José Manuel González y Fernández Valles

ORCID: 0000-0003-1093-8644
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About
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Research Areas
  • Comparative Literary Analysis and Criticism
  • Latin American Literature Studies
  • Archaeological and Historical Studies
  • Latin American Literature Analysis
  • Medieval Architecture and Archaeology
  • Spanish Literature and Culture Studies
  • Literary and Cultural Studies
  • Archaeological and Geological Studies
  • Spanish Philosophy and Literature
  • Advanced Neural Network Applications
  • Cultural and Social Studies in Latin America
  • Cultural and Mythological Studies
  • Advanced Image and Video Retrieval Techniques
  • Historical Studies of Medieval Iberia
  • Libraries, Manuscripts, and Books
  • Multimodal Machine Learning Applications
  • Early Modern Spanish Literature
  • Historical Art and Architecture Studies
  • Robotics and Sensor-Based Localization
  • Emergency and Acute Care Studies
  • Medieval Iberian Studies
  • Historical and Literary Analyses
  • Literature, Culture, and Aesthetics
  • Spanish Culture and Identity
  • Pneumonia and Respiratory Infections

Universidad de Salamanca
2013-2024

Universidad de Alcalá
2019

Friedrich-Alexander-Universität Erlangen-Nürnberg
2015-2017

Universitat Autònoma de Barcelona
2017

Universidad Externado de Colombia
2013-2016

Bellvitge University Hospital
1994-2016

Universidad Simón Bolívar
2015

Universidad Centroamericana José Simeón Cañas
2006-2014

Hospital Universitari Germans Trias i Pujol
2007

Universidad de Sevilla
2006

In this paper, we propose M$^2$BEV, a unified framework that jointly performs 3D object detection and map segmentation in the Birds Eye View~(BEV) space with multi-camera image inputs. Unlike majority of previous works which separately process segmentation, M$^2$BEV infers both tasks model improves efficiency. efficiently transforms multi-view 2D features into BEV feature ego-car coordinates. Such representation is important as it enables different to share single encoder. Our further...

10.48550/arxiv.2204.05088 preprint EN other-oa arXiv (Cornell University) 2022-01-01

False negatives (FN) in 3D object detection, e.g., missing predictions of pedestrians, vehicles, or other obstacles, can lead to potentially dangerous situations autonomous driving. While being fatal, this issue is understudied many current detection methods. In work, we propose Hard Instance Probing (HIP), a general pipeline that identifies FN multi-stage manner and guides the models focus on excavating difficult instances. For instantiate method as FocalFormer3D, simple yet effective...

10.1109/iccv51070.2023.00771 article EN 2021 IEEE/CVF International Conference on Computer Vision (ICCV) 2023-10-01

10.1109/cvpr52733.2024.01901 article EN 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024-06-16

<h3>Introduction</h3> Healthcare-associated pneumonia (HCAP) is actually considered a subgroup of hospital-acquired due to the reported high risk multidrug-resistant pathogens in USA. Therefore, current American Thoracic Society/Infectious Diseases Society America guidelines suggest nosocomial antibiotic treatment for HCAP. Unfortunately, scientific evidence supporting this contradictory. <h3>Methods</h3> We conducted prospective multicentre case–control study Spain, comparing clinical...

10.1136/thoraxjnl-2013-203828 article EN Thorax 2013-10-15

We propose Mask Auto-Labeler (MAL), a high-quality Transformer-based mask auto-labeling framework for instance segmentation using only box annotations. MAL takes box-cropped images as inputs and conditionally generates their pseudo-labels. show that Vision Transformers are good auto-labelers. Our method significantly reduces the gap between human annotation regarding quality. Instance models trained MAL-generated masks can nearly match performance of fully-supervised counterparts, retaining...

10.1109/cvpr52729.2023.02274 article EN 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2023-06-01

The total medical costs of community-acquired pneumonia are directly related to the hospital admission and length stay. aim present study was evaluate reasons for prolonged duration stay in patients stratified five risk classes death, identify factors associated with population consisted 295 patients. According lower (classes I, II, III) or higher IV, V) risk, target hospitalization set at 5 7 days, respectively. causes were classified as pneumonia-related, complications, unstable comorbid...

10.1183/09031936.01.00090001 article EN European Respiratory Journal 2001-07-01

Abstract Background and aim Registries are useful to address questions that difficult answer in clinical trials. The objective of this study was describe compare two heart failure (HF) cohorts from Spanish HF registries. Methods We compared the RICA EAHFE registries, both which prospective multicentre cohort studies including patients with decompensated consecutively admitted internal medicine wards (RICA) or attending emergency department (EAHFE). From latter registry we only included who...

10.1136/postgradmedj-2015-133739 article EN Postgraduate Medical Journal 2016-01-06

Guidelines have been developed to improve the treatment of community-acquired pneumonia (CAP) but information regarding their influence on costs is lacking. The aim present study was conduct a cost-effectiveness analysis CAP from hospital perspective when adhering Spanish guidelines. A prospective cohort performed in 271 patients with admitted tertiary-care hospital, not needing intensive care. Collected data included patients' characteristics, comorbidity, initial risk class, resource use...

10.1183/09031936.00052506 article EN European Respiratory Journal 2006-09-27

Dexketoprofen trometamol, a high water-soluble salt of the active enantiomer rac-ketoprofen, is nonsteroidal antiinflammatory drug (NSAID) widely used for pain relief. This study was conducted to determine pharmacokinetics this analgesic agent in elderly subjects and compare them with young volunteers following single repeated oral doses. Twelve healthy 12 received 25 mg dexketo- profen (equivalent 37 its tromethamine salt) as dose (day 1) 3-day doses (1 every 8 h total 10 doses). Serial...

10.1358/mf.2006.28.1.962772 article EN Methods and Findings in Experimental and Clinical Pharmacology 2006-01-01

10.1109/cvpr52733.2024.01407 article EN 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024-06-16

Rolipram (0.1-1000 micrograms kg-1, i.v.) reduced the increase in microvascular permeability induced by platelet-activating factor (PAF; 50 ng at different sites of guinea-pig airways. (1-100 inhibited histamine (30 i.v.)- and bradykinin (0.3 i.v.)-induced airway leakage. These effects rolipram were obtained doses which inhibit (7-20 kg-1 min-1)-induced bronchoconstriction (IC50 = 3 +/- 1 without depressing arterial blood pressure guinea-pig. Aminophylline (50 mg kg-1) did not change effect...

10.1111/j.2042-7158.1993.tb07188.x article EN Journal of Pharmacy and Pharmacology 1993-12-01

La intensificación de las relaciones comerciales a través la consolidación acuerdos regionales ha sido una tendencia constante partir los noventas. Valiéndose reciente firma del Acuerdo Asociación entre UE y dos países miembros CAN (Colombia Perú), se muestra que el esquema negociación bloque devino en multi-partes. El objetivo este artículo es examinar manejo dado por al derecho comunitario andino iniciativa negociar con UE. A metodología descriptiva, analizará cronológicamente dinámica...

10.18041/0124-0021/dialogos.38.2013.1824 article ES Diálogos de saberes 2013-06-01

Improving the detection of distant 3d objects is an important yet challenging task. For camera-based 3D perception, annotation bounding relies heavily on LiDAR for accurate depth information. As such, distance often limited due to sparsity points objects, which hampers capability existing detectors long-range scenarios. We address this challenge by considering only 2D box supervision since they are easy annotate. propose LR3D, a framework that learns recover missing objects. LR3D adopts...

10.48550/arxiv.2403.09230 preprint EN arXiv (Cornell University) 2024-03-14

The cornerstone of autonomous vehicles (AV) is a solid perception system, where camera encoders play crucial role. Existing works usually leverage pre-trained Convolutional Neural Networks (CNN) or Vision Transformers (ViTs) designed for general vision tasks, such as image classification, segmentation, and 2D detection. Although those well-known architectures have achieved state-of-the-art accuracy in AV-related e.g., 3D Object Detection, there remains significant potential improvement...

10.48550/arxiv.2407.07276 preprint EN arXiv (Cornell University) 2024-07-09
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