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Article: HOSPITAL DEPARTMENT - SAN FRANCISCO HOSPITALS.

Dorr, W R

2008 Volume 10, Issue 1, Page(s) 38–40

Language	English
Publishing date	2008-08-20
Publishing country	United States
Document type	Journal Article
ZDB-ID	2255409-9
ISSN	0093-402X
ISSN	0093-402X
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Impact of certification on fruit producers in the Sao Francisco Valley in Brazil

Andréa Cristina DÖRR / Ulrike GROTE

Annals of Dunarea de Jos University. Fascicle I : Economics and Applied Informatics, Iss 2, Pp 5-

2009 Volume 16

Abstract: ... regions of the Sao FranciscoValley in Brazil. Certified and non-certified farmers as well ...

Abstract	Producers and exporters of fresh fruits and vegetables from developingcountries like Brazil are increasingly required to demonstrate the safety andtraceability of their produce up to the consumption stage. In fact, the Brazilianexport market is still relatively underdeveloped, with an export share of only2.4% of the total produced volume. However, certification may also have theeffect of a non-tariff trade barrier, undermining the capability and financialability of especially small-scale farmers in exporting to international markets.This study, therefore, aims at providing an economic analysis of certification onmango and grapes producers. A survey of 303 grapes and mango farmers wasconducted in 2006 in the Juazeiro and Petrolina regions of the Sao FranciscoValley in Brazil. Certified and non-certified farmers as well as those in processto obtain certification were included in the sample. Empirical analysis using alogit model shows that grapes farmers have higher probability to certify thanmango growers. There are two variables which have a positive and significanteffect: education and experience. However, small-scale farms, the dependencyon non-agricultural income and a trust-based arrangement have a negative butsignificant effect.
Keywords	Certification ; fruits ; logit model ; Electronic computers. Computer science ; QA75.5-76.95 ; Economic theory. Demography ; HB1-3840 ; Economics as a science ; HB71-74
Language	English
Publishing date	2009-01-01T00:00:00Z
Publisher	Dunarea de Jos University of Galati
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Satellite Image Multi-Frame Super Resolution Using 3D Wide-Activation Neural Networks

Francisco Dorr

Remote Sensing, Vol 12, Iss 3812, p

2020 Volume 3812

Abstract: The small satellite market continues to grow year after year. A compound annual growth rate of 17% is estimated during the period between 2020 and 2025. Low-cost satellites can send a vast amount of images to be post-processed at the ground to improve ... ...

Abstract	The small satellite market continues to grow year after year. A compound annual growth rate of 17% is estimated during the period between 2020 and 2025. Low-cost satellites can send a vast amount of images to be post-processed at the ground to improve the quality and extract detailed information. In this domain lies the resolution enhancement task, where a low-resolution image is converted to a higher resolution automatically. Deep learning approaches to Super Resolution (SR) reached the state-of-the-art in multiple benchmarks; however, most of them were studied in a single-frame fashion. With satellite imagery, multi-frame images can be obtained at different conditions giving the possibility to add more information per image and improve the final analysis. In this context, we developed and applied to the PROBA-V dataset of multi-frame satellite images a model that recently topped the European Space Agency’s Multi-frame Super Resolution (MFSR) competition. The model is based on proven methods that worked on 2D images tweaked to work on 3D: the Wide Activation Super Resolution (WDSR) family. We show that with a simple 3D CNN residual architecture with WDSR blocks and a frame permutation technique as the data augmentation, better scores can be achieved than with more complex models. Moreover, the model requires few hardware resources, both for training and evaluation, so it can be applied directly on a personal laptop.
Keywords	multi-frame super resolution ; wide activation super resolution ; 3D convolutional neural network ; deep learning ; Science ; Q
Subject code	006
Language	English
Publishing date	2020-11-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Counter-intuitive penetration of droplets into hydrophobic gaps in theory and experiment.

Hagg, Daniel / Eifert, Alexander / Dörr, Aaron / Bodziony, Francisco / Marschall, Holger

Scientific reports

2023 Volume 13, Issue 1, Page(s) 16518

Abstract: Droplets that spontaneously penetrate a gap between two hydrophobic surfaces without any external stimulus seems counterintuitive. However, in this work we show that it can be energetically favorable for a droplet to penetrate a gap formed by two ... ...

Abstract	Droplets that spontaneously penetrate a gap between two hydrophobic surfaces without any external stimulus seems counterintuitive. However, in this work we show that it can be energetically favorable for a droplet to penetrate a gap formed by two hydrophobic or in some cases even superhydrophobic surfaces. For this purpose, we derived an analytical equation to calculate the change in Helmholtz free energy of a droplet penetrating a hydrophobic gap. The derived equation solely depends on the gap width, the droplet volume and the contact angle on the gap walls, and predicts whether a droplet penetrates a hydrophobic gap or not. Additionally, numerical simulations were conducted to provide insights into the gradual change in Helmholtz free energy during the process of penetration and to validate the analytical approach. A series of experiments with a hydrophobic gap having an advancing contact angle of [Formula: see text], a droplet volume of about 10 [Formula: see text]L and different gap widths confirmed the theoretical predictions. Limits and possible deviations between the analytical solution, the simulation and the experiments are presented and discussed.
Language	English
Publishing date	2023-10-02
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-43138-2
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Satellite Image Multi-Frame Super Resolution Using 3D Wide-Activation Neural Networks

Dorr, Francisco

Remote Sensing. 2020 Nov. 20, v. 12, no. 22

2020

Abstract	The small satellite market continues to grow year after year. A compound annual growth rate of 17% is estimated during the period between 2020 and 2025. Low-cost satellites can send a vast amount of images to be post-processed at the ground to improve the quality and extract detailed information. In this domain lies the resolution enhancement task, where a low-resolution image is converted to a higher resolution automatically. Deep learning approaches to Super Resolution (SR) reached the state-of-the-art in multiple benchmarks; however, most of them were studied in a single-frame fashion. With satellite imagery, multi-frame images can be obtained at different conditions giving the possibility to add more information per image and improve the final analysis. In this context, we developed and applied to the PROBA-V dataset of multi-frame satellite images a model that recently topped the European Space Agency’s Multi-frame Super Resolution (MFSR) competition. The model is based on proven methods that worked on 2D images tweaked to work on 3D: the Wide Activation Super Resolution (WDSR) family. We show that with a simple 3D CNN residual architecture with WDSR blocks and a frame permutation technique as the data augmentation, better scores can be achieved than with more complex models. Moreover, the model requires few hardware resources, both for training and evaluation, so it can be applied directly on a personal laptop.
Keywords	architecture ; artificial intelligence ; data collection ; information ; markets ; neural networks ; remote sensing ; satellites
Language	English
Dates of publication	2020-1120
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
Note	NAL-light
ZDB-ID	2513863-7
ISSN	2072-4292
ISSN	2072-4292
DOI	10.3390/rs12223812
Database	NAL-Catalogue (AGRICOLA)

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Book ; Online: Exploring DINO

Gallego-Mejia, Joseph A. / Jungbluth, Anna / Martínez-Ferrer, Laura / Allen, Matt / Dorr, Francisco / Kalaitzis, Freddie / Ramos-Pollán, Raúl

Emergent Properties and Limitations for Synthetic Aperture Radar Imagery

2023

Abstract: Self-supervised learning (SSL) models have recently demonstrated remarkable performance across various tasks, including image segmentation. This study delves into the emergent characteristics of the Self-Distillation with No Labels (DINO) algorithm and ... ...

Abstract	Self-supervised learning (SSL) models have recently demonstrated remarkable performance across various tasks, including image segmentation. This study delves into the emergent characteristics of the Self-Distillation with No Labels (DINO) algorithm and its application to Synthetic Aperture Radar (SAR) imagery. We pre-train a vision transformer (ViT)-based DINO model using unlabeled SAR data, and later fine-tune the model to predict high-resolution land cover maps. We rigorously evaluate the utility of attention maps generated by the ViT backbone and compare them with the model's token embedding space. We observe a small improvement in model performance with pre-training compared to training from scratch and discuss the limitations and opportunities of SSL for remote sensing and land cover segmentation. Beyond small performance increases, we show that ViT attention maps hold great intrinsic value for remote sensing, and could provide useful inputs to other algorithms. With this, our work lays the groundwork for bigger and better SSL models for Earth Observation. Comment: 9 pages, 5 figures
Keywords	Computer Science - Computer Vision and Pattern Recognition ; I.4.8 ; I.5
Subject code	006
Publishing date	2023-10-05
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Exploring Generalisability of Self-Distillation with No Labels for SAR-Based Vegetation Prediction

Martínez-Ferrer, Laura / Jungbluth, Anna / Gallego-Mejia, Joseph A. / Allen, Matt / Dorr, Francisco / Kalaitzis, Freddie / Ramos-Pollán, Raúl

2023

Abstract: In this work we pre-train a DINO-ViT based model using two Synthetic Aperture Radar datasets (S1GRD or GSSIC) across three regions (China, Conus, Europe). We fine-tune the models on smaller labeled datasets to predict vegetation percentage, and ... ...

Abstract	In this work we pre-train a DINO-ViT based model using two Synthetic Aperture Radar datasets (S1GRD or GSSIC) across three regions (China, Conus, Europe). We fine-tune the models on smaller labeled datasets to predict vegetation percentage, and empirically study the connection between the embedding space of the models and their ability to generalize across diverse geographic regions and to unseen data. For S1GRD, embedding spaces of different regions are clearly separated, while GSSIC's overlaps. Positional patterns remain during fine-tuning, and greater distances in embeddings often result in higher errors for unfamiliar regions. With this, our work increases our understanding of generalizability for self-supervised models applied to remote sensing. Comment: 10 pages, 9 figures
Keywords	Computer Science - Computer Vision and Pattern Recognition ; I.4.8 ; I.5
Publishing date	2023-10-03
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Fewshot learning on global multimodal embeddings for earth observation tasks

Allen, Matt / Dorr, Francisco / Gallego-Mejia, Joseph A. / Martínez-Ferrer, Laura / Jungbluth, Anna / Kalaitzis, Freddie / Ramos-Pollán, Raúl

2023

Abstract: In this work we pretrain a CLIP/ViT based model using three different modalities of satellite imagery across five AOIs covering over ~10\% of Earth's total landmass, namely Sentinel 2 RGB optical imagery, Sentinel 1 SAR radar amplitude and ... ...

Abstract	In this work we pretrain a CLIP/ViT based model using three different modalities of satellite imagery across five AOIs covering over ~10\% of Earth's total landmass, namely Sentinel 2 RGB optical imagery, Sentinel 1 SAR radar amplitude and interferometric coherence. This model uses $\sim 250$ M parameters. Then, we use the embeddings produced for each modality with a classical machine learning method to attempt different downstream tasks for earth observation related to vegetation, built up surface, croplands and permanent water. We consistently show how we reduce the need for labeled data by 99\%, so that with ~200-500 randomly selected labeled examples (around 4K-10K km$^2$) we reach performance levels analogous to those achieved with the full labeled datasets (about 150K image chips or 3M km$^2$ in each area of interest - AOI) on all modalities, AOIs and downstream tasks. This leads us to think that the model has captured significant earth features useful in a wide variety of scenarios. To enhance our model's usability in practice, its architecture allows inference in contexts with missing modalities and even missing channels within each modality. Additionally, we visually show that this embedding space, obtained with no labels, is sensible to the different earth features represented by the labelled datasets we selected. Comment: 9 pages, 6 figures, presented on NeurIPS workshop on Robustness of Few-shot and Zero-shot Learning in Foundation Models
Keywords	Computer Science - Computer Vision and Pattern Recognition ; I.4.8 ; I.5
Subject code	006
Publishing date	2023-09-29
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Large Scale Masked Autoencoding for Reducing Label Requirements on SAR Data

Allen, Matt / Dorr, Francisco / Gallego-Mejia, Joseph A. / Martínez-Ferrer, Laura / Jungbluth, Anna / Kalaitzis, Freddie / Ramos-Pollán, Raúl

2023

Abstract: Satellite-based remote sensing is instrumental in the monitoring and mitigation of the effects of anthropogenic climate change. Large scale, high resolution data derived from these sensors can be used to inform intervention and policy decision making, ... ...

Abstract	Satellite-based remote sensing is instrumental in the monitoring and mitigation of the effects of anthropogenic climate change. Large scale, high resolution data derived from these sensors can be used to inform intervention and policy decision making, but the timeliness and accuracy of these interventions is limited by use of optical data, which cannot operate at night and is affected by adverse weather conditions. Synthetic Aperture Radar (SAR) offers a robust alternative to optical data, but its associated complexities limit the scope of labelled data generation for traditional deep learning. In this work, we apply a self-supervised pretraining scheme, masked autoencoding, to SAR amplitude data covering 8.7\% of the Earth's land surface area, and tune the pretrained weights on two downstream tasks crucial to monitoring climate change - vegetation cover prediction and land cover classification. We show that the use of this pretraining scheme reduces labelling requirements for the downstream tasks by more than an order of magnitude, and that this pretraining generalises geographically, with the performance gain increasing when tuned downstream on regions outside the pretraining set. Our findings significantly advance climate change mitigation by facilitating the development of task and region-specific SAR models, allowing local communities and organizations to deploy tailored solutions for rapid, accurate monitoring of climate change effects. Comment: 12 pages, 6 figures
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Electrical Engineering and Systems Science - Image and Video Processing ; I.4.8 ; I.5
Subject code	910
Publishing date	2023-10-01
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Brain volumes quantification from MRI in healthy controls: Assessing correlation, agreement and robustness of a convolutional neural network-based software against FreeSurfer, CAT12 and FSL.

Chaves, Hernán / Dorr, Francisco / Costa, Martín Elías / Serra, María Mercedes / Slezak, Diego Fernández / Farez, Mauricio F / Sevlever, Gustavo / Yañez, Paulina / Cejas, Claudia

Journal of neuroradiology = Journal de neuroradiologie

2020 Volume 48, Issue 3, Page(s) 147–156

Abstract: Background and purpose: There are instances in which an estimate of the brain volume should be obtained from MRI in clinical practice. Our objective is to calculate cross-sectional robustness of a convolutional neural network (CNN) based software ( ... ...

Abstract	Background and purpose: There are instances in which an estimate of the brain volume should be obtained from MRI in clinical practice. Our objective is to calculate cross-sectional robustness of a convolutional neural network (CNN) based software (Entelai Pic) for brain volume estimation and compare it to traditional software such as FreeSurfer, CAT12 and FSL in healthy controls (HC). Materials and methods: Sixteen HC were scanned four times, two different days on two different MRI scanners (1.5 T and 3 T). Volumetric T1-weighted images were acquired and post-processed with FreeSurfer v6.0.0, Entelai Pic v2, CAT12 v12.5 and FSL v5.0.9. Whole-brain, grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) volumes were calculated. Correlation and agreement between methods was assessed using intraclass correlation coefficient (ICC) and Bland Altman plots. Robustness was assessed using the coefficient of variation (CV). Results: Whole-brain volume estimation had better correlation between FreeSurfer and Entelai Pic (ICC (95% CI) 0.96 (0.94-0.97)) than FreeSurfer and CAT12 (0.92 (0.88-0.96)) and FSL (0.87 (0.79-0.91)). WM, GM and CSF showed a similar trend. Compared to FreeSurfer, Entelai Pic provided similarly robust segmentations of brain volumes both on same-scanner (mean CV 1.07, range 0.20-3.13% vs. mean CV 1.05, range 0.21-3.20%, p = 0.86) and on different-scanner variables (mean CV 3.84, range 2.49-5.91% vs. mean CV 3.84, range 2.62-5.13%, p = 0.96). Mean post-processing times were 480, 5, 40 and 5 min for FreeSurfer, Entelai Pic, CAT12 and FSL respectively. Conclusion: Based on robustness and processing times, our CNN-based model is suitable for cross-sectional volumetry on clinical practice.
MeSH term(s)	Brain/diagnostic imaging ; Cross-Sectional Studies ; Humans ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging ; Neural Networks, Computer ; Software
Language	English
Publishing date	2020-11-01
Publishing country	France
Document type	Journal Article
ZDB-ID	131763-5
ISSN	1773-0406 ; 0150-9861
ISSN (online)	1773-0406
ISSN	0150-9861
DOI	10.1016/j.neurad.2020.10.001
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