Hasil untuk "History of Spain"

Concepción Lopezosa Aparicio

El propósito de este trabajo es poner de manifiesto la relevancia que la estimulación del olfato tuvo en el ceremonial de corte, tomando como referencia algunos ejemplos de celebraciones acontecidas en el contexto de la Monarquía Hispánica durante el siglo XVII. A partir del estudio de caso de determinadas festividades civiles y religiosas, sucedidas en Madrid a lo largo del seiscientos, se pretende mostrar la importancia que los olores y los aromas tuvieron en las complejas puestas en escena de las conmemoraciones durante la modernidad, con una afectación directa en los partícipes de los ceremoniales.

Eva Moreda Rodríguez

This article examines the unsuccessful attempts made by a group of Catalan and Spanish exiled composers (Roberto Gerhard, Baltasar Samper, Josep Valls and Óscar Esplá) to reconstruct the Spanish and Catalan delegations of the International Society of Contemporary Music (ISCM) in exile, during the second half of the 1940s. This episode is enormously valuable and revealing if we analyze it, as the article does, within two historical contexts: firstly, the history of the ISCM in Spain, and in particular the efforts that the republican government made to ensure that its country was still present at ISCM festivals during the Civil War, in an attempt to guarantee that the cultural legitimacy of Spain remained on the exile side, at a time when the Franco regime threatened to appropriate it. Secondly, we can observe parallels between this correspondence and other episodes of the Spanish musical exile in the period 1945-1950 which suggests that Spanish and Catalan music also engaged in a generalized reorientation of their strategies of resistance/adaptation

Solal Rapaport, Laurent Pautet, Samuel Tardieu et al.

Version Control Systems (VCS) like Git allow developers to locally rewrite recorded history, e.g., to reorder and suppress commits or specific data in them. These alterations have legitimate use cases, but become problematic when performed on public branches that have downstream users: they break push/pull workflows, challenge the integrity and reproducibility of repositories, and create opportunities for supply chain attackers to sneak into them nefarious changes. We conduct the first large-scale investigation of Git history alterations in public code repositories. We analyze 111 M (millions) repositories archived by Software Heritage, which preserves VCS histories even across alterations. We find history alterations in 1.22 M repositories, for a total of 8.7 M rewritten histories. We categorize changes by where they happen (which repositories, which branches) and what is changed in them (files or commit metadata). Conducting two targeted case studies we show that altered histories recurrently change licenses retroactively, or are used to remove ''secrets'' (e.g., private keys) committed by mistake. As these behaviors correspond to bad practices-in terms of project governance or security management, respectively-that software recipients might want to avoid, we introduce GitHistorian, an automated tool, that developers can use to spot and describe history alterations in public Git repositories.

M. I. Fullana Barceló, F. Artigues Serra, A. R. Millan Pons et al.

Abstract Background Influenza viruses cause pneumonia in approximately one-third of cases, and pneumonia is an important cause of death. The aim was to identify risk factors associated with severity and those that could predict the development of pneumonia. Methods This retrospective, observational study included all adult patients with confirmed influenza virus infection admitted to Son Espases University Hospital during four influenza seasons in Spain (October to May) from to 2012–2016. Results Overall, 666 patients with laboratory-confirmed influenza were included, 93 (14%) of which were severe; 73 (10.9%) were admitted to Intensive Care Unit (ICU), 39 (5.8%) died, and 185 (27.7%) developed pneumonia. Compared to less severe cases, patients with severe disease: were less vaccinated (40% vs. 28%, p = 0.021); presented with more confusion (26.9% vs. 6.8%), were more hypoxemic (Horowitz index (PaO2/FiO2) 261 vs. 280), had higher C-reactive protein (CRP) (12.3 vs. 4.0), had more coinfections (26.8% vs. 6.3%) and had more pleural effusion (14% vs. 2.6%) (last six all p < 0.001). Risk factors significantly associated with severity were pneumonia [OR (95% CI) = 4.14 (2.4–7.16)], history of heart disease (1.84, 1.03–3.28), and confusion at admission (4.99, 2.55–9.74). Influenza vaccination was protective (0.53, 0.28–0.98). Compared to those without pneumonia, the pneumonia group had higher CRP (11.3 vs. 4.0, p < 0.001), lower oxygen saturation (92% vs. 94%, p < 0.001), were more hypoxic (PaO2/FiO2 266 vs. 281, p < 0.001), and incurred more mechanical ventilation, septic shock, admission to the ICU, and deaths (all four p < 0.001). Higher CRP and lower oxygen saturation were independent variables for predicting the development of pneumonia. Conclusions Pneumonia, history of heart disease, confusion and no influenza vaccination were independent variables to present complications in patients admitted with influenza infection.

Juergen Schmidhuber

Machine learning (ML) is the science of credit assignment. It seeks to find patterns in observations that explain and predict the consequences of events and actions. This then helps to improve future performance. Minsky's so-called "fundamental credit assignment problem" (1963) surfaces in all sciences including physics (why is the world the way it is?) and history (which persons/ideas/actions have shaped society and civilisation?). Here I focus on the history of ML itself. Modern artificial intelligence (AI) is dominated by artificial neural networks (NNs) and deep learning, both of which are conceptually closer to the old field of cybernetics than what was traditionally called AI (e.g., expert systems and logic programming). A modern history of AI & ML must emphasize breakthroughs outside the scope of shallow AI text books. In particular, it must cover the mathematical foundations of today's NNs such as the chain rule (1676), the first NNs (circa 1800), the first practical AI (1914), the theory of AI and its limitations (1931-34), and the first working deep learning algorithms (1965-). From the perspective of 2025, I provide a timeline of the most significant events in the history of NNs, ML, deep learning, AI, computer science, and mathematics in general, crediting the individuals who laid the field's foundations. The text contains numerous hyperlinks to relevant overview sites. With a ten-year delay, it supplements my 2015 award-winning deep learning survey which provides hundreds of additional references. Finally, I will put things in a broader historical context, spanning from the Big Bang to when the universe will be many times older than it is now.

Chenxi Ji

Do we really understand how machine classifies art styles? Historically, art is perceived and interpreted by human eyes and there are always controversial discussions over how people identify and understand art. Historians and general public tend to interpret the subject matter of art through the context of history and social factors. Style, however, is different from subject matter. Given the fact that Style does not correspond to the existence of certain objects in the painting and is mainly related to the form and can be correlated with features at different levels.(Ahmed Elgammal et al. 2018), which makes the identification and classification of the characteristics artwork's style and the "transition" - how it flows and evolves - remains as a challenge for both human and machine. In this work, a series of state-of-art neural networks and manifold learning algorithms are explored to unveil this intriguing topic: How does machine capture and interpret the flow of Art History?

Mancheng Meng, Ziyan Wu, Terrence Chen et al.

Predicting the future trajectory of a person remains a challenging problem, due to randomness and subjectivity of human movement. However, the moving patterns of human in a constrained scenario typically conform to a limited number of regularities to a certain extent, because of the scenario restrictions and person-person or person-object interactivity. Thus, an individual person in this scenario should follow one of the regularities as well. In other words, a person's subsequent trajectory has likely been traveled by others. Based on this hypothesis, we propose to forecast a person's future trajectory by learning from the implicit scene regularities. We call the regularities, inherently derived from the past dynamics of the people and the environment in the scene, scene history. We categorize scene history information into two types: historical group trajectory and individual-surroundings interaction. To exploit these two types of information for trajectory prediction, we propose a novel framework Scene History Excavating Network (SHENet), where the scene history is leveraged in a simple yet effective approach. In particular, we design two components: the group trajectory bank module to extract representative group trajectories as the candidate for future path, and the cross-modal interaction module to model the interaction between individual past trajectory and its surroundings for trajectory refinement. In addition, to mitigate the uncertainty in ground-truth trajectory, caused by the aforementioned randomness and subjectivity of human movement, we propose to include smoothness into the training process and evaluation metrics. We conduct extensive evaluations to validate the efficacy of our proposed framework on ETH, UCY, as well as a new, challenging benchmark dataset PAV, demonstrating superior performance compared to state-of-the-art methods.

Geza Kovacs

Users' detailed browsing activity - such as what sites they are spending time on and for how long, and what tabs they have open and which one is focused at any given time - is useful for a number of research and practical applications. Gathering such data, however, requires that users install and use a monitoring tool over long periods of time. In contrast, browser extensions can gain instantaneous access months of browser history data. However, the browser history is incomplete: it records only navigation events, missing important information such as time spent or tab focused. In this work, we aim to reconstruct time spent on sites with only users' browsing histories. We gathered three months of browsing history and two weeks of ground-truth detailed browsing activity from 185 participants. We developed a machine learning algorithm that predicts whether the browser window is focused and active at one second-level granularity with an F1-score of 0.84. During periods when the browser is active, the algorithm can predict which the domain the user was looking at with 76.2% accuracy. We can use these results to reconstruct the total time spent online for each user with an R^2 value of 0.96, and the total time each user spent on each domain with an R^2 value of 0.92.

Alfredo Garbuno-Inigo, F. Alejandro DiazDelaO, Konstantin M. Zuev

The scientific understanding of real-world processes has dramatically improved over the years through computer simulations. Such simulators represent complex mathematical models that are implemented as computer codes which are often expensive. The validity of using a particular simulator to draw accurate conclusions relies on the assumption that the computer code is correctly calibrated. This calibration procedure is often pursued under extensive experimentation and comparison with data from a real-world process. The problem is that the data collection may be so expensive that only a handful of experiments are feasible. History matching is a calibration technique that, given a simulator, it iteratively discards regions of the input space using an implausibility measure. When the simulator is computationally expensive, an emulator is used to explore the input space. In this paper, a Gaussian process provides a complete probabilistic output that is incorporated into the implausibility measure. The identification of regions of interest is accomplished with recently developed annealing sampling techniques. Active learning functions are incorporated into the history matching procedure to refocus on the input space and improve the emulator. The efficiency of the proposed framework is tested in well-known examples from the history matching literature, as well as in a proposed testbed of functions of higher dimensions.

Hasan Padamsee

Radio frequency (RF) superconductivity has become a key technology for many modern particle accelerators. One of its most salient features of this technology is the ability of superconducting RF cavities to deliver high accelerating gradients in continuous-wave and long-pulse modes of operation. However, reaching the current state of the technology was not an easy fit. Over many years scientists and engineers had to overcome several serous performance limitations. In this paper, I attempt to the best of my knowledge to trace the history of accelerating gradients evolution in the field of superconducting radio frequency. I will restrict the scope to primary innovations along with some of the ensuing developments in developing cavities made of bulk niobium. But I will not cover all the many applications and findings over the subsequent decades of progress that were based on the primary discoveries and inventions. I will also not cover a number of other important topics in the history of cavity developments, such as the drive for higher Q values, or the push for lower cavity costs via Nb/Cu cavities or large grain Nb cavities.

Raquel Manso-Calderón

Background. Paroxysmal dyskinesias (PxD) are a group of heterogeneous disorders characterized by intermittent episodes of involuntary movements. PxD include paroxysmal kinesigenic (PKD), nonkinesigenic (PNK), and exercise-induced (PED) varieties. Objectives. To define the phenotype of primary and secondary PxD forms. Methods. Twenty-two patients with PxD (9 men/13 women) were evaluated in two hospitals in south-west Castilla y Leon, Spain. Clinical features of the episodes, causes, family history, and response to treatment were collected. Results. Thirteen participants with primary PxD (6 men/7 women) and 9 with secondary PxD (3 men/6 women) were recruited. Nine patients belong to three nonrelated families (2 had PKD and 1 had PED). Mean age at onset in primary PKD cases was 10 years (range 5-23 years), earlier than in PNKD (24 years) and PED (20 years). Most primary PKD cases experienced daily episodes of duration <1 minute, which are more frequent and shorter attacks than in PNKD (1-2 per month, 5 minutes) and PED (1 per day, 15 minutes). The location of the involuntary movements varied widely; isolated dystonia was more common than mixed chorea and dystonia. All PKD patients who received antiepileptic treatment significantly improved. Levodopa and ketogenic diet proved to be effective in two patients with PED. Secondary forms presented a later mean age of onset (51 years). Six cases had PNKD, 1 had PKD, 1 both PNKD and PKD, and 1 had PED. Causes comprised vascular lesions, encephalitis, multiple sclerosis, peripheral trauma, endocrinopathies, and drugs such as selective serotonin reuptake inhibitors (SSRIs). Conclusion. The knowledge of the clinical features and spectrum of causes related to PxD is crucial to avoid delays in diagnosis and treatment, or even a nonorganic disorder diagnosis.

Branislav Kveton, Csaba Szepesvari, Mohammad Ghavamzadeh et al.

We propose a new online algorithm for cumulative regret minimization in a stochastic linear bandit. The algorithm pulls the arm with the highest estimated reward in a linear model trained on its perturbed history. Therefore, we call it perturbed-history exploration in a linear bandit (LinPHE). The perturbed history is a mixture of observed rewards and randomly generated i.i.d. pseudo-rewards. We derive a $\tilde{O}(d \sqrt{n})$ gap-free bound on the $n$-round regret of LinPHE, where $d$ is the number of features. The key steps in our analysis are new concentration and anti-concentration bounds on the weighted sum of Bernoulli random variables. To show the generality of our design, we generalize LinPHE to a logistic model. We evaluate our algorithms empirically and show that they are practical.

Chengcheng Han

A light higgsino is strongly favored by the naturalness, while as a dark matter candidate it is usually under-abundant. We consider the higgsino production in a non-standard history of the universe, caused by a scalar field with an initially displaced vacuum. We find that given a proper reheating temperature induced by the scalar decay, a light higgsino could provide the correct dark matter relic abundance. On the other hand, a sub-TeV higgsino dark matter, once observed, would be a strong hint of the non-standard thermal history of the universe.

Robert N. Swanson

Margery Kempe peregrinó desde Inglaterra a Compostela en 1417, una visita de la que dejó constancia en su cuasi-autobiográfico Libro. Este artículo analiza y contextualiza esa travesía como una parte de una vida y un texto marcados por peregrinajes similares. Además, integra la peregrinación en su vida como viatrix, comprometida con un viaje espiritual no hacia santuarios terrenales, sino a la salvación celestial. [gl] Margery Kempe peregrinou desde Inglaterra a Compostela en 1417, unha visita da que quedou constancia no seu case-autobiográfico Libro. Este artigo analiza e contextualiza esa travesía coma unha parte dunha vida e un texto marcados por peregrinacións semellantes. Ademais, integra a peregrinación na súa vida coma viatrix, comprometida cunha viaxe espiritual non cara a santuarios terreais, senón á salvación celestial.

Hsin-Yuan Huang, Eunsol Choi, Wen-tau Yih

Conversational machine comprehension requires the understanding of the conversation history, such as previous question/answer pairs, the document context, and the current question. To enable traditional, single-turn models to encode the history comprehensively, we introduce Flow, a mechanism that can incorporate intermediate representations generated during the process of answering previous questions, through an alternating parallel processing structure. Compared to approaches that concatenate previous questions/answers as input, Flow integrates the latent semantics of the conversation history more deeply. Our model, FlowQA, shows superior performance on two recently proposed conversational challenges (+7.2% F1 on CoQA and +4.0% on QuAC). The effectiveness of Flow also shows in other tasks. By reducing sequential instruction understanding to conversational machine comprehension, FlowQA outperforms the best models on all three domains in SCONE, with +1.8% to +4.4% improvement in accuracy.

Ghazaleh Beigi, Ruocheng Guo, Alexander Nou et al.

The overturning of the Internet Privacy Rules by the Federal Communications Commissions (FCC) in late March 2017 allows Internet Service Providers (ISPs) to collect, share and sell their customers' Web browsing data without their consent. With third-party trackers embedded on Web pages, this new rule has put user privacy under more risk. The need arises for users on their own to protect their Web browsing history from any potential adversaries. Although some available solutions such as Tor, VPN, and HTTPS can help users conceal their online activities, their use can also significantly hamper personalized online services, i.e., degraded utility. In this paper, we design an effective Web browsing history anonymization scheme, PBooster, aiming to protect users' privacy while retaining the utility of their Web browsing history. The proposed model pollutes users' Web browsing history by automatically inferring how many and what links should be added to the history while addressing the utility-privacy trade-off challenge. We conduct experiments to validate the quality of the manipulated Web browsing history and examine the robustness of the proposed approach for user privacy protection.

Zhaopeng Tu, Yang Liu, Shuming Shi et al.

Existing neural machine translation (NMT) models generally translate sentences in isolation, missing the opportunity to take advantage of document-level information. In this work, we propose to augment NMT models with a very light-weight cache-like memory network, which stores recent hidden representations as translation history. The probability distribution over generated words is updated online depending on the translation history retrieved from the memory, endowing NMT models with the capability to dynamically adapt over time. Experiments on multiple domains with different topics and styles show the effectiveness of the proposed approach with negligible impact on the computational cost.

Jingxian Zhang, Dong Liu

Music history, referring to the records of users' listening or downloading history in online music services, is the primary source for music service providers to analyze users' preferences on music and thus to provide personalized recommendations to users. In order to engage users into the service and to improve user experience, it would be beneficial to provide visual analyses of one user's music history as well as visualized recommendations to that user. In this paper, we take a user-centric approach to the design of such visual analyses. We start by investigating user needs on such visual analyses and recommendations, then propose several different visualization schemes, and perform a pilot study to collect user feedback on the designed schemes. We further conduct user studies to verify the utility of the proposed schemes, and the results not only demonstrate the effectiveness of our proposed visualization, but also provide important insights to guide the visualization design in the future.

Zeller, Olivier

La historia francesa de los criados se encuentra en suspensión. Amplias perspectivas quedan todavía por recorrer. Conviene hacer hincapié en el aspecto temporal de la condición de criado que resulta de una pluralidad de actividades derivada de una importante movilidad. La relación entre amo y sirviente requiere una re-contextualización con respecto a los cambios de las relaciones sociales. Por último, la densidad y el alto grado de feminidad de los criados constituyen un elemento distintivo de la demografía urbana.

Fèlix Villagrasa