Inferring Transportation Mode and Human Activity from Mobile Sensing in Daily Life

In this paper, we focus on simultaneous inference of transportation modes and human activities in daily life via modelling and inference from multivariate time series data, which are streamed from off-the- shelf mobile sensors (e.g. embedded in smartphones) in real-world dynamic environments. The transportation mode will be inferred from the structured hierarchical contexts associated with human activities. Through our mobile context recognition system, an ac- curate and robust solution can be obtained to infer transportation mode, human activity and their associated contexts (e.g. whether the user is in moving or stationary environment) simultaneously. There are many challenges in analysing and modelling human mobility patterns within urban areas due to the ever-changing en- vironments of the mobile users. For instance, a user could stay at a particular location and then travel to various destinations depend- ing on the tasks they carry within a day. Consequently, there is a need to reduce the reliance on location-based sensors (e.g. GPS), since they consume a significant amount of energy on smart de- vices, for the purpose of intelligent mobile sensing (i.e. automatic inference of transportation mode, human activity and associated contexts). Nevertheless, our system is capable of outperforming the simplistic approach that only considers independent classifications of multiple context label sets on data streamed from low energy sensors

Metaphor Identification in Large Texts Corpora

Identifying metaphorical language-use (e.g., sweet child) is one of the challenges facing natural language processing. This paper describes three novel algorithms for automatic metaphor identification. The algorithms are variations of the same core algorithm. We evaluate the algorithms on two corpora of Reuters and the New York Times articles. The paper presents the most comprehensive study of metaphor identification in terms of scope of metaphorical phrases and annotated corpora size. Algorithms’ performance in identifying linguistic phrases as metaphorical or literal has been compared to human judgment. Overall, the algorithms outperform the state-of-the-art algorithm with 71% precision and 27% averaged improvement in prediction over the base-rate of metaphors in the corpus.United States. Intelligence Advanced Research Projects Activity (IARPA)United States. Dept. of Defense (U.S. Army Research Laboratory Contract W911NF-12-C-0021

Prototype Data Models and Data Dictionaries for Hanford Sediment Physical and Hydraulic Properties

The Remediation Decision Support (RDS) project, managed by the Pacific Northwest National Laboratory (PNNL) for the U.S. Department of Energy (DOE) and the CH2M HILL Plateau Remediation Company (CHPRC), has been compiling physical and hydraulic property data and parameters to support risk analyses and waste management decisions at Hanford. In FY09 the RDS project developed a strategic plan for a physical and hydraulic property database. This report documents prototype data models and dictionaries for these properties and associated parameters. Physical properties and hydraulic parameters and their distributions are required for any type of quantitative assessment of risk and uncertainty associated with predictions of contaminant transport and fate in the subsurface. The central plateau of the Hanford Site in southeastern Washington State contains most of the contamination at the Site and has up to {approx}100 m of unsaturated and unconsolidated or semi-consolidated sediments overlying the unconfined aquifer. These sediments contain a wide variety of contaminants ranging from organic compounds, such as carbon tetrachloride, to numerous radionuclides including technetium, plutonium, and uranium. Knowledge of the physical and hydraulic properties of the sediments and their distributions is critical for quantitative assessment of the transport of these contaminants in the subsurface, for evaluation of long-term risks and uncertainty associated with model predictions of contaminant transport and fate, and for evaluating, designing, and operating remediation alternatives. One of the goals of PNNL's RDS project is to work with the Hanford Environmental Data Manager (currently with CHPRC) to develop a protocol and schedule for incorporation of physical property and hydraulic parameter datasets currently maintained by PNNL into HEIS. This requires that the data first be reviewed to ensure quality and consistency. New data models must then be developed for HEIS that are approved by the HTAG that oversees HEIS development. After approval, these new data models then need to be implemented in HEIS by the EDM before there is an actual repository for the data. This document summarizes modifications to previously developed data models, and new data models and data dictionaries for physical and hydraulic property data and parameters to be transferred to HEIS. A prototype dataset that conforms to the specifications of these recommended data models has been identified and processed, and is ready for transfer to CHPRC for inclusion in HEIS. Additional datasets are planned for transfer from PNNL to CHPRC in FY11

Ontological co-belonging in Peter Sloterdijk's spherological philosophy of mediation

(Winner of the 2017 Paragraph annual essay prize competition, on the theme of ‘Belongings’) This article examines the ontology and politics of Peter Sloterdijk's Spheres trilogy, focusing in particular upon the notion of microspherical enclosure explicated in the first volume of this series. Noting Sloterdijk's unusual alignment of his philosophy with media theory, three main contentions are put forward. Firstly, that Sloterdijk's reconfiguration of Heidegger's fundamental ontology represents a largely unacknowledged renunciation of the primacy of Being-towards-death in the authentic existence of Dasein, foregrounding instead an originary co-belonging between mother and child. Secondly, that Sloterdijk borrows from media theory a concern regarding the facticity of all communication, grounding philosophical discourse in the determinate locality of its origin, but does so while exalting a pre-natal communicative immediacy that would seem to disparage the everydayness of Dasein. Finally, that Sloterdijk's oft-justified scepticism regarding globalization often retreats into an anti-cosmopolitanism that, in its nostalgia for the comfort, security and immediacy of the matrixial co-belonging (and the various attempts by humans to replicate this enclosure), evinces a covert but potentially noxious politics of exclusion

Carbon Tetrachloride Flow and Transport in the Subsurface of the 216-Z-18 Crib and 216-Z-1A Tile Field at the Hanford Site: Multifluid Flow Simulations and Conceptual Model Update

Carbon tetrachloride (CT) was discharged to the 216-Z-9, Z-1A, and Z-18 waste sites that are included in the 200-PW-1 Operable Unit in Hanford 200 West Area. Fluor Hanford, Inc. is conducting a Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) remedial investigation/feasibility study (RI/FS) for the 200-PW-1 Operable Unit. As part of this overall effort, Pacific Northwest National Laboratory (PNNL) was contracted to improve the conceptual model of how CT is distributed in the Hanford 200 West Area subsurface through use of numerical flow and transport modeling. This work supports the U.S. Department of Energy's (DOE's) efforts to characterize the nature and distribution of CT in the 200 West Area and subsequently select an appropriate final remedy

Updated Conceptual Model for the 300 Area Uranium Groundwater Plume

The 300 Area uranium groundwater plume in the 300-FF-5 Operable Unit is residual from past discharge of nuclear fuel fabrication wastes to a number of liquid (and solid) disposal sites. The source zones in the disposal sites were remediated by excavation and backfilled to grade, but sorbed uranium remains in deeper, unexcavated vadose zone sediments. In spite of source term removal, the groundwater plume has shown remarkable persistence, with concentrations exceeding the drinking water standard over an area of approximately 1 km2. The plume resides within a coupled vadose zone, groundwater, river zone system of immense complexity and scale. Interactions between geologic structure, the hydrologic system driven by the Columbia River, groundwater-river exchange points, and the geochemistry of uranium contribute to persistence of the plume. The U.S. Department of Energy (DOE) recently completed a Remedial Investigation/Feasibility Study (RI/FS) to document characterization of the 300 Area uranium plume and plan for beginning to implement proposed remedial actions. As part of the RI/FS document, a conceptual model was developed that integrates knowledge of the hydrogeologic and geochemical properties of the 300 Area and controlling processes to yield an understanding of how the system behaves and the variables that control it. Recent results from the Hanford Integrated Field Research Challenge site and the Subsurface Biogeochemistry Scientific Focus Area Project funded by the DOE Office of Science were used to update the conceptual model and provide an assessment of key factors controlling plume persistence

Prediction of Cognitive States During Flight Simulation Using Multimodal Psychophysiological Sensing

The Commercial Aviation Safety Team found the majority of recent international commercial aviation accidents attributable to loss of control inflight involved flight crew loss of airplane state awareness (ASA), and distraction was involved in all of them. Research on attention-related human performance limiting states (AHPLS) such as channelized attention, diverted attention, startle/surprise, and confirmation bias, has been recommended in a Safety Enhancement (SE) entitled "Training for Attention Management." To accomplish the detection of such cognitive and psychophysiological states, a broad suite of sensors was implemented to simultaneously measure their physiological markers during a high fidelity flight simulation human subject study. Twenty-four pilot participants were asked to wear the sensors while they performed benchmark tasks and motion-based flight scenarios designed to induce AHPLS. Pattern classification was employed to predict the occurrence of AHPLS during flight simulation also designed to induce those states. Classifier training data were collected during performance of the benchmark tasks. Multimodal classification was performed, using pre-processed electroencephalography, galvanic skin response, electrocardiogram, and respiration signals as input features. A combination of one, some or all modalities were used. Extreme gradient boosting, random forest and two support vector machine classifiers were implemented. The best accuracy for each modality-classifier combination is reported. Results using a select set of features and using the full set of available features are presented. Further, results are presented for training one classifier with the combined features and for training multiple classifiers with features from each modality separately. Using the select set of features and combined training, multistate prediction accuracy averaged 0.64 +/- 0.14 across thirteen participants and was significantly higher than that for the separate training case. These results support the goal of demonstrating simultaneous real-time classification of multiple states using multiple sensing modalities in high fidelity flight simulators. This detection is intended to support and inform training methods under development to mitigate the loss of ASA and thus reduce accidents and incidents