# Survival analysis

Patient-derived xenografts (PDX) implant human tumors into immunocompromised mice to test drugs, but screening every tumor-drug combination is infeasible in time and cost. GrAFT-PDX is a Bayesian model that jointly models tumor growth trajectories and mouse death risk, using a low-rank tensor decomposition to share information across PDX lines, treatments and time, so it can impute untested experiments and predict death times. On the large PDXe screen it assigns higher log probability to death times than Royston-Parmar and other survival baselines, matches strong baselines on tumor response, and preserves known gene-drug associations without genomic input.

Patient-derived xenografts (PDX) implant human tumors into immunocompromised mice to test drugs, but screening every tumor-drug combination is infeasible in time and cost. GrAFT-PDX is a Bayesian model that jointly models tumor growth trajectories and mouse death risk, using a low-rank tensor decomposition to share information across PDX lines, treatments and time, so it can impute untested experiments and predict death times. On the large PDXe screen it assigns higher log probability to death times than Royston-Parmar and other survival baselines, matches strong baselines on tumor response, and preserves known gene-drug associations without genomic input.

A laboratory mouse's age can be read from an ordinary photograph, but what transfers is relative age rather than absolute age. Across 2,846 photographs of 1,292 mice from seven facilities, a frozen DINOv2 vision model with ridge regression predicted age; on unseen facilities the median absolute error was 138 days, yet within a session the older of two mice was ranked older in 65% of pairs, rising above 95% for age gaps over 500 days. Mice predicted older than their age were frailer, with each 100 days of photographic age gap linked to 0.12 SD more frailty and a 14% higher hazard of death.

A laboratory mouse's age can be read from an ordinary photograph, but what transfers is relative age rather than absolute age. Across 2,846 photographs of 1,292 mice from seven facilities, a frozen DINOv2 vision model with ridge regression predicted age; on unseen facilities the median absolute error was 138 days, yet within a session the older of two mice was ranked older in 65% of pairs, rising above 95% for age gaps over 500 days. Mice predicted older than their age were frailer, with each 100 days of photographic age gap linked to 0.12 SD more frailty and a 14% higher hazard of death.

Using XMap to probe port 11434 across the full IPv4 space daily from 2025-02-14 to 2026-02-13 over 362 observed days, a Nankai University team found 152,137 cumulative exposed Ollama endpoints, with daily alive endpoints rising from 10,473 to 16,059 (+53.3%), 26.4% of IPs appearing on a single day, only 0.43%–2.90% of below-fix IPs upgrading in place across five CVE cutoffs, the top five countries/regions holding about 70% of weighted observations, and the top five ASNs all cloud or hosting providers, complemented by PTR and port-443 TLS probing of operational characteristics.

Using XMap to probe port 11434 across the full IPv4 space daily from 2025-02-14 to 2026-02-13 over 362 observed days, a Nankai University team found 152,137 cumulative exposed Ollama endpoints, with daily alive endpoints rising from 10,473 to 16,059 (+53.3%), 26.4% of IPs appearing on a single day, only 0.43%–2.90% of below-fix IPs upgrading in place across five CVE cutoffs, the top five countries/regions holding about 70% of weighted observations, and the top five ASNs all cloud or hosting providers, complemented by PTR and port-443 TLS probing of operational characteristics.

Analyzing the intrinsic difficulty of delay detection across 14 public event logs, the study finds that remaining times are strongly right-skewed, that existing LSTM models capture the mode of the distribution but perform poorly on high-delay cases, and that prediction error and prediction-interval width both grow with true remaining time (positive in 10 of 14 logs); it then evaluates imbalanced regression approaches (SMOGN resampling, CSW, BMSE, EAL, SERA) with only limited benefit, and instead feeds uncertainty features from a survival model (prediction standard deviation, 80% and 90% prediction-interval widths, tail mass, and temporal context) into a CatBoost classifier for binary delay detection, raising average recall from 0.21 to 0.61 at the q=0.

Analyzing the intrinsic difficulty of delay detection across 14 public event logs, the study finds that remaining times are strongly right-skewed, that existing LSTM models capture the mode of the distribution but perform poorly on high-delay cases, and that prediction error and prediction-interval width both grow with true remaining time (positive in 10 of 14 logs); it then evaluates imbalanced regression approaches (SMOGN resampling, CSW, BMSE, EAL, SERA) with only limited benefit, and instead feeds uncertainty features from a survival model (prediction standard deviation, 80% and 90% prediction-interval widths, tail mass, and temporal context) into a CatBoost classifier for binary delay detection, raising average recall from 0.21 to 0.61 at the q=0.

The work presents CleanSurvival, a reinforcement-learning (Q-learning) based automated data-preprocessing framework tailored to time-to-event (survival analysis) models for censored data: it selects among combinations of data imputation, outlier detection, and feature extraction techniques to optimize performance for a Cox, random forest, neural network, or user-supplied time-to-event model; on real-world dataset benchmarks, this Q-learning-based preprocessing improved predictive performance relative to simple baselines, runtime behavior was condition-dependent and most clearly interpretable in the best-covered benchmark cells, and a simulation study showed effectiveness across different types and levels of missingness and noise.

The work presents CleanSurvival, a reinforcement-learning (Q-learning) based automated data-preprocessing framework tailored to time-to-event (survival analysis) models for censored data: it selects among combinations of data imputation, outlier detection, and feature extraction techniques to optimize performance for a Cox, random forest, neural network, or user-supplied time-to-event model; on real-world dataset benchmarks, this Q-learning-based preprocessing improved predictive performance relative to simple baselines, runtime behavior was condition-dependent and most clearly interpretable in the best-covered benchmark cells, and a simulation study showed effectiveness across different types and levels of missingness and noise.