MorphoCluster: Efficient Annotation of Plankton Images by Clustering

In this work, we present MorphoCluster, a software tool for data-driven, fast, and accurate annotation of large image data sets. While already having surpassed the annotation rate of human experts, volume and complexity of marine data will continue to increase in the coming years. Still, this data requires interpretation. MorphoCluster augments the human ability to discover patterns and perform object classification in large amounts of data by embedding unsupervised clustering in an interactive process. By aggregating similar images into clusters, our novel approach to image annotation increases consistency, multiplies the throughput of an annotator, and allows experts to adapt the granularity of their sorting scheme to the structure in the data. By sorting a set of 1.2 M objects into 280 data-driven classes in 71 h (16 k objects per hour), with 90% of these classes having a precision of 0.889 or higher. This shows that MorphoCluster is at the same time fast, accurate, and consistent; provides a fine-grained and data-driven classification; and enables novelty detection

S2C2 -- An orthogonal method for Semi-Supervised Learning on ambiguous labels

Semi-Supervised Learning (SSL) can decrease the required amount of labeled image data and thus the cost for deep learning. Most SSL methods assume a clear distinction between classes, but class boundaries are often ambiguous in real-world datasets due to intra- or interobserver variability. This ambiguity of annotations must be addressed as it will otherwise limit the performance of SSL and deep learning in general due to inconsistent label information. We propose Semi-Supervised Classification & Clustering (S2C2) which can extend many deep SSL algorithms. S2C2 automatically estimates the ambiguity of an image and applies the respective SSL algorithm as a classification to certainly labeled data while partitioning the ambiguous data into clusters of visual similar images. We show that S2C2 results in a 7.6% better F1-score for classifications and 7.9% lower inner distance of clusters on average across multiple SSL algorithms and datasets. Moreover, the output of S2C2 can be used to decrease the ambiguity of labels with the help of human experts. Overall, a combination of Semi-Supervised Learning with our method S2C2 leads to better handling of ambiguous labels and thus real-world datasets

PlanktonID – Combining deep learning, in situ imaging and citizen science to resolve the distribution of zooplanktonin major upwelling regions

Recent publications revealed the global importance of single-celled zooplankton, belonging to the super group Rhizaria and highlighted the need of in-situ imaging to study these fragile organisms. The advance of in situ plankton imaging techniques leads to increasing amounts of image data sets that require identification to different taxonomic levels. Automatic classification by computer algorithms provides the means for fast data availability, however the accuracy of those algorithms still requires manual identification by humans. We combined state of the art automatic image classification by convolutional neural networks (deep learning) with a citizen science project to classify a large dataset of ~ 3 million images from an Underwater Vision Profiler 5 (UVP5). On our website https://planktonid.geomar.de, citizen scientists can confirm or reject the automatic assignment of UVP5 images to different plankton categories in a memory-like game. Inbuilt quality controls and multiple validations per image enable scientific analysis of the citizen science data. In total more than 500 users have validated more than 300.000 images until now. We will present further data on citizen scientist engagement, data quality assessment and the distribution analysis of large protists (Rhizaria) in the Mauretanian, Benguela and Humboldt Current upwelling systems

The behaviour of political parties and MPs in the parliaments of the Weimar Republic

Copyright @ 2012 The Authors. This is the author's accepted manuscript. The final published article is available from the link below.Analysing the roll-call votes of the MPs of the Weimar Republic we find: (1) that party competition in the Weimar parliaments can be structured along two dimensions: an economic left–right and a pro-/anti-democratic. Remarkably, this is stable throughout the entire lifespan of the Republic and not just in the later years and despite the varying content of votes across the lifespan of the Republic, and (2) that nearly all parties were troubled by intra-party divisions, though, in particular, the national socialists and communists became homogeneous in the final years of the Republic.Zukunftskolleg, University of Konstan

Particulate matter flux interception in oceanic mesoscale eddies by the polychaete Poeobius sp.

Gelatinous zooplankton hold key functions in the ocean and have been shown to significantly influence the transport of organic carbon to the deep sea. We discovered a gelatinous, flux‐feeding polychaete of the genus Poeobius in very high abundances in a mesoscale eddy in the tropical Atlantic Ocean, where it co‐occurred with extremely low particle concentrations. Subsequent analysis of an extensive in situ imaging dataset revealed that Poeobius sp. occurred sporadically between 5°S–20°N and 16°W–46°W in the upper 1000 m. Abundances were significantly elevated and the depth distribution compressed in anticyclonic modewater eddies (ACMEs). In two ACMEs, high Poeobius sp. abundances were associated with strongly reduced particle concentrations and fluxes in the layers directly below the polychaete. We discuss possible reasons for the elevated abundances of Poeobius sp. in ACMEs and provide estimations showing that a single zooplankton species can completely intercept the downward particle flux by feeding with their mucous nets, thereby substantially altering the biogeochemical setting within the eddy

Controlled density-downramp injection in a beam-driven plasma wakefield accelerator

This paper describes the utilization of beam-driven plasma wakefield acceleration to implement a high-quality plasma cathode via density-downramp injection in a short injector stage at the FLASHForward facility at DESY. Electron beams with charge of up to 105 pC and energy spread of a few percent were accelerated by a tunable effective accelerating field of up to 2.7 GV/m. The plasma cathode was operated drift-free with very high injection efficiency. Sources of jitter, the emittance and divergence of the resulting beam were investigated and modeled, as were strategies for performance improvements that would further increase the wide-ranging applications for a plasma cathode with the demonstrated operational stabilityComment: 11 pages, 9 figure

Shower development of particles with momenta from 15 GeV to 150 GeV in the CALICE scintillator-tungsten hadronic calorimeter

We present a study of showers initiated by electrons, pions, kaons, and protons with momenta from 15 GeV to 150 GeV in the highly granular CALICE scintillator-tungsten analogue hadronic calorimeter. The data were recorded at the CERN Super Proton Synchrotron in 2011. The analysis includes measurements of the calorimeter response to each particle type as well as measurements of the energy resolution and studies of the longitudinal and radial shower development for selected particles. The results are compared to Geant4 simulations (version 9.6.p02). In the study of the energy resolution we include previously published data with beam momenta from 1 GeV to 10 GeV recorded at the CERN Proton Synchrotron in 2010.Comment: 35 pages, 21 figures, 8 table

Enhancement of CO2 uptake and selectivity in a metal-organic framework by incorporation of thiophene functionality

The complex [Zn2(tdc)2dabco] (H2tdc = thiophene-2,5-dicarboxylic acid; dabco = 1,4-diazabicyclooctane) shows a remarkable increase in CO2 uptake and CO2/N2 selectivity compared to the non-thiophene analogue [Zn2(bdc)2dabco] (H2bdc = benzene-1,4-dicarboxylic acid; terephthalic acid). CO2 adsorption at 1 bar for [Zn2(tdc)2dabco] is 67.4 cm3 x g–1 (13.2 wt.%) at 298 K and 153 cm3 x g–1 (30.0 wt.%) at 273 K. For [Zn2(bdc)2dabco] the equivalent values are 46 cm3 x g–1 (9.0 wt.%) and 122 cm3 x g–1 (23.9 wt.%), respectively. The isosteric heat of adsorption for CO2 in [Zn2(tdc)2dabco] at zero coverage is low (23.65 kJ x mol–1), ensuring facile regeneration of the porous material. The enhancement by the thiophene group on the separation of CO2/N2 gas mixtures has been confirmed by both ideal adsorbate solution theory (IAST) calculations and dynamic breakthrough experiments. The preferred binding sites of adsorbed CO2 in [Zn2(tdc)2dabco] have been unambiguously determined by in situ single crystal diffraction studies on CO2 loaded [Zn2(tdc)2dabco], coupled with quantum chemical calculations. These studies unveil the role of the thiophene moieties in the specific CO2 binding via an induced dipole interaction between the CO2 and the sulfur center, confirming that enhanced CO2 capacity in [Zn2(tdc)2dabco] is achieved without the presence of open metal sites. The experimental data and the theoretical insights suggest a viable strategy for improvement of adsorption properties of already known materials through incorporation of S-based heterocycles within their porous structures