arXiv subjects

Lea Karbevska

Publications and source records attributed to Lea Karbevska.

5 recordsLinked to original sources

arXiv2026

Structural Consequences of Policy-Based Interventions on the Global Supply Chain Network

As global political tensions rise and the anticipation of additional tariffs from the United States on international trade increases, the issues of economic independence and supply chain resilience become more prominent. The importance of supply chain resilience has been further underscored by disruptions caused by the COVID-19 pandemic and the ongoing war in Ukraine. In light of these challenges, ranging from geopolitical instability to product supply uncertainties, governments are increasingly focused on adopting new trade policies. This study explores the impact of several of these policies on the global electric vehicle (EV) supply chain network, with a particular focus on their effects on country clusters and the broader structure of international trade. Specifically, we analyse three key policies: Country Plus One, Friendshoring, and Reshoring. Our findings show that Friendshoring, contrary to expectations, leads to greater globalisation by increasing the number of supply links across friendly countries, potentially raising transaction costs. The Country Plus One policy similarly enhances network density through redundant links, while the Reshoring policy creates challenges in the EV sector due to the high number of irreplaceable products. Additionally, the effects of these policies vary across industries; for instance, mining goods being less affected in Country Plus One than the Friendshoring policy.

cs.LG↗

arXiv2025

Lost in Serialization: Invariance and Generalization of LLM Graph Reasoners

While promising, graph reasoners based on Large Language Models (LLMs) lack built-in invariance to symmetries in graph representations. Operating on sequential graph serializations, LLMs can produce different outputs under node reindexing, edge reordering, or formatting changes, raising robustness concerns. We systematically analyze these effects, studying how fine-tuning impacts encoding sensitivity as well generalization on unseen tasks. We propose a principled decomposition of graph serializations into node labeling, edge encoding, and syntax, and evaluate LLM robustness to variations of each of these factors on a comprehensive benchmarking suite. We also contribute a novel set of spectral tasks to further assess generalization abilities of fine-tuned reasoners. Results show that larger (non-fine-tuned) models are more robust. Fine-tuning reduces sensitivity to node relabeling but may increase it to variations in structure and format, while it does not consistently improve performance on unseen tasks.

cs.LG↗

arXiv2023

Mapping Global Value Chains at the Product Level

Value chain data is crucial to navigate economic disruptions, such as those caused by the COVID-19 pandemic and the war in Ukraine. Yet, despite its importance, publicly available value chain datasets, such as the ``World Input-Output Database'', ``Inter-Country Input-Output Tables'', ``EXIOBASE'' or the ``EORA'', lack detailed information about products (e.g. Radio Receivers, Telephones, Electrical Capacitors, LCDs, etc.) and rely instead on more aggregate industrial sectors (e.g. Electrical Equipment, Telecommunications). Here, we introduce a method based on machine learning and trade theory to infer product-level value chain relationships from fine-grained international trade data. We apply our method to data summarizing the exports and imports of 300+ world regions (e.g. states in the U.S., prefectures in Japan, etc.) and 1200+ products to infer value chain information implicit in their trade patterns. Furthermore, we use proportional allocation to assign the trade flow between regions and countries. This work provides an approximate method to map value chain data at the product level with a relevant trade flow, that should be of interest to people working in logistics, trade, and sustainable development.

econ.GN↗

arXiv2023

Gaia Data Release 3: All-sky classification of 12.4 million variable sources into 25 classes

Gaia DR3 contains 1.8 billion sources with G-band photometry, 1.5 billion of which with BP and RP photometry, complemented by positions on the sky, parallax, and proper motion. The median number of field-of-view transits in the three photometric bands is between 40 and 44 measurements per source and covers 34 months of data collection. We pursue a classification of Galactic and extra-galactic objects that are detected as variable by Gaia across the whole sky. Supervised machine learning (eXtreme Gradient Boosting and Random Forest) was employed to generate multi-class, binary, and meta-classifiers that classified variable objects with photometric time series in the G, BP, and RP bands. Classification results comprise 12.4 million sources (selected from a much larger set of potential variable objects) and include about 9 million variable stars classified into 22 variability types in the Milky Way and nearby galaxies such as the Magellanic Clouds and Andromeda, plus thousands of supernova explosions in distant galaxies, 1 million active galactic nuclei, and almost 2.5 million galaxies. The identification of galaxies was made possible by the artificial variability of extended objects as detected by Gaia, so they were published in the galaxy_candidates table of the Gaia DR3 archive, separate from the classifications of genuine variability (in the vari_classifier_result table). The latter contains 24 variability classes or class groups of periodic and non-periodic variables (pulsating, eclipsing, rotating, eruptive, cataclysmic, stochastic, and microlensing), with amplitudes from a few milli-magnitudes to several magnitudes.

astro-ph.GA↗

arXiv2023

Gaia Data Release 3 Validating the classification of variable Young Stellar Object candidates

Context. The Gaia third Data Release (DR3) presents the first catalogue of full-sky variable Young Stellar Object (YSO) candidates observed by the Gaia space telescope during the initial 34 months of science operations. Aims. Numerous types of variable stars were classified using photometric data collected by Gaia. One of the new classes presented in the Gaia DR3 is the class of YSOs showing brightness variability. We analysed 79 375 sources classified as YSO candidates to validate their young nature and investigate the completeness and purity of the sample. Methods. We cross-matched the Gaia DR3 YSO sample with numerous catalogues from the literature, including YSO catalogues based on optical and infrared data, as well as catalogues of extragalactic sources and Galactic variable stars. YSO catalogues were used to quantify the completeness of the Gaia DR3 YSO sample, while others were inspected to calculate the contamination. Results. Among the 79 375 potential Young Stellar Object candidates published in the Gaia DR3 variable star catalogue, the majority of these objects are distributed along the line of sight of well-known Star Forming Regions and the Galactic mid-plane. We found that the contamination level is well below 1%, while the completeness is also at the percent level, taking into account that the Gaia DR3 YSO sample is based on sources that showed significant variability during the data collection period. The number of sources in our sample that were never catalogued before as YSO candidates is in the order of $\sim$40 000 objects.

astro-ph.SR↗