Lightweight learning from label proportions on satellite imagery

This work addresses the challenge of producing chip level predictions on satellite imagery when only label proportions at a coarser spatial geometry are available, typically from statistical or aggregated data from administrative divisions (such as municipalities or communes). This kind of tabular data is usually widely available in many regions of the world and application areas and, thus, its exploitation may contribute to leverage the endemic scarcity of fine grained labelled data in Earth Observation (EO). This can be framed as a Learning from Label Proportions (LLP) problem setup. LLP applied to EO data is still an emerging field and performing comparative studies in applied scenarios remains a challenge due to the lack of standardized datasets. In this work, first, we show how simple deep learning and probabilistic methods generally perform better than standard more complex ones, providing a surprising level of finer grained spatial detail when trained with much coarser label proportions. Second, we provide a set of benchmarking datasets enabling comparative LLP applied to EO, providing both fine grained labels and aggregated data according to existing administrative divisions. Finally, we argue how this approach might be valuable when considering on-orbit inference and training. Source code is available at https://github.com/rramosp/llpeoComment: 16 pages, 13 figure

Quantum Kernel Mixtures for Probabilistic Deep Learning

This paper presents a novel approach to probabilistic deep learning (PDL), quantum kernel mixtures, derived from the mathematical formalism of quantum density matrices, which provides a simpler yet effective mechanism for representing joint probability distributions of both continuous and discrete random variables. The framework allows for the construction of differentiable models for density estimation, inference, and sampling, enabling integration into end-to-end deep neural models. In doing so, we provide a versatile representation of marginal and joint probability distributions that allows us to develop a differentiable, compositional, and reversible inference procedure that covers a wide range of machine learning tasks, including density estimation, discriminative learning, and generative modeling. We illustrate the broad applicability of the framework with two examples: an image classification model, which can be naturally transformed into a conditional generative model thanks to the reversibility of our inference procedure; and a model for learning with label proportions, which is a weakly supervised classification task, demonstrating the framework's ability to deal with uncertainty in the training samples

Impact of the first wave of the COVID-19 pandemic on non-COVID inpatient care in southern Spain

We assessed the impact of the first wave of COVID-19 pandemic on non-COVID hospital admissions, non-COVID mortality, factors associated with non-COVID mortality, and changes in the profile of non-COVID patients admitted to hospital. We used the Spanish Minimum Basic Data Set with diagnosis grouped according to the Diagnostic Related Groups. A total of 10,594 patients (3% COVID-19; 97% non-COVID) hospitalised during the first wave in 2020 (27-February/07-June) were compared with those hospitalised within the same dates of 2017-2019 (average annual admissions: 14,037). We found a decrease in non-COVID medical (22%) and surgical (33%) hospitalisations and a 25.7% increase in hospital mortality among non-COVID patients during the first pandemic wave compared to pre-pandemic years. During the officially declared sub-period of excess mortality in the area (17-March/20-April, in-hospital non-COVID mortality was even higher (58.7% higher than the pre-pandemic years). Non-COVID patients hospitalised during the first pandemic wave (compared to pre-pandemic years) were older, more frequently men, with longer hospital stay and increased disease severity. Hospitalisation during the first pandemic wave in 2020, compared to hospitalisation during the pre-pandemic years, was an independent risk factor for non-COVID mortality (HR 1.30, 95% CI 1.07-1.57, p = 0.008), reflecting the negative impact of the pandemic on hospitalised patients

Phenotypic, molecular characterization, antimicrobial susceptibility and draft genome sequence of Corynebacterium argentoratense strains isolated from clinical samples

During a 12-year period we isolated five Corynebacterium argentoratense strains identified by phenotypic methods, including the use of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF) and 16S rRNA gene sequencing. In addition, antimicrobial susceptibility was determined, and genome sequencing for the detection of antibiotic resistance genes was performed. The organisms were isolated from blood and throat cultures and could be identified by all methods used. All strains were resistant to cotrimoxazole, and resistance to β-lactams was partly present. Two strains were resistant to erythromycin and clindamycin. The draft genome sequences of theses isolates revealed the presence of the erm(X) resistance gene that is embedded in the genetic structure of the transposable element Tn5423. Although rarely reported as a human pathogen, C. argentoratense can be involved in bacteraemia and probably in other infections. Our results also show that horizontal transfer of genes responsible for antibiotic resistance is occurring in this species.Supported in part by the Gerencia Regional de Salud, Junta de Castilla y León, Spain (research project GRS 698/A/2011

XML-VM: An XML-Based Grid Computing Middleware

This paper describes a novel distributing computing middleware named XML-VM. Its architecture is inspired by the \u2018Grid Computing\u2019 paradigm. The proposed system improves many characteristics of previous Grid systems, in particular the description of the distributed computation, the distribution of the code and the execution times. XML is a markup language commonly used to interchange arbitrary data over the Internet. The idea behind this work is to use XML to describe algorithms; XML documents are distributed by means of XML-RPC, interpreted and executed using virtual machines. XML-VM is an assembly-like language, coded in XML. Parsing of XML-VM programs is performed with a fast SAX parser for JAVA. XML-VM interpreter is coded in JAVA. Several algorithms are written in XML-VM and executed in a distributed environment. Representative experimental results are reported

Tratado de derechos reales. Tomo II Propiedad y posesión

La presente investigación está referida al análisis de los dos principales derechos reales: la posesión y la propiedad, y es la continuación de la investigación concluida denominada Tratado de derechos reales, tomo I, teoría de los bienes y los derechos reales. Esta parte de la investigación corresponde al desarrollo de dos de sus principales instituciones. Así, se comienza con el análisis sociojurídico de la posesión (poder hecho) como derecho transitorio (temporal y momentáneo), para luego entrar al desarrollo de la propiedad (poder de derecho) como derecho definitivo (permanente y total), ambas instituciones reconocidas como situaciones jurídicas de gran trascendencia en las relaciones jurídicas patrimoniales, protegidas por la ley a través de mecanismos de defensa

Time trends in municipal distribution patterns of cancer mortality in Spain

BACKGROUND: New disease mapping techniques widely used in small-area studies enable disease distribution patterns to be identified and have become extremely popular in the field of public health. This paper reports on trends in the geographical mortality patterns of the most frequent cancers in Spain, over a period of 20 years. METHODS: We studied the municipal spatial pattern of stomach, colorectal, lung, breast, prostate and urinary bladder cancer mortality in Spain across four quinquennia, spanning the period 1989-2008. Case data were broken down by town (8073 municipalities), period and sex. Expected cases for each town were calculated using reference rates for each five-year period. For map plotting purposes, smoothed municipal relative risks were calculated using the conditional autoregressive model proposed by Besag, York and Mollié, with independent data for each quinquennium. We evaluated the presence of spatial patterns in maps on the basis of models, calculating the variance in relative risk corresponding to the structured spatial component and the unstructured component, as well as the proportion of variance explained by the structured spatial component. RESULTS: The mortality patterns observed for stomach, colorectal and lung cancer were maintained over the 20 years covered by the study. Prostate cancer and the tumours studied in women showed no defined spatial pattern, with the single exception of stomach cancer. The trend in spatial fractional variance indicated the possibility of a change in the spatial pattern in breast, bladder and colorectal cancer in women during the last five-year period. The paper goes on to discuss ways in which spatio-temporal data are depicted in the case of cancer, and review the risk factors that may possibly influence the respective tumours’ spatial patterns. CONCLUSION: In men, the marked geographical patterns of stomach, colorectal, lung and bladder cancer remained stable over time. Breast, colorectal and bladder cancer in women show signs of the possible appearance of a spatial pattern in Spain and should therefore be monitored. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2407-14-535) contains supplementary material, which is available to authorized users

Trends in incidence, mortality and survival in women with breast cancer from 1985 to 2012 in Granada, Spain: a population-based study

The incidence of breast cancer has increased since the 1970s. Despite favorable trends in prognosis, the role of changes in clinical practice and the introduction of screening remain controversial. We examined breast cancer trends to shed light on their determinants Overall, age-adjusted (European Standard Population) incidence rates increased from 48.0 cases × 100,000 women in 1985–1989 to 83.4 in 2008–2012, with an annual percentage change (APC) of 2.5% (95%CI, 2.1–2.9) for 1985–2012. The greatest increase was in women younger than 40 years (APC 3.5, 95%CI, 2.4–4.8). For 2000–2012 the incidence trend increased only for stage I tumors (APC 3.8, 95%CI, 1.9–5.8). Overall age-adjusted breast cancer mortality decreased (APC − 1, 95%CI, − 1.4 – − 0.5), as did mortality in the 50–69 year age group (APC − 1.3, 95%CI, − 2.2 – − 0.4). Age-standardized net survival increased from 67.5% at 5 years in 1985–1989 to 83.7% in 2010–2012. All age groups younger than 70 years showed a similar evolution. Five-year net survival rates were 96.6% for patients with tumors diagnosed in stage I, 88.2% for stage II, 62.5% for stage III and 23.3% for stage IV. Breast cancer incidence is increasing – a reflection of the evolution of risk factors and increasing diagnostic pressure. After screening was introduced, the incidence of stage I tumors increased, with no decrease in the incidence of more advanced stages. Reductions were seen for overall mortality and mortality in the 50–69 year age group, but no changes were found after screening implementation. Survival trends have evolved favorably except for the 70–84 year age group and for metastatic tumors.This study was supported by a grant from the Acción Estratégica en Salud plan for the High Resolution Project on Prognosis and Care of Cancer Patients (No. AC14/00036) awarded by the Spanish Ministry of Economy and Competitiveness and co-funded by the European Regional Development Fund (ERDF)