This page presents a list of the longest railway tunnels of the world, excluding subway tunnel sections.
Multilingual sentiment analysis remains challenging due to limited labeled data and strong cross-lingual variation. More specifically, the same expression may signal praise in one language but criticism in another, making sentiment detection highly context-dependent. At the same time, large language models (LLMs) often struggle to maintain stable classifications in low-resource and zero-shot settings. Therefore, we introduce MultiSent-RAG, a training-free retrieval-augmented framework that integrates structured sentiment corpora with unstructured multilingual evidence, and MultiSent-RAG-Cache, a semantic memory module that reuses prior label inferences based on embedding similarity. Our study uses 80,000 labeled instances across 12 languages, including low-resource and zero-shot settings. Retrieval augmentation leads to substantial improvements, with F1 gains of up to +0.65 points, and relative gains exceeding 70%-110% over strong LLM baselines. The semantic cache further reveals when memory reinforces consistent classifications and when it risks propagating errors. Beyond improving accuracy, our analysis reveals how retrieval strengthens contextual grounding across languages, while the semantic cache exposes a trade-off between classification stability and error propagation. These findings provide practical insights for designing robust and interpretable multilingual information processing systems.
Car ownership models are essential for understanding travel behavior and informing transportation policy decisions. However, previous research on car ownership modeling has not addressed the determinants of car ownership related to pollutant emissions such as fuel type or emission standard. This study seeks to fill this gap by comparing the performance of several classification models in predicting the number of cars owned by households, their fuel type and their Euro norm (i.e. car age), while also investigating the significance of explanatory variables. These variables include socioeconomic characteristics, as well as mobility-related variables such as commuting distance, parking availability, and public transportation accessibility to the home and workplace. The methodology is applied to the Paris region. We find that supervised learning models slightly outperform the multinomial logistic regression for the three models. Our results show that the main explanatory variables of the types of cars owned are related to the income, the household composition and the mobility-related characteristics of the household. For the fuel type, the household composition and regional accessibility of home city have a highly significant effect on petrol car ownership rather than diesel, while the income is an important predictor through non-linear relationships and interactions. Furthermore, the household income is the main determinant of the Euro norm, with a non-linear effect of the mobility-related variables (commuting distance and regional accessibility of home and work cities). This work paves the way for future research evaluating transportation policies related to household car ownership by allowing a deeper understanding of the determinants of the type of car owned by households and by providing the trained classifiers as open data.
The preparation of thermal states of matter is a crucial task in quantum simulation. In this work, we prove that a recently introduced, efficiently implementable dissipative evolution thermalizes to the Gibbs state in time scaling polynomially with system size at high enough temperatures for any Hamiltonian that satisfies a Lieb-Robinson bound, such as local Hamiltonians on a lattice. Furthermore, we show the efficient adiabatic preparation of the associated purifications or "thermofield double" states. To the best of our knowledge, these are the first results rigorously establishing the efficient preparation of high-temperature Gibbs states and their purifications. In the low-temperature regime, we show that implementing this family of dissipative evolutions for inverse temperatures polynomial in the system's size is computationally equivalent to standard quantum computations. On a technical level, for high temperatures, our proof makes use of the mapping of the generator of the evolution into a Hamiltonian, and then connecting its convergence to that of the infinite temperature limit. For low temperature, we instead perform a perturbation at zero temperature and resort to circuit-to-Hamiltonian mappings akin to the proof of universality of quantum adiabatic computing. Taken together, our results show that a family of quasi-local dissipative evolutions efficiently prepares a large class of quantum many-body states of interest, and has the potential to mirror the success of classical Monte Carlo methods for quantum many-body systems.
In the first half of 2025, coding agents have emerged as a category of development tools that have very quickly transitioned to the practice. Unlike ”traditional” code completion LLMs such as Copilot, agents like Cursor, Claude Code, or Codex operate with high degrees of autonomy, up to generating complete pull requests starting from a developer-provided task description. This new mode of operation is poised to change the landscape in an even larger way than code completion LLMs did, making the need to study their impact critical. Also, unlike traditional LLMs, coding agents tend to leave more explicit traces in software engineering artifacts, such as co-authoring commits or pull requests. We leverage these traces to present the first large-scale study (129,134 projects) of the adoption of coding agents on GitHub, finding an estimated adoption rate of 15.85
Sickle cell disease (SCD) causes pulmonary parenchymal and vascular complications with a major impact on mortality. Quantitative computed tomography (CT) assessment of pulmonary vascular “pruning” (rarefaction of small-caliber pulmonary vessels) may provide a non-invasive tool to better understand these complications. To evaluate vascular pruning in SCD and its association with clinical, functional respiratory, and imaging parameters. Non-contrast chest CT scans from 73 adult patients with SCD followed at Avicenne Hospital (Paris, France) were analyzed. Pulmonary vascular volumes were quantified using the blood volume in vessels with a cross-sectional area < 5 mm² (BV5) and the ratio of BV5 to total pulmonary blood volume (TBV), measured globally and in peripheral lung regions. These biomarkers were compared with pulmonary function tests (PFTs), CT parenchymal abnormalities, and clinical history of vascular disease. In 73 patients (mean age 33 ± 14 years; 38 women), linear opacities (78
