Discovering human activities from binary data in smart homes

With the rapid development in sensing technology, data mining, and machine learning fields for human health monitoring, it became possible to enable monitoring of personal motion and vital signs in a manner that minimizes the disruption of an individual’s daily routine and assist individuals with difficulties to live independently at home. A primary difficulty that researchers confront is acquiring an adequate amount of labeled data for model training and validation purposes. Therefore, activity discovery handles the problem that activity labels are not available using approaches based on sequence mining and clustering. In this paper, we introduce an unsupervised method for discovering activities from a network of motion detectors in a smart home setting. First, we present an intra-day clustering algorithm to find frequent sequential patterns within a day. As a second step, we present an inter-day clustering algorithm to find the common frequent patterns between days. Furthermore, we refine the patterns to have more compressed and defined cluster characterizations. Finally, we track the occurrences of various regular routines to monitor the functional health in an individual’s patterns and lifestyle. We evaluate our methods on two public data sets captured in real-life settings from two apartments during seven-month and three-month periods

BMP signaling components in embryonic transcriptomes of the hover fly Episyrphus balteatus (Syrphidae)

<p>Abstract</p> <p>Background</p> <p>In animals, signaling of Bone Morphogenetic Proteins (BMPs) is essential for dorsoventral (DV) patterning of the embryo, but how BMP signaling evolved with changes in embryonic DV differentiation is largely unclear. Based on the extensive knowledge of BMP signaling in <it>Drosophila melanogaster</it>, the morphological diversity of extraembryonic tissues in different fly species provides a comparative system to address this question. The closest relatives of <it>D. melanogaster </it>with clearly distinct DV differentiation are hover flies (Diptera: Syrphidae). The syrphid <it>Episyrphus balteatus </it>is a commercial bio-agent against aphids and has been established as a model organism for developmental studies and chemical ecology. The dorsal blastoderm of <it>E. balteatus </it>gives rise to two extraembryonic tissues (serosa and amnion), whereas in <it>D. melanogaster</it>, the dorsal blastoderm differentiates into a single extraembryonic epithelium (amnioserosa). Recent studies indicate that several BMP signaling components of <it>D. melanogaster</it>, including the BMP ligand Screw (Scw) and other extracellular regulators, evolved in the dipteran lineage through gene duplication and functional divergence. These findings raise the question of whether the complement of BMP signaling components changed with the origin of the amnioserosa.</p> <p>Results</p> <p>To search for BMP signaling components in <it>E. balteatus</it>, we generated and analyzed transcriptomes of freshly laid eggs (0-30 minutes) and late blastoderm to early germband extension stages (3-6 hours) using Roche/454 sequencing. We identified putative <it>E. balteatus </it>orthologues of 43% of all annotated <it>D. melanogaster </it>genes, including the genes of all BMP ligands and other BMP signaling components.</p> <p>Conclusion</p> <p>The diversification of several BMP signaling components in the dipteran linage of <it>D. melanogaster </it>preceded the origin of the amnioserosa.</p> <p>[Transcriptome sequence data from this study have been deposited at the NCBI Sequence Read Archive (SRP005289); individually assembled sequences have been deposited at GenBank (<ext-link ext-link-id="JN006969" ext-link-type="gen">JN006969</ext-link>-<ext-link ext-link-id="JN006986" ext-link-type="gen">JN006986</ext-link>).]</p

Air Force Institute of Technology Research Report 2010

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physic

Search for surviving companions in type Ia supernova remnants

The nature of the progenitor systems of type~Ia supernovae is still unclear. One way to distinguish between the single-degenerate scenario and double-degenerate scenario for their progenitors is to search for the surviving companions. Using a technique that couples the results from multi-dimensional hydrodynamics simulations with calculations of the structure and evolution of main-sequence- and helium-rich surviving companions, the color and magnitude of main-sequence- and helium-rich surviving companions are predicted as functions of time. The surviving companion candidates in Galactic type~Ia supernova remnants and nearby extragalactic type~Ia supernova remnants are discussed. We find that the maximum detectable distance of main-sequence surviving companions (helium-rich surviving companions) is $0.6-4$~Mpc ($0.4-16$~Mpc), if the apparent magnitude limit is 27 in the absence of extinction, suggesting that the Large and Small Magellanic Clouds and the Andromeda Galaxy are excellent environments in which to search for surviving companions. However, only five Ia~SNRs have been searched for surviving companions, showing little support for the standard channels in the singe-degenerate scenario. To better understand the progenitors of type Ia supernovae, we encourage the search for surviving companions in other nearby type Ia supernova remnants.Comment: 25 pages, 5 figures, and 2 tables. Accepted for publication in Ap

Learning Linear Temporal Properties

We present two novel algorithms for learning formulas in Linear Temporal Logic (LTL) from examples. The first learning algorithm reduces the learning task to a series of satisfiability problems in propositional Boolean logic and produces a smallest LTL formula (in terms of the number of subformulas) that is consistent with the given data. Our second learning algorithm, on the other hand, combines the SAT-based learning algorithm with classical algorithms for learning decision trees. The result is a learning algorithm that scales to real-world scenarios with hundreds of examples, but can no longer guarantee to produce minimal consistent LTL formulas. We compare both learning algorithms and demonstrate their performance on a wide range of synthetic benchmarks. Additionally, we illustrate their usefulness on the task of understanding executions of a leader election protocol

Air Force Institute of Technology Research Report 2009

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

A runtime heuristic to selectively replicate tasks for application-specific reliability targets

In this paper we propose a runtime-based selective task replication technique for task-parallel high performance computing applications. Our selective task replication technique is automatic and does not require modification/recompilation of OS, compiler or application code. Our heuristic, we call App_FIT, selects tasks to replicate such that the specified reliability target for an application is achieved. In our experimental evaluation, we show that App FIT selective replication heuristic is low-overhead and highly scalable. In addition, results indicate that complete task replication is overkill for achieving reliability targets. We show that with App FIT, we can tolerate pessimistic exascale error rates with only 53% of the tasks being replicated.This work was supported by FI-DGR 2013 scholarship and the European Community’s Seventh Framework Programme [FP7/2007-2013] under the Mont-blanc 2 Project (www.montblanc-project.eu), grant agreement no. 610402 and in part by the European Union (FEDER funds) under contract TIN2015-65316-P.Peer ReviewedPostprint (author's final draft

A Precision Photometric Comparison between SDSS-II and CSP Type Ia Supernova Data

Consistency between Carnegie Supernova Project (CSP) and SDSS-II supernova (SN) survey ugri measurements has been evaluated by comparing SDSS and CSP photometry for nine spectroscopically confirmed Type Ia supernova observed contemporaneously by both programs. The CSP data were transformed into the SDSS photometric system. Sources of systematic uncertainty have been identified, quantified, and shown to be at or below the 0.023 magnitude level in all bands. When all photometry for a given band is combined, we find average magnitude differences of equal to or less than 0.011 magnitudes in ugri, with rms scatter ranging from 0.043 to 0.077 magnitudes. The u band agreement is promising, with the caveat that only four of the nine supernovae are well-observed in u and these four exhibit an 0.038 magnitude supernova-to-supernova scatter in this filter.Comment: This paper has been accepted for publication in The Astronomical Journa

Air Force Institute of Technology Research Report 2013

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems Engineering and Management, Operational Sciences, Mathematics, Statistics and Engineering Physics