNQYVd6ME9aSlhvUDFGRzJnbXNoRVZyekhaYkRuRFdsay1uZnRyVmVvajBUeFo4al9yV0h5REFkQUNmZV9SaTRvZ2cxVl85ZUhJRnFqd19YV0d0ek1Ub1FmSVhWb2g2S0RteUlqY0g2dDZWdkczX0FUWWp3TGVOYTUzeGF6RFdDTEdQTkJubk9SYTE5RHR1bU5JQlh0anRwSmpnekE0OGRhZkE?oc=5" target="_blank">Agritech Startup MazaoHub Raises $2M Pre-Seed to Scale AI-Driven, Climate-Smart Farming in Tanzania</a> <font color="#6f6f6f">Tech In Africa</font>
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Approximating Analytically-Intractable Likelihood Densities with Deterministic Arithmetic for Optimal Particle Filtering
arXiv:2512.01023v3 Announce Type: replace-cross Abstract: Particle filtering algorithms have enabled practical solutions to problems in autonomous robotics (self-driving cars, UAVs, warehouse robots), target tracking, and econometrics, with further applications in speech processing and medicine (patient monitoring). Yet, their inherent weakness at representing the likelihood of the observation (which often leads to particle degeneracy) remains unaddressed for real-time resource-constrained systems. Improvements such as the optimal proposal and auxiliary particle filter mitigate this issue under specific circumstances and with increased computational cost. This work presents a new particle filtering method and its implementation, which enables tunably-approximative representation of arbitra




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