arXiv subjects

Ruhila Goswami

Publications and source records attributed to Ruhila Goswami.

2 recordsLinked to original sources

arXiv2026

rsx: A high-performance streaming toolkit for RAD-seq sex determination

Background Restriction site-associated DNA sequencing (RAD-seq) is widely used to discover sex-linked markers in non-model organisms, and RADSex provides the reference workflow for building marker-by-individual depth tables and testing sex-biased marker distributions. Its table-building commands grow memory-hungry as panels reach millions of RAD tags, it reports frequentist calls with no posterior evidence, and it offers no Python or C interface. Results rsx is a Rust implementation of the complete RADSex command set that preserves marker-table semantics and command-line compatibility. It combines 2-bit DNA keys, parallel ingestion, memory-mapped tables, external sorting, bitset group counts and a streamed Gram matrix so that writable allocations stay bounded by the number of individuals or by an explicit buffer, with false-discovery-rate ranking the one deliberate exception. Conjugate Beta-Binomial Bayes factors and directional posteriors grade each marker as a strict call, a posterior-supported hypothesis or a Bayes-factor-only row, and an optional CUDA backend batches the per-marker arithmetic on the GPU. On four published RAD-seq panels comprising 41.9 billion sequenced bases, rsx reproduced the RADSex v1.2.0 calls, recovered every Bonferroni-significant positive-control marker, and was 8.38-fold faster in geometric mean across 56 paired timings; the CUDA backend adds up to 29.86-fold on the p-value batch. Python and C bindings drive the same core from notebooks and pipelines. Conclusions rsx is an allocation-bounded, statistically extended replacement for RADSex that stays backward-compatible and reports its evidence in explicit grades. It is released under the GPL-3.0-or-later licence, with a reproducibility archive covering every reported number.

q-bio.GN↗

arXiv2026

Typed Component Algebras for Simulated Annealing and Markov-Chain Monte Carlo

Simulated annealing (SA) and fixed-temperature Markov-chain Monte Carlo (MCMC) run the same Metropolis-Hastings kernel over a tempered objective, but the variants appear as separate monolithic drivers, so improving one ingredient requires rewriting and re-verifying a whole solver. The shared kernel becomes a typed algebra of five components (objective, cooling schedule, neighborhood, move kernel, and acceptance rule) whose four local composition laws the construction checks; a single Sampler step then runs any point of the algebra. A surrogate proposal, a fitted generalized-Langevin thermostat, a quasi-Monte Carlo polish, or a noise-aware acceptance rule is implemented once and becomes available to every classical, fast, generalized, Hamiltonian, or parallel-tempered driver that shares the interface. The same typing carries the correctness artifacts: SymPy-checked reductions of Generalized SA to its Boltzmann, fast, and Metropolis limits (the reductions surfaced a sign error that had stood in the visiting-distribution literature for three decades); a TLA+ specification model-checked for four safety and two liveness properties; and a three-channel finite-precision audit showing that fixing one channel of the acceptance path does not let float16 reproduce float64 basin selection. The implementation is the open-source Rust-and-Python package anneal, with an Array-API/DLPack device boundary and a portfolio optimizer whose only argument is a budget. On the CUTEst collection under a shared work-unit budget it reaches the best observed basin on more problems than a budget-matched CMA-ES restart heuristic, while carrying the almost-sure convergence and regret guarantees that heuristic lacks. Every reported number and figure regenerates from the reproducibility package with its pinned environment.

cs.SE↗