Pushing the Limits of Automated Glycan Assembly: Synthesis of a 50mer Polymannoside

Automated glycan assembly (AGA) enables rapid access to oligosaccharides. The overall length of polymers created via automated solid phase synthesis depends on very high yields at every step to obtain full length product. The synthesis of long polymers serves as the ultimate test of the efficiency and reliability of synthetic processes. A series of Man-(1[rightward arrow]6)-[small alpha]-Man linked oligosaccharides up to a 50mer, the longest synthetic sequence yet assembled from monosaccharides, has been realized via a 102 step synthesis. We identified a suitable mannose building block and applied a capping step in the final five AGA cycles to minimize (n-1) deletion sequences that are otherwise difficult to remove by HPLC

Localization Recall Precision (LRP): A New Performance Metric for Object Detection

Average precision (AP), the area under the recall-precision (RP) curve, is the standard performance measure for object detection. Despite its wide acceptance, it has a number of shortcomings, the most important of which are (i) the inability to distinguish very different RP curves, and (ii) the lack of directly measuring bounding box localization accuracy. In this paper, we propose 'Localization Recall Precision (LRP) Error', a new metric which we specifically designed for object detection. LRP Error is composed of three components related to localization, false negative (FN) rate and false positive (FP) rate. Based on LRP, we introduce the 'Optimal LRP', the minimum achievable LRP error representing the best achievable configuration of the detector in terms of recall-precision and the tightness of the boxes. In contrast to AP, which considers precisions over the entire recall domain, Optimal LRP determines the 'best' confidence score threshold for a class, which balances the trade-off between localization and recall-precision. In our experiments, we show that, for state-of-the-art object (SOTA) detectors, Optimal LRP provides richer and more discriminative information than AP. We also demonstrate that the best confidence score thresholds vary significantly among classes and detectors. Moreover, we present LRP results of a simple online video object detector which uses a SOTA still image object detector and show that the class-specific optimized thresholds increase the accuracy against the common approach of using a general threshold for all classes. At https://github.com/cancam/LRP we provide the source code that can compute LRP for the PASCAL VOC and MSCOCO datasets. Our source code can easily be adapted to other datasets as well.Comment: to appear in ECCV 201

Deployment of RDFa, Microdata, and Microformats on the Web – A Quantitative Analysis

More and more websites embed structured data describing for instance products, reviews, blog posts, people, organizations, events, and cooking recipes into their HTML pages using markup standards such as Microformats, Microdata and RDFa. This development has accelerated in the last two years as major Web companies, such as Google, Facebook, Yahoo!, and Microsoft, have started to use the embedded data within their applications. In this paper, we analyze the adoption of RDFa, Microdata, and Microformats across the Web. Our study is based on a large public Web crawl dating from early 2012 and consisting of 3 billion HTML pages which originate from over 40 million websites. The analysis reveals the deployment of the different markup standards, the main topical areas of the published data as well as the different vocabularies that are used within each topical area to represent data. What distinguishes our work from earlier studies, published by the large Web companies, is that the analyzed crawl as well as the extracted data are publicly available. This allows our findings to be verified and to be used as starting points for further domain-specific investigations as well as for focused information extraction endeavors

Opportunities and challenges for galvanized steel sheets in Europe

There is increasing social and political focus on a global solution to reduce CO2 vehicle emissions andto conserve diminishing raw material resources. This is leading to ever stricter environmental regulations andconsequently new challenges for steel users in Europe. These challenges must be supported by the steelproducers, who can also look to find new opportunities through innovative solutions to increase thecompetitiveness of steel against other materials such as aluminum.This paper presents answers for many of the challenges being faced by the steel industry.These are demonstrated with reference to the continual development of the galvanizing process, thecoating of ultra high strength steels, new coatings for hot forming products and highly corrosion resistant zincmagnesium based alloy coatings

Balanced and Restored Cross-Sections Representing Post-Miocene Crustal Extension of Fluvial Deposits, North-Central Montana to Southeast Idaho

This research is part of a larger project based on the theory of the existence of a pre-ice age, Amazon-scale river that had headwaters in the southern Colorado Plateau and flowed north through the western United States and Canada before discharging into the Labrador Sea. Stream-rounded fluvial deposits in Montana and Idaho provide evidence of sediment provenance in Nevada and Utah, as there are no confirmed bedrock sources for these sediments in Montana or Idaho. The Miocene river bed has been offset and tilted by dozens of extensional faults in the region. Some faults bound large mountain ranges including the Lost River, Lemhi, Beaverhead, Tendoy, Blacktail Deer, Ruby, Madison, and Big Belt Mountains. The reconstructed trend of the Miocene river bed provides a reference line against which to measure active faulting. We constructed five balanced cross-sections of the deformed subsurface along the Miocene river bed from north-central Montana to southeast Idaho across the faulted mountain ranges and restored the cross-sections to represent an un-deformed subsurface. This provided valuable insight into crustal deformation in these regions. Knowing the timing and extent of crustal deformation has many scientific and societal benefits. Western Montana and adjacent Idaho occupy the Inter-mountain Seismic Zone and have the potential for large earthquakes. Detailed cross-sections through this zone can provide information for development projects in faulted areas, and target potential aquifer locations where the thick river gravel has been down-faulted into the sub-surface. This research will be an important contribution to understanding the evolution of the tectonic landscape of Montana and Idaho

Localization recall precision (LRP): A new performance metric for object detection

Average precision (AP), the area under the recall-precision (RP) curve, is the standard performance measure for object detection. Despite its wide acceptance, it has a number of shortcomings, the most important of which are (i) the inability to distinguish very different RP curves, and (ii) the lack of directly measuring bounding box localization accuracy. In this paper, we propose “Localization Recall Precision (LRP) Error”, a new metric specifically designed for object detection. LRP Error is composed of three components related to localization, false negative (FN) rate and false positive (FP) rate. Based on LRP, we introduce the “Optimal LRP” (oLRP), the minimum achievable LRP error representing the best achievable configuration of the detector in terms of recall-precision and the tightness of the boxes. In contrast to AP, which considers precisions over the entire recall domain, oLRP determines the “best” confidence score threshold for a class, which balances the trade-off between localization and recall-precision. In our experiments, we show that oLRP provides richer and more discriminative information than AP. We also demonstrate that the best confidence score thresholds vary significantly among classes and detectors. Moreover, we present LRP results of a simple online video object detector and show that the class-specific optimized thresholds increase the accuracy against the common approach of using a general threshold for all classes. Our experiments demonstrate that LRP is more competent than AP in capturing the performance of detectors. Our source code for PASCAL VOC AND MSCOCO datasets are provided at https://github.com/cancam/LRP