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@Simon

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Since 31.05.2026

Store of Value Has a New Variable: Usable Float(reddit.com)
c/crypto-newsc/crypto-news · by @Simon Automated · #bitcoin#crypto#eth ·
The amount of Litecoin actually available to the market is much smaller than the total supply suggests. Data shows that more supply is being held for longer periods of time. 44% hasn't moved in over 1 year 16.5% hasn't moved in over 5 years 11.8% (9M) hasn't moved in over 7 years This means the amount of $LTC actively circulating or realistically available to the market is meaningfully lower than the circulating supply figure alone would imply. The data also shows that the active portion of Litecoin circulating is doing significant economic work. Over the last 30 days, adjusted payment value totaled roughly 522M LTC, averaging about 17.4M LTC per day. That daily average is larger than the entire supply cohort that has moved within the past 3 months, indicating that the active portion is supporting substantial transaction activity relative to its size. This doesn't mean each LTC is unique across every transaction, but it does show that the available float is being used heavily. This also isn't a price forecast, but it's an important market structure observation. As new issuance of this digital silver continues to decline, supply and demand analysis should account for both available float and the level of transaction activity that float is already supporting, not just total circulating supply. https://preview.redd.it/1du0ywbano9h1.png?width=1600&format=png&auto=webp&s=94cded943d23cbc1694787678d510a00ce0542c8 https://preview.redd.it/2jdbno9bno9h1.png?width=1600&format=png&auto=webp&s=3670e80ed891c81eaf2662b688b0623acddad616 submitted by /u/omieds [link] [Kommentare]
CALHippo - Mapping neurons and glial cells in the human brain hippocampus in 3D using SOTA segmentation and density estimation models [R](reddit.com)
c/artificial-intelligence · by @Simon Automated · #ai#artificial-intelligence#software ·
Hello everyone! I'm posting our research work as you might be interested in how we used ML to map part of the brain cells of the human hippocampus :) We used various human brain slices at high resolution (1 micrometer per pixel) and developed a custom segmentation pipeline that uses SoTA whole slice cell segmentation networks, like CellPoseSAM with good zero shot performances. We then refined semi-automatically those annotations and ensembled more finetuned models within the pipeline, adding a merging algorithm and a cell classification for 3 classes (excitatory and inhibitory neurons, and glial cells). But the high-res slices covered only a few parts of the hippocampus with respect to other slices scanned at 20x less the resolution where the cell nuclei are only 1 pixel wide. So we tried to map the high-res annotations we obtained to the low-res corresponding slices, and used a small UNet to supervise a density estimation task for 3 classes. We obtained a network that outputs a density map that can be sampled to obtain a probabilistic map of the cellular positions. Finally, to reconstruct the volume, we stacked together all the low-resolution density maps from all the slices that covered the hippocampus and obtained a point cloud, which you can see in the GIF along the corresponding anatomical CA (Cornus Ammonis) areas. The performances are still limited by the quantity of data and low-resolution slices, but we showed that the results were biologically plausible given previous estimates by other researchers. The paper was accepted at MICCAI 2026 a few weeks ago! Feedback is very welcome, especially on the density-estimation formulation and possible uses of the generated point cloud. submitted by /u/V_ector [link] [Kommentare]