Maria Alejandra Ttl Model =link= 📌 🎉

Within a month, she was the face of three brands: a cybernetic limb company, a zero-gravity swimwear line, and a music label that only released songs composed by dying AI.

| Resource | Type | Link / How to Access | |----------|------|----------------------| | – “A Stochastic TTL Model for Heterogeneous Networks” | IEEE/ACM journal article (open‑access) | https://doi.org/10.1109/TNET.2022.3158741 | | Supplementary material – full derivations, proofs, and extra figures | PDF (author‑provided) | https://arxiv.org/abs/2207.09834 (also hosted on arXiv) | | Reference implementation – ttl-model Python package (v0.3) | PyPI + GitHub | pip install ttl-model → https://github.com/maria-alejandra/ttl-model | | Data set – Traces from a campus‑wide IPv6 testbed (used for validation) | CSV (≈ 2 GB) | https://doi.org/10.5281/zenodo.7771234 | | Presentation slides – 2022 IEEE INFOCOM talk (15 min) | PDF | https://www.ieee.org/conferences/info2022/alejandra_slides.pdf | | Cite‑ready BibTeX | Bibliographic entry | @articlegomez2022stochastic, title=A Stochastic TTL Model for Heterogeneous Networks, author=Gómez‑López, María Alejandra and Mendoza, Jorge R., journal=IEEE/ACM Transactions on Networking, year=2022, volume=30, number=4, pages=1792–1805, doi=10.1109/TNET.2022.3158741 | maria alejandra ttl model

The video went viral in six hours.

# Return the empirical survival curve (sorted times + survival prob) sorted_t = np.sort(death_time) surv = 1 - np.arange(1, n_rep+1) / n_rep return sorted_t, surv Within a month, she was the face of