Hasil untuk "History of Spain"

Víctor S. Peña, M. Miravitlles, R. Gabriel et al.

Lang Feng, Fuchao Yang, Feng Chen et al.

Recent advances in large language models (LLMs) enable agentic systems trained with reinforcement learning (RL) over multi-turn interaction trajectories, but practical deployment is bottlenecked by rapidly growing textual histories that inflate token budgets and memory usage. We introduce AgentOCR, a framework that exploits the superior information density of visual tokens by representing the accumulated observation-action history as a compact rendered image. To make multi-turn rollouts scalable, AgentOCR proposes segment optical caching. By decomposing history into hashable segments and maintaining a visual cache, this mechanism eliminates redundant re-rendering. Beyond fixed rendering, AgentOCR introduces agentic self-compression, where the agent actively emits a compression rate and is trained with compression-aware reward to adaptively balance task success and token efficiency. We conduct extensive experiments on challenging agentic benchmarks, ALFWorld and search-based QA. Remarkably, results demonstrate that AgentOCR preserves over 95\% of text-based agent performance while substantially reducing token consumption (>50\%), yielding consistent token and memory efficiency. Our further analysis validates a 20x rendering speedup from segment optical caching and the effective strategic balancing of self-compression.

Benjamin Mako Hill, Aaron Shaw

Wikipedia's founders could not have dreamed they were creating the most important laboratory for social scientific and computing research in history but that is exactly what happened. Hill and Shaw take account of Wikipedia's enormous effect on academic scholarship

Serin Kim, Sangam Lee, Dongha Lee

Large language models have advanced web agents, yet current agents lack personalization capabilities. Since users rarely specify every detail of their intent, practical web agents must be able to interpret ambiguous queries by inferring user preferences and contexts. To address this challenge, we present Persona2Web, the first benchmark for evaluating personalized web agents on the real open web, built upon the clarify-to-personalize principle, which requires agents to resolve ambiguity based on user history rather than relying on explicit instructions. Persona2Web consists of: (1) user histories that reveal preferences implicitly over long time spans, (2) ambiguous queries that require agents to infer implicit user preferences, and (3) a reasoning-aware evaluation framework that enables fine-grained assessment of personalization. We conduct extensive experiments across various agent architectures, backbone models, history access schemes, and queries with varying ambiguity levels, revealing key challenges in personalized web agent behavior. For reproducibility, our codes and datasets are publicly available at https://anonymous.4open.science/r/Persona2Web-73E8.

Sofía Rodríguez Serrador, Rafael Serrano García

Chen Zhang, Bo Hu, Weidong Chen et al.

While large language models (LLMs) have proven effective in leveraging textual data for recommendations, their application to multimodal recommendation tasks remains relatively underexplored. Although LLMs can process multimodal information through projection functions that map visual features into their semantic space, recommendation tasks often require representing users' history interactions through lengthy prompts combining text and visual elements, which not only hampers training and inference efficiency but also makes it difficult for the model to accurately capture user preferences from complex and extended prompts, leading to reduced recommendation performance. To address this challenge, we introduce HistLLM, an innovative multimodal recommendation framework that integrates textual and visual features through a User History Encoding Module (UHEM), compressing multimodal user history interactions into a single token representation, effectively facilitating LLMs in processing user preferences. Extensive experiments demonstrate the effectiveness and efficiency of our proposed mechanism.

A. Aassve, G. Alfani, Francesco Gandolfi et al.

Abstract Recent studies argue that major crises can have long‐lasting effects on individual behavior. While most studies focused on natural disasters, we explore the consequences of the global pandemic caused by a lethal influenza virus in 1918–19: the so‐called “Spanish Flu.” This was by far the worst pandemic of modern history, causing up to 100 million deaths worldwide. Using information about attitudes of respondents to the General Social Survey, we find evidence that experiencing the pandemic likely had permanent consequences in terms of individuals' social trust. Our findings suggest that lower social trust was passed on to the descendants of the survivors of the Spanish Flu who migrated to the United States. As trust is a crucial factor for long‐term economic development, our research offers a new angle from which to assess current health threats.

Abinash Kumar Shaw, Raghunath Ghara, Paz Beniamini et al.

We propose different estimators to probe the intergalactic medium (IGM) during epoch of reionization (EoR) using the dispersion measure (${\rm DM}$) of the fast radio bursts. We consider three different reionization histories, which we can distinguish with a total of $\lesssim 1000\,{\rm DM}$ measurements during EoR if their redshifts are known. We note that the redshift derivatives of ${\rm DM}$ are also directly sensitive to the reionization history. The major point of this work is to explore the variance in the ${\rm DM}$ measurements and the information encoded in them. We find that the all-sky average $\overline{\rm DM}(z)$ gets biased from the line-of-sight (LoS) fluctuations in the ${\rm DM}$ measurements introduced by the ionization of IGM during EoR. We find that the ratio $σ_{\rm DM}/\overline{\rm DM}$ depends directly on the ionization bubble sizes as well as the reionization history. On the other hand, we also find that angular variance (coined as $\textit{structure function}$) of ${\rm DM}$ encodes the information about the duration of reionization and the typical bubble sizes as well. We establish the usefulness of variances in ${\rm DM}$ using toy models of reionization and later verify it with the realistic reionization simulations.

Lekshmi Thulasidharan, Elena D'Onghia, Robert Benjamin et al.

The prevailing model of galaxy formation proposes that galaxies like the Milky Way are built through a series of mergers with smaller galaxies over time. However, the exact details of the Milky Way's assembly history remain uncertain. In this study, we show that the Milky Way's merger history is uniquely encoded in the vertical thickness of its stellar disk. By leveraging age estimates from the value-added LAMOST DR8 catalog and the StarHorse ages from SDSS-IV DR12 data, we investigate the relationship between disk thickness and stellar ages in the Milky Way using a sample comprising Red Giants (RG), Red Clump Giants (RCG), and metal-poor stars (MPS). Guided by the IllustrisTNG50 simulations, we show that an increase in the dispersion of the vertical displacement of stars in the disk traces its merger history. This analysis reveals the epoch of a major merger event that assembled the Milky Way approximately 11.13 billion years ago, as indicated by the abrupt increase in disk thickness among stars of that age, likely corresponding to the Gaia-Sausage Enceladus (GSE) event. The data do not exclude an earlier major merger, which may have occurred about 1.3 billion years after the Big Bang. Furthermore, the analysis suggests that the geometric thick disk of the Milky Way was formed around 11.13 billion years ago, followed by a transition period of approximately 2.6 billion years leading to the formation of the geometric thin disk, illustrating the galaxy's structural evolution. Additionally, we identified three more recent events -- 5.20 billion, 2.02 billion, and 0.22 billion years ago -- potentially linked to multiple passages of the Sagittarius dwarf galaxy. Our study not only elucidates the complex mass assembly history of the Milky Way and highlights its past interactions but also introduces a refined method for examining the merger histories of external galaxies.

M. Maldonado, R. Aguilar, R. Bannister et al.

Marina Blanco-Aparicio, Francisco José García-Río, Francisco Javier González-Barcala et al.

Resumen: Introducción: El asma es una enfermedad con elevada prevalencia, que afecta a todos los grupos de edad y genera elevados costes sociosanitarios. Estudios realizados en diversas poblaciones muestran gran variabilidad en su prevalencia, incluso en poblaciones cercanas geográficamente, con datos que sugieren una influencia relevante de factores socioeconómicos. Actualmente en población adulta de España no disponemos de datos poblacionales fiables sobre la prevalencia de esta enfermedad. Los objetivos de este estudio son estimar la prevalencia de asma en población española de 18-79 años, describir la variabilidad entre comunidades autónomas, estimar la prevalencia de infra y sobrediagnóstico, prevalencia de asma no controlada, de asma córticodependiente, conocer el consumo de recursos sanitarios, identificar los fenotipos más frecuentes y establecer un punto de partida para evaluar la tendencia temporal con estudios posteriores. Material y métodos: Se realizará un estudio transversal, bietápico, incluyendo pacientes de 50 áreas sanitarias. El estudio se desarrollará en tres fases: 1) cribado y confirmación en historia clínica, en la cual se identificarán los pacientes con diagnóstico previo correctamente establecido de asma; 2) diagnóstico de asma, evaluando a los pacientes en los cuales no está claro el diagnóstico de asma con los datos disponibles en la historia clínica; 3) caracterización del asma, analizando las características de estos pacientes e identificando los fenotipos más frecuentes. Discusión: Parece necesario y factible realizar un estudio epidemiológico del asma en España que permita identificar la prevalencia de asma, optimizar la planificación sanitaria, caracterizar los fenotipos más frecuentes de la enfermedad y evaluar los diagnósticos erróneos. Abstract: Introduction: Asthma is a disease with high prevalence, which affects all age groups and generates high health and social care costs. Studies carried out in a number of populations show great variability in its prevalence, even in geographically close populations, with data suggesting a relevant influence of socio-economic factors. At present, we do not have reliable data on the prevalence of this disease in the adult population of Spain. The objectives of this study are to estimate the prevalence of asthma in the Spanish population for those aged 18-79, to describe the variability between autonomous communities, to estimate the prevalence of under and overdiagnosis, to analyse the prevalence of uncontrolled asthma and steroid-dependent asthma, to evaluate the health care cost, to identify the most frequent phenotypes and to establish a starting point to evaluate the temporal trend with subsequent studies. Methods: A cross-sectional, two-stage study will be carried out, including patients from 50 catchment areas. The study will be carried out in 3 phases: 1) screening and confirmation in the clinical history, in which patients with a previously correctly established diagnosis of asthma will be identified; 2) diagnosis of asthma to evaluate patients without a confirmed or excluded diagnosis; 3) characterization of asthma, where the characteristics of the asthmatic patients will be analysed, identifying the most frequent phenotypes. Discussion: It seems necessary and feasible to carry out an epidemiological study of asthma in Spain to identify the prevalence of asthma, to optimize healthcare planning, to characterize the most frequent phenotypes of the disease, and to evaluate inaccurate diagnoses.

Yanzhao Zheng, Haibin Wang, Baohua Dong et al.

Recently, context-dependent text-to-SQL semantic parsing which translates natural language into SQL in an interaction process has attracted a lot of attention. Previous works leverage context-dependence information either from interaction history utterances or the previous predicted SQL queries but fail in taking advantage of both since of the mismatch between natural language and logic-form SQL. In this work, we propose a History Information Enhanced text-to-SQL model (HIE-SQL) to exploit context-dependence information from both history utterances and the last predicted SQL query. In view of the mismatch, we treat natural language and SQL as two modalities and propose a bimodal pre-trained model to bridge the gap between them. Besides, we design a schema-linking graph to enhance connections from utterances and the SQL query to the database schema. We show our history information enhanced methods improve the performance of HIE-SQL by a significant margin, which achieves new state-of-the-art results on the two context-dependent text-to-SQL benchmarks, the SparC and CoSQL datasets, at the writing time.

Yunjia Xi, Weiwen Liu, Jieming Zhu et al.

As the final stage of the multi-stage recommender system (MRS), reranking directly affects users' experience and satisfaction, thus playing a critical role in MRS. Despite the improvement achieved in the existing work, three issues are yet to be solved. First, users' historical behaviors contain rich preference information, such as users' long and short-term interests, but are not fully exploited in reranking. Previous work typically treats items in history equally important, neglecting the dynamic interaction between the history and candidate items. Second, existing reranking models focus on learning interactions at the item level while ignoring the fine-grained feature-level interactions. Lastly, estimating the reranking score on the ordered initial list before reranking may lead to the early scoring problem, thereby yielding suboptimal reranking performance. To address the above issues, we propose a framework named Multi-level Interaction Reranking (MIR). MIR combines low-level cross-item interaction and high-level set-to-list interaction, where we view the candidate items to be reranked as a set and the users' behavior history in chronological order as a list. We design a novel SLAttention structure for modeling the set-to-list interactions with personalized long-short term interests. Moreover, feature-level interactions are incorporated to capture the fine-grained influence among items. We design MIR in such a way that any permutation of the input items would not change the output ranking, and we theoretically prove it. Extensive experiments on three public and proprietary datasets show that MIR significantly outperforms the state-of-the-art models using various ranking and utility metrics.

Aditya Prakash, K. S. Thejaswini

We consider the model of history-deterministic one-counter nets (OCNs). History-determinism is a property of transition systems that allows for a limited kind of non-determinism which can be resolved 'on-the-fly'. Token games, which have been used to characterise history-determinism over various models, also characterise history-determinism over OCNs. By reducing 1-token games to simulation games, we are able to show that checking for history-determinism of OCNs is decidable. Moreover, we prove that this problem is PSPACE-complete for a unary encoding of transitions, and EXPSPACE-complete for a binary encoding. We then study the language properties of history-deterministic OCNs. We show that the resolvers of non-determinism for history-deterministic OCNs are eventually periodic. As a consequence, for a given history-deterministic OCN, we construct a language equivalent deterministic one-counter automaton. We also show the decidability of comparing languages of history-deterministic OCNs, such as language inclusion and language universality.

Marcel Keller, M. Spyrou, C. Scheib et al.

Significance The first historically reported pandemic attributed to Yersinia pestis started with the Justinianic Plague (541–544) and continued for around 200 y as the so-called First Pandemic. To date, only one Y. pestis strain from this pandemic has been reconstructed using ancient DNA. In this study, we present eight genomes from Britain, France, Germany, and Spain, demonstrating the geographic range of plague during the First Pandemic and showing microdiversity in the Early Medieval Period. Moreover, we detect similar genome decay during the First and Second Pandemics (14th to 18th century) that includes the same two virulence factors, thus providing an example of potential convergent evolution of Y. pestis during large-scale epidemics. The first historically documented pandemic caused by Yersinia pestis began as the Justinianic Plague in 541 within the Roman Empire and continued as the so-called First Pandemic until 750. Although paleogenomic studies have previously identified the causative agent as Y. pestis, little is known about the bacterium’s spread, diversity, and genetic history over the course of the pandemic. To elucidate the microevolution of the bacterium during this time period, we screened human remains from 21 sites in Austria, Britain, Germany, France, and Spain for Y. pestis DNA and reconstructed eight genomes. We present a methodological approach assessing single-nucleotide polymorphisms (SNPs) in ancient bacterial genomes, facilitating qualitative analyses of low coverage genomes from a metagenomic background. Phylogenetic analysis on the eight reconstructed genomes reveals the existence of previously undocumented Y. pestis diversity during the sixth to eighth centuries, and provides evidence for the presence of multiple distinct Y. pestis strains in Europe. We offer genetic evidence for the presence of the Justinianic Plague in the British Isles, previously only hypothesized from ambiguous documentary accounts, as well as the parallel occurrence of multiple derived strains in central and southern France, Spain, and southern Germany. Four of the reported strains form a polytomy similar to others seen across the Y. pestis phylogeny, associated with the Second and Third Pandemics. We identified a deletion of a 45-kb genomic region in the most recent First Pandemic strains affecting two virulence factors, intriguingly overlapping with a deletion found in 17th- to 18th-century genomes of the Second Pandemic.

Adelheid Holl

José Luis Girón Alconchel

In this paper, the linguistic segmentation of the discourse is applied to the stanzas 695-706 of the manuscript E of Rimado de Palacio to sustain that they are not of a stylistic level lower than those read in the two manuscripts, N and E, nor are they repetitive and thus cannot be deleted without damaging the text.

Paulo Esmeraldo Catarino Lopes

In 1510, an anonymous nobleman servant of the 4th Duke of Braganza, D. Jaime, left Portugal for Italy to return only in 1517. On the basis of this travel, he later wrote an extensive and impressive account. One of the figures that holds his attention is Francis I of France. Although sparse, the outlined portrait of the then young ruler suggests, from various perspectives, the decisive role that he will play in European foreign policy in the early 16th century. It is, in essence, a case study, but the truth is that we are dealing with an excellent critical instrument to go beyond the «circumscribed» context of the case itself, addressing broader questions at the interpretive level, such as the contest for hegemony in 16th century Europe, the passage from Christendom to Europe of increasingly centralized national monarchies or even the growing autonomy in relation to Rome by the emerging powers of the West.

Dhiraj Kumar Hazra, Daniela Paoletti, Fabio Finelli et al.

Cosmic Microwave Background temperature and polarization anisotropies from Planck have estimated a lower value of the optical depth to reionization ($τ$) compared to WMAP. A significant period in the reionization history would then fall within $6<z< 10$, where detection of galaxies with Hubble Frontier Fields program and independent estimation of neutral hydrogen in the inter galactic medium by Lyman-$α$ observations are also available. This overlap allows an analysis of cosmic reionization which utilizes a direct combination of CMB and these astrophysical measurements and potentially breaks degeneracies in parameters describing the physics of reionization. For the first time we reconstruct reionization histories by assuming photo-ionization and recombination rates to be free-form and by allowing underlying cosmological parameters to vary with CMB (temperature and polarization anisotropies and lensing) data from Planck 2018 release and a compilation of astrophysical data. We find an excellent agreement between the low-$\ell$ Planck 2018 HFI polarization likelihood and astrophysical data in determining the integrated optical depth. By combining both data, we report for a minimal reconstruction $τ=0.051^{+0.001+0.002}_{-0.0012-0.002}$ at 68\% and 95\% CL, which, for the errors in the current astrophysical measurements quoted in the literature, is nearly twice better than the projected cosmic variance limited CMB measurements. For the duration of reionization, redshift interval between 10\% and complete ionization, we get $2.9^{+0.12+0.29}_{-0.16-0.26}$ at 68\% and 95\% CL, which improves significantly on the corresponding result obtained by using Planck 2015 data. By a Bayesian analysis of the combined results we do not find evidence beyond monotonic reionization histories, therefore multi-phase reionization scenario is disfavored compared to minimal alternatives.

Sethuraman Sankaran, David Lesage, Rhea Tombropoulos et al.

Predictive modeling of blood flow and pressure have numerous applications ranging from non-invasive assessment of functional significance of disease to planning invasive procedures. While several such predictive modeling techniques have been proposed, their use in the clinic has been limited due in part to the significant time required to perform virtual interventions and compute the resultant changes in hemodynamic conditions. We propose a fast hemodynamic assessment method based on first constructing an exploration space of geometries, tailored to each patient, and subsequently building a physics driven reduced order model in this space. We demonstrate that this method can predict fractional flow reserve derived from coronary computed tomography angiography in response to changes to a patient-specific lumen geometry in real time while achieving high accuracy when compared to computational fluid dynamics simulations. We validated this method on over 1300 patients that received a coronary CT scan and demonstrated a correlation coefficient of 0.98 with an error of 0.005 +- 0.015 (95% confidence interval: (-0.020, 0.031)) as compared to three-dimensional blood flow calculations.