CiNCT: Compression and retrieval for massive vehicular trajectories via relative movement labeling

In this paper, we present a compressed data structure for moving object trajectories in a road network, which are represented as sequences of road edges. Unlike existing compression methods for trajectories in a network, our method supports pattern matching and decompression from an arbitrary position while retaining a high compressibility with theoretical guarantees. Specifically, our method is based on FM-index, a fast and compact data structure for pattern matching. To enhance the compression, we incorporate the sparsity of road networks into the data structure. In particular, we present the novel concepts of relative movement labeling and PseudoRank, each contributing to significant reductions in data size and query processing time. Our theoretical analysis and experimental studies reveal the advantages of our proposed method as compared to existing trajectory compression methods and FM-index variants

Scalable Change Analysis and Representation Using Characteristic Function

In this paper, we propose a novel framework to help human operators—who are domain experts but not necessarily familiar with statistics— analyze a complex system and find unknown changes and causes. Despite the prevalence, researchers have rarely tackled this problem. Our framework focuses on the representation and explanation of changes occurring between two datasets, specifically the normal data and data with the observed changes. We employ two-dimensional scatter plots which can provide comprehensive representation without requiring statistical knowledge. This helps a human operator to intuitively understand the change and the cause. An analysis to find two-attribute pairs whose scatter plots well explain the change does not require high computational complexity owing to the novel characteristic function-based approach. Although a hyper-parameter needs to be determined, our analysis introduces a novel appropriate prior distribution to determine the proper hyper-parameter automatically. The experimental results show that our method presents the change and the cause with the same accuracy as that of the state-of-the-art kernel hypothesis testing approaches, while reducing the computational costs by almost 99% at the maximum for all popular benchmark datasets. The experiment using real vehicle driving data demonstrates the practicality of our framework

Nanomechanical system meets ultra-small, robust, and ultra-low-powered digital communication receiver

Nanomechanical systems offer a versatile platform for both fundamental science and industrial applications. Resonating vibration has been demonstrated to enable an ultrasensitive detection of various physical quantities, with emerging applications including signal processing, biological detection and fundamental tests of quantum mechanics. It has also been shown that the mechanical vibration of a nanoscale cantilever can be used to detect electromagnetic analogue-modulated waves. However, signals obtained with nanoscale receivers are so weak that the data transfer often fails; to the best of our knowledge, no successful practical demonstration has yet been reported. Here, we present the first experimental demonstration of the use of nanomechanical systems for digital data transfer with a digital image. Furthermore, our fabrication method achieved a tiny gap around field emitter of vibrational nano-antenna, which enables the receiver to work with quite a low power consumption, on the order of 10nW

A multi-decade record of high quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT)

The Surface Ocean CO2 Atlas (SOCAT) is a synthesis of quality-controlled fCO2 (fugacity of carbon dioxide) values for the global surface oceans and coastal seas with regular updates. Version 3 of SOCAT has 14.7 million fCO2 values from 3646 data sets covering the years 1957 to 2014. This latest version has an additional 4.6 million fCO2 values relative to version 2 and extends the record from 2011 to 2014. Version 3 also significantly increases the data availability for 2005 to 2013. SOCAT has an average of approximately 1.2 million surface water fCO2 values per year for the years 2006 to 2012. Quality and documentation of the data has improved. A new feature is the data set quality control (QC) flag of E for data from alternative sensors and platforms. The accuracy of surface water fCO2 has been defined for all data set QC flags. Automated range checking has been carried out for all data sets during their upload into SOCAT. The upgrade of the interactive Data Set Viewer (previously known as the Cruise Data Viewer) allows better interrogation of the SOCAT data collection and rapid creation of high-quality figures for scientific presentations. Automated data upload has been launched for version 4 and will enable more frequent SOCAT releases in the future. High-profile scientific applications of SOCAT include quantification of the ocean sink for atmospheric carbon dioxide and its long-term variation, detection of ocean acidification, as well as evaluation of coupled-climate and ocean-only biogeochemical models. Users of SOCAT data products are urged to acknowledge the contribution of data providers, as stated in the SOCAT Fair Data Use Statement. This ESSD (Earth System Science Data) “living data” publication documents the methods and data sets used for the assembly of this new version of the SOCAT data collection and compares these with those used for earlier versions of the data collection (Pfeil et al., 2013; Sabine et al., 2013; Bakker et al., 2014). Individual data set files, included in the synthesis product, can be downloaded here: doi:10.1594/PANGAEA.849770. The gridded products are available here: doi:10.3334/CDIAC/OTG.SOCAT_V3_GRID