AURA: Active-Response Attribution under Treatment Ambiguity in Bacterial Cytological Profiling

When a bacterial sample is exposed to several antibiotics, not every applied drug necessarily acts: if the organism is resistant to one of them, that drug leaves no morphological trace. The clinically meaningful quantity is therefore not which antibiotics were applied, but which ones were active. We show that these two are sharply decoupled in real E. coli microscopy - naively assuming the applied combination equals the active one is correct only about 37% of the time - yet existing computational tools are ill-suited to recovering the active set. Forward perturbation models such as scGen, CPA, and IMPA are designed to predict appearance from treatment, not the reverse, and inverting them degrades sharply; discriminative image classifiers tend to memorise strain- and batch-specific texture and fail to transfer across experimental replicates. We introduce AURA, which reframes the task as constrained, energy-based inverse attribution. Its central inductive bias is that the active set must be a subset of the applied set; this collapses the candidate space and lets AURA infer the active subset of applied antibiotics by decomposing residual morphology into antibiotic response atoms and selecting the subset with the lowest reconstruction energy, using no strain label at test time. AURA-E adds evidence-aware abstention, withholding a prediction when candidate explanations remain near-equally plausible. On cross-replicate transfer in an E. coli cytological profiling dataset, AURA recovers the active antibiotic combination with 95.47% exact-match accuracy.

Magnetic Field Aided Vehicle Localization with Acceleration Correction

This paper presents a novel approach for vehicle localization by leveraging the ambient magnetic field within a given environment. Our approach involves introducing a global mathematical function for magnetic field mapping, combined with Euclidean distance-based matching technique for accurately estimating vehicle position in suburban settings. The mathematical function based map structure ensures efficiency and scalability of the magnetic field map, while the batch processing based localization provides continuity in pose estimation. Additionally, we establish a bias estimation pipeline for an onboard accelerometer by utilizing the updated poses obtained through magnetic field matching. Our work aims to showcase the potential utility of magnetic fields as supplementary aids to existing localization methods, particularly beneficial in scenarios where Global Positioning System (GPS) signal is restricted or where cost-effective navigation systems are required.