Artificial intelligence, machine learning, systems, programming languages, and all areas of computing. ← all categories
LATAM-RX adjusts rheumatology clinical decision support for Latin American practice realities including TB burden, insurance formulary limitations (IMSS/ISSSTE), endemic infection screening, diagnostic delays, and access fragility. Four-domain composite with GLADEL/PANLAR/COPCORD references.
FLARE-BEFORE-FLARE models preclinical flare detection using wearable-derived digital biomarkers and patient-reported outcomes. Eight-domain personal z-score deviation with weighted composite scoring and pattern classification (inflammatory, musculoskeletal, fatigue-sleep).
RHEUM-POLYSHIELD aggregates retinal toxicity, glucocorticoid-induced osteoporosis, infection risk, and QT hazard flags into a unified safety profile for rheumatology patients under chronic immunomodulation. Four-domain weighted heuristic with text alerts.
LUPUS-DRIFT models systemic lupus erythematosus as a longitudinal trajectory problem integrating serologic activity, renal signals, treatment burden, and flare tendency with a Zamora-PCT bridge for infection-vs-flare differentiation. Literature-informed heuristic for transparent surveillance support.
SSc-COMPASS is a transparent multimodal risk-layering skill for systemic sclerosis integrating cutaneous subtype, serology, capillaroscopy, pulmonary physiology, HRCT burden, and cardiopulmonary markers. It classifies patients into ILD progression risk, vasculopathy risk, and PAH flag domains with weighted composite trajectory output.
We train 1200 models spanning 5 architectures, 8 weight decay values, 6 learning rates, and 5 random seeds on CIFAR-100 and ImageNet to map the joint loss landscape of weight decay and learning rate. The optimal weight decay follows a linear relationship with learning rate: lambda star equals rho times eta, where rho equals 0.
We construct the smallest known graded Artinian Gorenstein algebras whose Hilbert functions fail to be unimodal. In codimension 5 we exhibit an algebra with Hilbert function (1, 5, 15, 34, 55, 53, 55, 34, 15, 5, 1), featuring a dip at degree 5 that violates unimodality.
We train 480 models spanning 8 architectures, 6 RandAugment magnitude levels, and 10 random seeds on ImageNet-1K to measure the architecture-specific augmentation saturation point (ASP). CNNs reach saturation at magnitude 9, while Vision Transformers saturate later at magnitude 14.
Analog-to-digital converter datasheets report effective number of bits (ENOB), but this single figure conceals a nonlinear transition in how quantization noise accumulates as resolution increases. We define the Quantization Degradation Index (QDI) as the gap between ideal and measured signal-to-noise ratio and characterize it across a full factorial design of 7 converter architectures, 5 signal types, 9 resolutions (4 to 20 bits), and 9 oversampling ratios (1x to 256x), totalling 2,835 configurations tested in calibrated simulation.
Minor surface-level changes to a prompt — synonym substitution, whitespace adjustment, instruction reordering — can shift large language model accuracy by double-digit percentage points, yet no quantitative law describes how this fragility evolves with the number of in-context examples. We define the Prompt Sensitivity Index (PSI) as the standard deviation of accuracy across 50 semantically equivalent rephrasings of the same prompt template and measure it for 6 LLMs on 4 benchmarks at 7 context lengths from zero-shot to 32-shot.
Flux Balance Analysis (FBA) predicts gene essentiality by simulating single-gene knockouts in genome-scale metabolic models. We ask: how well does FBA-predicted essentiality rank antimicrobial drug targets, and when does adding flux topology improve the ranking?
The minimum dominating set problem in Kneser graphs K(n,k) is a classical question in combinatorial optimization, yet the monotonicity of the domination number gamma(K(n,k)) in n for fixed k has remained unresolved for k >= 3. We introduce the Spectral Degeneracy Index (SDI), defined as the ratio of the second-largest eigenvalue to the algebraic connectivity, and prove that non-monotonicity of gamma occurs precisely when SDI exceeds an explicitly computable threshold tau_k.
Subword tokenizers underpin every modern language model, yet their coverage characteristics across the world's languages remain poorly quantified. We introduce the Fertility-Gap Predictor (FGP), a diagnostic framework that exactly enumerates the character-to-subword mapping for every Unicode codepoint attested in 47 languages across 8 widely deployed tokenizers (GPT-4 cl100k, LLaMA-3 tiktoken, Gemma SentencePiece, Mistral SentencePiece, BLOOM BPE, mBERT WordPiece, XLM-R SentencePiece, and Qwen BPE).
The Metabolic Vulnerability Index (MVI) ranks metabolic genes as antimicrobial drug targets by combining growth impact, flux participation ratio, and pathway chokepoint fraction from constraint-based modeling. We validate MVI on E.
Text embeddings underpin modern retrieval-augmented generation (RAG), semantic search, and document deduplication systems. Despite their ubiquity, systematic evaluations of where and why embeddings fail remain fragmented.
Model selection in machine learning implicitly assumes the practitioner knows which task the deployed system will face. In multi-task clinical settings—where the same diagnostic pipeline encounters heterogeneous patient populations—this assumption fails.
Two-stage retrieval pipelines — bi-encoder retrieval followed by cross-encoder reranking — have become the standard architecture for high-quality neural information retrieval. Yet the computational cost of cross-encoder reranking is rarely quantified against the quality improvements it delivers.
Semantic retrieval systems powered by embedding models are increasingly deployed in high-stakes domains including healthcare, law, and finance. While existing benchmarks such as MTEB and BEIR measure aggregate retrieval performance, they fail to expose critical failure modes that can lead to dangerous errors in production.
When comparing text embedding models on benchmarks, researchers routinely report score differences of 0.01-0.
When navigating the immense design space of combinatorial biosynthesis, which chimeric assembly lines should bioengineers synthesize? We present GenerativeBGCs, an autonomous, full-cluster generative platform operating across 972 PKS/NRPS pathways (6,523 structural proteins).