The Principle of Proportionality and the European Arrest Warrant

<p>The European Arrest Warrant (EAW) is a grossly coercive instrument that was designed for the persecution of serious cross-border crimes. In recent years, however, Member States have increasingly reported cases in which EAWs have not been issued for serious, but rather for harmless and minor offences. This article analyses the reasons behind the disproportionate use of the EAW and outlines measures to alleviate the problem.</p> <p>Thereby, it claims that in current debates different categories of disproportionate use of EAWs are often lumped together, and only concentrate on the introduction of a (binding) proportionality test, failing to consider other alternative legislative solutions regarding minor crimes, such as the introduction of new comparable and effective alternative measures. These, however, are considered to be crucial for an alleviation of disproportionate warrants.</p> <hr /><p>La orden de detenci&oacute;n europea (ODE) es un instrumento extremadamente coercitivo que fue dise&ntilde;ado para la persecuci&oacute;n de delitos transfronterizos graves. En a&ntilde;os recientes, sin embargo, los Estados miembro han notificado cada vez m&aacute;s casos en los que la ODE no se deb&iacute;a a delitos serios, sino a casos menores e inofensivos.. En este art&iacute;culo se analizan las razones que hay detr&aacute;s del uso desproporcionado de la orden de detenci&oacute;n europea y propone medidas para paliar el problema.</p> <p>De esta manera, se defiende que el debate actual, frecuentemente agrupan diferentes categor&iacute;as de uso desproporcionado de la ODE, y s&oacute;lo se concentran en la introducci&oacute;n de un test de proporcionalidad (vinculante), sin tener en cuenta otras soluciones legislativas alternativas, en lo que respecta a delitos menores, como la introducci&oacute;n de nuevas medidas alternativas, comparables y eficaces. Sin embargo, se considera que estas medidas son cruciales para reducir las &oacute;rdenes de arresto desproporcionadas.</p> <p><strong>DOWNLOAD THIS PAPER FROM SSRN</strong>: <a href="http://ssrn.com/abstract=2200874" target="_blank">http://ssrn.com/abstract=2200874</a></p

Evaluating deep learning-based melanoma classification using immunohistochemistry and routine histology: A three center study.

Pathologists routinely use immunohistochemical (IHC)-stained tissue slides against MelanA in addition to hematoxylin and eosin (H&E)-stained slides to improve their accuracy in diagnosing melanomas. The use of diagnostic Deep Learning (DL)-based support systems for automated examination of tissue morphology and cellular composition has been well studied in standard H&E-stained tissue slides. In contrast, there are few studies that analyze IHC slides using DL. Therefore, we investigated the separate and joint performance of ResNets trained on MelanA and corresponding H&E-stained slides. The MelanA classifier achieved an area under receiver operating characteristics curve (AUROC) of 0.82 and 0.74 on out of distribution (OOD)-datasets, similar to the H&E-based benchmark classification of 0.81 and 0.75, respectively. A combined classifier using MelanA and H&E achieved AUROCs of 0.85 and 0.81 on the OOD datasets. DL MelanA-based assistance systems show the same performance as the benchmark H&E classification and may be improved by multi stain classification to assist pathologists in their clinical routine

S5 Dataset -

Pathologists routinely use immunohistochemical (IHC)-stained tissue slides against MelanA in addition to hematoxylin and eosin (H&E)-stained slides to improve their accuracy in diagnosing melanomas. The use of diagnostic Deep Learning (DL)-based support systems for automated examination of tissue morphology and cellular composition has been well studied in standard H&E-stained tissue slides. In contrast, there are few studies that analyze IHC slides using DL. Therefore, we investigated the separate and joint performance of ResNets trained on MelanA and corresponding H&E-stained slides. The MelanA classifier achieved an area under receiver operating characteristics curve (AUROC) of 0.82 and 0.74 on out of distribution (OOD)-datasets, similar to the H&E-based benchmark classification of 0.81 and 0.75, respectively. A combined classifier using MelanA and H&E achieved AUROCs of 0.85 and 0.81 on the OOD datasets. DL MelanA-based assistance systems show the same performance as the benchmark H&E classification and may be improved by multi stain classification to assist pathologists in their clinical routine.</div

Description of the population in our datasets.

For continuous features we report median, range, and number of NAs, for categorical features we report the total number of observations per group. Here the training population as well as all three test populations are described. Melanoma in situ describes the early stage of a malignant melanoma that has not yet broken through the basement membrane. However, features at the cellular level do not differ between melanoma in situ and malignant melanoma.</p

Additional results derived by using different fusion approaches: Dist-opt means weighted by the distance to the individual models optimal thresholds; dist-05 means weighted by the distance to the default threshold of 0.5; avg denotes the fusion by conducting a simple average of all scores; perf means weighted based on the individual models validation performance in a way that better performing models contribute more to the fused result.

Additional results derived by using different fusion approaches: Dist-opt means weighted by the distance to the individual models optimal thresholds; dist-05 means weighted by the distance to the default threshold of 0.5; avg denotes the fusion by conducting a simple average of all scores; perf means weighted based on the individual models validation performance in a way that better performing models contribute more to the fused result.</p

S4 Dataset -

Pathologists routinely use immunohistochemical (IHC)-stained tissue slides against MelanA in addition to hematoxylin and eosin (H&E)-stained slides to improve their accuracy in diagnosing melanomas. The use of diagnostic Deep Learning (DL)-based support systems for automated examination of tissue morphology and cellular composition has been well studied in standard H&E-stained tissue slides. In contrast, there are few studies that analyze IHC slides using DL. Therefore, we investigated the separate and joint performance of ResNets trained on MelanA and corresponding H&E-stained slides. The MelanA classifier achieved an area under receiver operating characteristics curve (AUROC) of 0.82 and 0.74 on out of distribution (OOD)-datasets, similar to the H&E-based benchmark classification of 0.81 and 0.75, respectively. A combined classifier using MelanA and H&E achieved AUROCs of 0.85 and 0.81 on the OOD datasets. DL MelanA-based assistance systems show the same performance as the benchmark H&E classification and may be improved by multi stain classification to assist pathologists in their clinical routine.</div

Antibodies and parameters of staining methods used by the different clinics.

Antibodies and parameters of staining methods used by the different clinics.</p

Schematic diagram of the different models.

The red box shows the pipeline for MelanA-stained WSIs and the purple box the pipeline for H&E-stained WSIs. We tessellated MelanA-stained WSIs corresponding to different magnifications and trained individual models on each tile size. The class probabilities for each tile were predicted and aggregated into a slide score by averaging all tile scores. For the H&E-based model we proceeded in the same way.</p

AUROC values as well as 95% bootstrapped CIs for the three test cohorts and all evolved models.

AUROC values as well as 95% bootstrapped CIs for the three test cohorts and all evolved models.</p

ROC plots by data source site with corresponding AUROC values.

A: Results from Dresden B: Results from Erlangen C: Results from Naples. Red: 40x magnification Blue: 20x magnification Purple: 10x magnification Gray: 5x magnification. (TIF)</p