arXiv subjects

Maria Levchenko

Publications and source records attributed to Maria Levchenko.

6 recordsLinked to original sources

arXiv2026

Passive wearable physiology tracks a state-level material-hardship gradient in resting heart rate

Resting heart rate is an established marker of cardiovascular risk, but population-scale measurement has depended on clinical or survey instruments. We ask whether passively sensed consumer-wearable physiology recovers the socioeconomic gradient established in clinical cohorts. Using 19.1 million quality-filtered photoplethysmography readings from 18,734 opt-in users of the Welltory app, we computed cohort-adjusted mean daytime resting heart rate per US state and related it to a four-component state-level material-hardship composite (uninsurance, food insecurity, utility shutoff, housing insecurity; 41 states with module coverage, 12,497 contributing users). Adjusting for six state health and behaviour indicators, latitude, median age, and density, state resting heart rate tracked hardship at partial Spearman $\rho = +0.74$ (bootstrap 95\% CI $[+0.31, +0.87]$; $[+0.06, +0.79]$ under a conservative two-stage bootstrap that also resamples users within states). The gradient stayed positive under every check: leave-one-state-out ($+0.67$ to $+0.79$), demographic re-weighting, composition and census-region fixed effects ($+0.75$, $+0.78$), split-sample resampling (held-out median $+0.51$ to $+0.56$), and reduced variants such as a heavy-wear subsample ($+0.48$); residuals carry no detectable spatial autocorrelation. It was absent in two other metrics from the same panel and not matched by income or inequality under the same adjustment. Users in the five highest-hardship states averaged $+1.33$ bpm over those in the five lowest. Passive consumer-wearable physiology tracks, at the area level, a gradient previously established primarily in clinical and cohort studies, without surveying the users themselves. The composite was selected in exploratory analysis, after a pre-specified broader precarity index gave a weaker association ($+0.29$); the association is cross-sectional and ecological.

cs.CY↗

arXiv2026

How Surprising Is Historical Italian to Language Models? Tokenization Tax, Comprehension Tax, and a Simple Mitigation

Large language models (LLMs) are increasingly critical to digital library workflows, yet their ability to process historical language remains poorly understood. Historical difficulty is typically treated as a monolithic barrier, conflating orthographic variation, linguistic distance, and pretraining exposure. In this paper, we propose a diagnostic framework that decomposes this difficulty into four distinct dimensions: tokenization cost, predictive uncertainty (surprisal), semantic robustness, and context sensitivity. We evaluate this framework on three datasets spanning three centuries: (1) a newly curated corpus of 17th-century Italian texts (1610-1689) digitized from original page images; (2) canonical 19th-century Italian "I Promessi Sposi" serving as a high-exposure control; and (3) 18th-century Russian civil print books as a contrastive orthographic stress test. Our results reveal a distinct dissociation between encoding cost and comprehension. While Russian and early modern Italian incur comparable tokenization penalties (25-30% inflation), their predictive difficulty diverges sharply. 17th-century Italian is on average 2.4 times more surprising than its modern equivalent - with academic prose reaching 3.2 times - whereas Russian shows only a modest increase. But predictive uncertainty does not imply representational degradation: embedding similarity remains robust (> 0.85) across all datasets, confirming that models can represent historical meaning even when generation is unstable. Finally, we demonstrate that a minimal temporal context prompt reduces historical surprisal by approximately 60%, offering a simple, model-agnostic mitigation. These findings suggest that while historical text imposes a consistent encoding tax, digital libraries can safely deploy LLMs for semantic retrieval tasks, provided that generative applications are carefully adapted.

cs.CL↗

arXiv2025

Evaluating LLMs for Historical Document OCR: A Methodological Framework for Digital Humanities

Digital humanities scholars increasingly use Large Language Models for historical document digitization, yet lack appropriate evaluation frameworks for LLM-based OCR. Traditional metrics fail to capture temporal biases and period-specific errors crucial for historical corpus creation. We present an evaluation methodology for LLM-based historical OCR, addressing contamination risks and systematic biases in diplomatic transcription. Using 18th-century Russian Civil font texts, we introduce novel metrics including Historical Character Preservation Rate (HCPR) and Archaic Insertion Rate (AIR), alongside protocols for contamination control and stability testing. We evaluate 12 multimodal LLMs, finding that Gemini and Qwen models outperform traditional OCR while exhibiting over-historicization: inserting archaic characters from incorrect historical periods. Post-OCR correction degrades rather than improves performance. Our methodology provides digital humanities practitioners with guidelines for model selection and quality assessment in historical corpus digitization.

cs.CV↗

arXiv2025

Evaluating Named Entity Recognition Models for Russian Cultural News Texts: From BERT to LLM

This paper addresses the challenge of Named Entity Recognition (NER) for person names within the specialized domain of Russian news texts concerning cultural events. The study utilizes the unique SPbLitGuide dataset, a collection of event announcements from Saint Petersburg spanning 1999 to 2019. A comparative evaluation of diverse NER models is presented, encompassing established transformer-based architectures such as DeepPavlov, RoBERTa, and SpaCy, alongside recent Large Language Models (LLMs) including GPT-3.5, GPT-4, and GPT-4o. Key findings highlight the superior performance of GPT-4o when provided with specific prompting for JSON output, achieving an F1 score of 0.93. Furthermore, GPT-4 demonstrated the highest precision at 0.99. The research contributes to a deeper understanding of current NER model capabilities and limitations when applied to morphologically rich languages like Russian within the cultural heritage domain, offering insights for researchers and practitioners. Follow-up evaluation with GPT-4.1 (April 2025) achieves F1=0.94 for both simple and structured prompts, demonstrating rapid progress across model families and simplified deployment requirements.

cs.CL↗

arXiv2024

Automatic Translation Alignment Pipeline for Multilingual Digital Editions of Literary Works

This paper investigates the application of translation alignment algorithms in the creation of a Multilingual Digital Edition (MDE) of Alessandro Manzoni's Italian novel "I promessi sposi" ("The Betrothed"), with translations in eight languages (English, Spanish, French, German, Dutch, Polish, Russian and Chinese) from the 19th and 20th centuries. We identify key requirements for the MDE to improve both the reader experience and support for translation studies. Our research highlights the limitations of current state-of-the-art algorithms when applied to the translation of literary texts and outlines an automated pipeline for MDE creation. This pipeline transforms raw texts into web-based, side-by-side representations of original and translated texts with different rendering options. In addition, we propose new metrics for evaluating the alignment of literary translations and suggest visualization techniques for future analysis.

cs.CL↗

arXiv2024

Mapping Literary Space: A Social Network from the Timeline of Cultural Events

This study applies social network analysis (SNA) to map and analyze literary networks in St Petersburg from 1999 to 2019, using data from the 'SPbLitGuide' newsletter. By examining co-participation in literary events, we reveal the dynamics and structures of these networks, identifying key communities and influential figures. Our network graph, consisting of 14,066 nodes and 127,068 edges, represents a highly interconnected and cohesive small-world network with robust local clustering and extensive collaboration. Focusing on core participants, we refined the graph and applied community detection methods to identify distinct groups with specific aesthetic preferences and personal connections. These findings provide insights into the structure and dynamics of literary groups in St. Petersburg and provide a foundation for further research in the digital humanities.

cs.SI↗