Improving the Scalability of DPWS-Based Networked Infrastructures

The Devices Profile for Web Services (DPWS) specification enables seamless discovery, configuration, and interoperability of networked devices in various settings, ranging from home automation and multimedia to manufacturing equipment and data centers. Unfortunately, the sheer simplicity of event notification mechanisms that makes it fit for resource-constrained devices, makes it hard to scale to large infrastructures with more stringent dependability requirements, ironically, where self-configuration would be most useful. In this report, we address this challenge with a proposal to integrate gossip-based dissemination in DPWS, thus maintaining compatibility with original assumptions of the specification, and avoiding a centralized configuration server or custom black-box middleware components. In detail, we show how our approach provides an evolutionary and non-intrusive solution to the scalability limitations of DPWS and experimentally evaluate it with an implementation based on the the Web Services for Devices (WS4D) Java Multi Edition DPWS Stack (JMEDS).Comment: 28 pages, Technical Repor

Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data

Accurately modeling traffic speeds is a fundamental part of efficient intelligent transportation systems. Nowadays, with the widespread deployment of GPS-enabled devices, it has become possible to crowdsource the collection of speed information to road users (e.g. through mobile applications or dedicated in-vehicle devices). Despite its rather wide spatial coverage, crowdsourced speed data also brings very important challenges, such as the highly variable measurement noise in the data due to a variety of driving behaviors and sample sizes. When not properly accounted for, this noise can severely compromise any application that relies on accurate traffic data. In this article, we propose the use of heteroscedastic Gaussian processes (HGP) to model the time-varying uncertainty in large-scale crowdsourced traffic data. Furthermore, we develop a HGP conditioned on sample size and traffic regime (SRC-HGP), which makes use of sample size information (probe vehicles per minute) as well as previous observed speeds, in order to more accurately model the uncertainty in observed speeds. Using 6 months of crowdsourced traffic data from Copenhagen, we empirically show that the proposed heteroscedastic models produce significantly better predictive distributions when compared to current state-of-the-art methods for both speed imputation and short-term forecasting tasks.Comment: 22 pages, Transportation Research Part C: Emerging Technologies (Elsevier

Industrial Illegitimacy and Negative Externalities: the Case of the Illinois Livestock Industry

An industry's legitimacy depends on stakeholders' perceptions and assessments of the appropriateness of its behavior across a wide array of settings. While products and services may be highly valued, and in some cases essential, business externalities serve as a powerful counterforce undermining legitimacy. The work draws on the theory of industrial legitimacy and employs a taxonomy of four different legitimacy sub components; pragmatic, regulative, normative, and cognitive. The paper identifies how externalities affect an industry's legitimacy and the relative contribution of each sub component. The research then empirically tests the theory using the case of the Illinois livestock industry.Livestock Production/Industries,

Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation

Traffic speed data imputation is a fundamental challenge for data-driven transport analysis. In recent years, with the ubiquity of GPS-enabled devices and the widespread use of crowdsourcing alternatives for the collection of traffic data, transportation professionals increasingly look to such user-generated data for many analysis, planning, and decision support applications. However, due to the mechanics of the data collection process, crowdsourced traffic data such as probe-vehicle data is highly prone to missing observations, making accurate imputation crucial for the success of any application that makes use of that type of data. In this article, we propose the use of multi-output Gaussian processes (GPs) to model the complex spatial and temporal patterns in crowdsourced traffic data. While the Bayesian nonparametric formalism of GPs allows us to model observation uncertainty, the multi-output extension based on convolution processes effectively enables us to capture complex spatial dependencies between nearby road segments. Using 6 months of crowdsourced traffic speed data or "probe vehicle data" for several locations in Copenhagen, the proposed approach is empirically shown to significantly outperform popular state-of-the-art imputation methods.Comment: 10 pages, IEEE Transactions on Intelligent Transportation Systems, 201

Epigenetic events underlying somatic cell reprogramming

Although differentiated cells normally retain cell-type-specific gene expression patterns throughout their lifetime, cell identity can sometimes be modified or reversed in vivo by transdifferentiation, or experimentally through cell fusion or by nuclear transfer. Several studies have illustrated the importance of chromatin remodelling, DNA demethylation and dominant transcriptional factor expression for changes in lineage identity. Here the epigenetic mechanisms required to “reset” genome function were investigated using experimental heterokaryons. To examine the epigenetic changes that are required for the dominant conversion of lymphocytes to muscle, I generated stable heterokaryons between human B-lymphocytes and mouse C2C12 myotubes. I show that lymphocyte nuclei adopt an architecture resembling that of muscle and initiate the expression of musclespecific genes in the same temporal order as developing muscle. The establishment of this novel gene expression program is coordinated with the shutdown of several lymphocyte-associated genes. Interestingly, inhibition of histone deacetylase (HDAC) activity during reprogramming selectively blocks the silencing of lymphocyte-specific genes but does not prevent the establishment of muscle-specific gene expression. In order to reprogram somatic cells to pluripotency, I fused human Blymphocytes and mouse embryonic stem (ES) cells. The conversion of human cells is initiated rapidly, occurring in heterokaryons before nuclear fusion. Reprogramming of human lymphocytes by mouse ES cells elicits the expression of a human ES-specific gene expression profile in which endogenous hSSEA4, hFgf receptors and ligands are expressed while factors that are characteristic of mouse ES cells, such as Bmp4 and Lif receptor are not. Using genetically engineered mouse ES cells I demonstrate that successful reprogramming requires the expression of Oct4, but importantly, does not require Sox2, a factor implicated as critical for the induction of pluripotency. Following reprogramming, mOct4 becomes dispensable for maintaining the multi-potent state of hybrid cells. Finally, I have examined the reprogramming potential of embryonic germ (EG), embryonic carcinoma (EC) and ES cells deficient for the Polycomb repressive complex 2 (PRC2) proteins Eed, Suz12 and Ezh2. While EC and EG cells share the ability to reprogram human lymphocytes with ES cells, the lack of Polycomb proteins abolishes reprogramming. Thus, the repressive chromatin mark (H3K27 methylation) catalysed by PRC2 play a crucial role in keeping ES cells with full reprogramming capacity. Collectively my results underscore the importance of chromatin events during cell fate reprogramming

Avaliação da contaminação bacteriana de produtos oftálmicos em uso em consultórios e centDa Roscirúrgicos hospitalares.

Trabalho de Conclusão de Curso - Universidade Federal de Santa Catarina, Centro de Ciências da Saúde, Departamento de Clínica Cirúrgica, Curso de Medicina, Florianópolis, 199