The United Nations collects data at gigabyte scale from the member countries for sustainable development goals, and recognizes the impact big data collection has on opportunities and risks for sustainable development. The United Nations (UN) Sustainable Development Goals (SDG) open data hub provided a dataset that details the "proportion of time spent on unpaid domestic chores and care work" world wide. This primary dataset is only a portion of the data available related to the research area. This data contains many related variables, such as the population's age, sex, and location metrics. In an effort to better understand the impact of unpaid domestic work, the dataset was analyzed in conjunction with another dataset from the UN Statistics Division that details the rate of divorce/separation in the world population. Our analysis included methods such as principal component analysis, the k-means clustering algorithm, the random forest clustering algorithm, and implementing a neural network. The principal components were clustered using the k-means algorithm to cluster the variables in a manner that explains the variance present in the dataset. The analysis found that age, sex, and location demographics are key variables that explain the diverse variation between countries and the percentage of time spent on unpaid domestic work.Machine learning algorithms enabled the confirmation of this relationship. Using the key variables identified, a random forest of decision trees and a neural network were generated to classify the percentage of time spent on unpaid domestic work. Similarly, the random forest algorithm and a neural network were also implemented to classify geographical regions. These models were compared to determine the strength of the relationship between age, sex, location metrics, the percentage of time spent, and geographical regions.The analysis detailed in this work strives to identify the social factors that classify the percentage of time spent on unpaid domestic work in accordance with the UN SDG 5.4, which is to "recognize and value unpaid care and domestic work through the provision of public services, infrastructure, and social protection policies and the promotion of shared responsibility within the household and the family as nationally appropriate."

We present a fast algorithm for the joint maximum likelihood (JML) estimate of information-symbols, phase, amplitude, and noise-variance, given baud-sampled M-ary phase shift keyed signals (MPSK) observed in complex additive white Gaussian noise. The algorithm is fast in that it has a complexity that grows as O(N log2N), where N is the number of observed symbols. Further, the algorithm only requires one optional division and one square root, and no other transcendental functions. Finally, the algorithm is parallelizable with N processors in O(log2N) time using standard parallelizable processing primitives. The performances for phase, amplitude, and SNR estimation are compared to the Cramer Rao Lower Bound (CRLB) for a data-aided estimator.

Abstract Summary: We report on a major new version of the RMAP software for mapping reads from short-read sequencing technology. General improvements to accuracy and space requirements are included, along with novel functionality. Included in the RMAP software package are tools for mapping paired-end reads, mapping using more sophisticated use of quality scores, collecting ambiguous mapping locations and mapping bisulfite-treated reads. Availability: The applications described in this note are available for download at http://www.cmb.usc.edu/people/andrewds/rmap and are distributed as Open Source software under the GPLv3.0. The software has been tested on Linux and OS X platforms. Contact: [email protected]; [email protected] The RMAP algorithm was introduced by (Smith et al., 2008) as one of the earliest available programs for mapping reads from the Illumina second-generation sequencing technology. One important contribution of RMAP was to incorporate the use of quality scores directly into the mapping process: read positions with too low a quality score were not considered while mapping, and that quality score cutoff could be adjusted by the user. Subsequently, numerous mapping algorithm have appeared (Langmead et al., 2009; Li,H. et al., 2008; Li,R. et al., 2008; Lin et al., 2008; Schatz, 2009; Yanovsky et al., 2008), with improvements in both efficiency and breadth of functionality (e.g. ability to map paired-end reads; integrated SNP calling). Investigators requiring solutions to mapping problems now have many options. As new applications of short-read sequencing emerge, many variations on the analysis task of read mapping emerge. Diversity in performance characteristics of existing mapping tools becomes potentially valuable. We report the first major update to RMAP. The basic algorithmic framework in RMAP is still to preprocess reads and scan the genome, but several modifications have been made and much additional functionality has been included. Importantly, RMAP has a memory footprint that depends on the number of reads being mapped. This feature allows RMAP to be used effectively in cluster environments with commodity nodes, because partitioning the reads allows natural parallelizations with linear reduction in memory requirements per processor core used. Included in this release of the RMAP software package is functionality for mapping paired-end reads, making more sophisticated use of quality scores, collecting mapping locations for ambiguously mapping reads and mapping bisulfite-treated reads.