GiViP: A Visual Profiler for Distributed Graph Processing Systems

Analyzing large-scale graphs provides valuable insights in different application scenarios. While many graph processing systems working on top of distributed infrastructures have been proposed to deal with big graphs, the tasks of profiling and debugging their massive computations remain time consuming and error-prone. This paper presents GiViP, a visual profiler for distributed graph processing systems based on a Pregel-like computation model. GiViP captures the huge amount of messages exchanged throughout a computation and provides an interactive user interface for the visual analysis of the collected data. We show how to take advantage of GiViP to detect anomalies related to the computation and to the infrastructure, such as slow computing units and anomalous message patterns.Comment: Appears in the Proceedings of the 25th International Symposium on Graph Drawing and Network Visualization (GD 2017

Genetic dissection of shoot development in maize

The isolation of genes affecting plant shoot formation is an important prerequisite for understanding the logic of plant development as well as for manipulating plant architecture. To this aim, maize transpositional mutagenesis has been adopted in our laboratory and has led to the isolation of different developmental mutants that are currently under study. Two of them, shootmeristemless (sml) and fused leaves (fdl), are presently described. Their locations, on the long arm of chromosome 10 and chromosome 7 respectively, have been established by traditional B-A translocation mapping and subsequently by linkage analysis with visible, as well as molecular, markers. The sml gene is a recessive mutation affecting shoot apical meristem maintenance and lateral organ formation. Its introgression in different genetic backgrounds has highlighted the epistatic interaction between sml and the unlinked distorted growth (dgr) locus. Seeds homozygous for both sml and dgr loci have a shootless phenotype whereas Dgr/-sml/sml seeds produce plants with altered fillotaxis and abnormal leaf morphogenesis. Microscopy analysis of young dgr primordia will be presented showing that the mutation does not interfere with epidermis formation although cell shape is less regular than in the wild-type. The fdl mutation confers a pleiotropic phenotype to the maize plant, which is specifically confined to the juvenile phase. The lack of a regular coleoptile opening that in normal development occurs after germination, is one of the fdl specific traits that we are presently investigating, for its putative relationship with the programmed cell death (PCD) process. We will present preliminary data on the cloning of the fdl gene along with a description of the strategy we have undertaken for the isolation of novel alleles. They will constitute proof for the gene identity and will provide new genetic material suitable for functional studies

Benchmarking Materials Property Prediction Methods: The Matbench Test Set and Automatminer Reference Algorithm

We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning (ML) models for predicting properties of inorganic bulk materials. The test suite, Matbench, is a set of 13 ML tasks that range in size from 312 to 132k samples and contain data from 10 density functional theory-derived and experimental sources. Tasks include predicting optical, thermal, electronic, thermodynamic, tensile, and elastic properties given a materials composition and/or crystal structure. The reference algorithm, Automatminer, is a highly-extensible, fully-automated ML pipeline for predicting materials properties from materials primitives (such as composition and crystal structure) without user intervention or hyperparameter tuning. We test Automatminer on the Matbench test suite and compare its predictive power with state-of-the-art crystal graph neural networks and a traditional descriptor-based Random Forest model. We find Automatminer achieves the best performance on 8 of 13 tasks in the benchmark. We also show our test suite is capable of exposing predictive advantages of each algorithm - namely, that crystal graph methods appear to outperform traditional machine learning methods given ~10^4 or greater data points. The pre-processed, ready-to-use Matbench tasks and the Automatminer source code are open source and available online (http://hackingmaterials.lbl.gov/automatminer/). We encourage evaluating new materials ML algorithms on the MatBench benchmark and comparing them against the latest version of Automatminer.Comment: Main text, supplemental inf

GaAs-Based Superluminescent Light-Emitting Diodes with 290-nm Emission Bandwidth by Using Hybrid Quantum Well/Quantum Dot Structures

A high-performance superluminescent light-emitting diode (SLD) based upon a hybrid quantum well (QW)/quantum dot (QD) active element is reported and is assessed with regard to the resolution obtainable in an optical coherence tomography system. We report on the appearance of strong emission from higher order optical transition from the QW in a hybrid QW/QD structure. This additional emission broadening method contributes significantly to obtaining a 3-dB linewidth of 290 nm centered at 1200 nm, with 2.4 mW at room temperature

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer : development of a prognostic model through a crowdsourced challenge with open clinical trial data

Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0.791; Bayes factor >5) and surpassed the reference model (iAUC 0.743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3.32, 95% CI 2.39-4.62, p Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Peer reviewe

Direct quality prediction in resistance spot welding process: Sensitivity, specificity and predictive accuracy comparative analysis

In this work, several of the most popular and state-of-the-art classification methods are compared as pattern recognition tools for classification of resistance spot welding joints. Instead of using the result of a non-destructive testing technique as input variables, classifiers are trained directly with the relevant welding parameters, i.e. welding current, welding time and the type of electrode (electrode material and treatment). The algorithms are compared in terms of accuracy and area under the receiver operating characteristic (ROC) curve metrics, using nested cross-validation. Results show that although there is not a dominant classifier for every specificity/sensitivity requirement, support vector machines using radial kernel, boosting and random forest techniques obtain the best performance overallSpanish MICINN Project CSD2010-00034 (SimulPast CONSOLIDER-INGENIO 2010) and by the Junta de Castilla y León GREX251-200

Electrodeposition and characterization of Fe–Mo alloys as cathodes for hydrogen evolution in the process of chlorate

Fe–Mo alloys were electrodeposited from a pyrophosphate bath using a single diode rectified AC current. Their composition and morphology were investigated by SEM, optical microscopy and EDS, in order to determine the influence of the deposition conditions on the morphology and composition of these alloys. It was shown that the electrodeposition parameters, such as: chemical bath composition and current density, influenced both the composition of the Fe–Mo alloys and the current efficiency for their deposition, while the micro and macro-morphology did not change significantly with changing conditions of alloy electrodeposition. It was found that the electrodeposited Fe–Mo alloys possessed a 0.15 V to 0.30 V lower overvoltage than mild steel for hydrogen evolution in an electrolyte commonly used in commercial chlorate production, depending on the alloy composition, i.e., the conditions of alloy electrodeposition