Visual Analysis of Uncertainty in Trajectories

Mining trajectory datasets has many important applications. Real trajectory data often involve uncertainty due to inadequate sampling rates and measurement errors. For some trajectories, their precise positions cannot be recovered and the exact routes that vehicles traveled cannot be accurately reconstructed. In this paper, we investigate the uncertainty problem in trajectory data and present a visual analytics system to reveal, analyze, and solve the uncertainties associated with trajectory samples. We first propose two novel visual encoding schemes called the road map analyzer and the uncertainty lens for discovering road map errors and visually analyzing the uncertainty in trajectory data respectively. Then, we conduct three case studies to discover the map errors, to address the ambiguity problem in map-matching, and to reconstruct the trajectories with historical data. These case studies demonstrate the capability and effectiveness of our system. ? 2014 Springer International Publishing.EI

Selective gene silencing by viral delivery of short hairpin RNA

RNA interference (RNAi) technology has not only become a powerful tool for functional genomics, but also allows rapid drug target discovery and in vitro validation of these targets in cell culture. Furthermore, RNAi represents a promising novel therapeutic option for treating human diseases, in particular cancer. Selective gene silencing by RNAi can be achieved essentially by two nucleic acid based methods: i) cytoplasmic delivery of short double-stranded (ds) interfering RNA oligonucleotides (siRNA), where the gene silencing effect is only transient in nature, and possibly not suitable for all applications; or ii) nuclear delivery of gene expression cassettes that express short hairpin RNA (shRNA), which are processed like endogenous interfering RNA and lead to stable gene down-regulation. Both processes involve the use of nucleic acid based drugs, which are highly charged and do not cross cell membranes by free diffusion. Therefore, in vivo delivery of RNAi therapeutics must use technology that enables the RNAi therapeutic to traverse biological membrane barriers in vivo. Viruses and the vectors derived from them carry out precisely this task and have become a major delivery system for shRNA. Here, we summarize and compare different currently used viral delivery systems, give examples of in vivo applications, and indicate trends for new developments, such as replicating viruses for shRNA delivery to cancer cells

Geoinformatica DOI 10.1007/s10707-007-0029-9 Efficient Maintenance of Continuous Queries for Trajectories

Abstract We address the problem of optimizing the maintenance of continuous queries in Moving Objects Databases, when a set of pending continuous queries need to be reevaluated as a result of bulk updates to the trajectories of moving objects. Such bulk updates may happen when traffic abnormalities, e.g., accidents or road works, affect a subset of trajectories in the corresponding regions, throughout the duration of these abnormalities. The updates to the trajectories may in turn affect the correctness of the answer sets for the pending continuous queries in much larger geographic areas. We present a comprehensive set of techniques, both static and dynamic, for improving the performance of reevaluating the continuous queries in response to the bulk updates. The static techniques correspond to specifying the values for the various semantic dimensions of trigger execution. The dynamic techniques include an in-memory shared reevaluation algorithm, extending query indexing to queries described by trajectories and query reevaluation ordering based on space-filling curves. We have completely implemented our system prototype on top of an existing Object-Relational Database Management System, Oracle 9i, and conducted extensive experimental evaluations using realistic data sets to demonstrate the validity of our approach. Keywords moving object database · continuous queries · triggers · context-aware reevaluation Research supported by the Northrop Grumman Corp., contract: P.O.8200082518

Multi-view learning with distinguishable feature fusion for rumor detection

Researchers, enterprises, and governments have made great efforts to detect misinformation promptly and accurately. Traditional solutions either examine complicated hand-crafted features or rely heavily on the constructed credibility networks to extract useful indicators for discerning false information. However, such approaches require insightful domain expert knowledge and intensive feature engineering that are often non-generalizable. Recent advances in deep learning techniques have spurred learning high-level representations from textual and image content and discovering diffusion patterns with various neural networks. Despite the progress made by these methods, they still face the problem of overdependence on the content features and fail to discriminate against the influence of each user involved in the process of rumor spreading. Different user-aspect information plays different roles in various stages of rumor diffusion, effectively extract features from each aspect, and aggregate the learned features into a unique representation, which has not been well investigated. To address these limitations, we propose a novel model, UMLARD (User-aspect Multi-view Learning with Attention for Rumor Detection), to effectively learn the representation of different views of the users who engaged in spreading the tweet, and fuse the learned features through the distinguishable fusion mechanism. Finally, we concatenate the learned user-aspect features with content features to form a unique representation and feed it into a fully connected layer to predict the label of rumors. Our experiments conducted on real-world datasets demonstrate that UMLARD significantly improves the rumor detection performance compared to state-of-the-art baselines. It also allows explainability of the model behavior and the predicted results.Algorithms and the Foundations of Software technolog

MIguración y escuela: una frontera de desafíos para el enseño de Historia en la ciudad de Medianeira

Artigo apresentado como trabalho de conclusão de curso da Especialização em História e América Latina pela Universidade Federal da Integração Latino-Americana (UNILA)O artigo analisa os processos migratórios com destino em Medianeira/PR, considerando-a, na região fronteiriça, um dos circuitos imigrantes em busca de trabalho. A pesquisa mostra como cidades do oeste do Paraná, no caso específico de Medianeira, também se inserem na região de fronteira, geralmente caracterizada só pela região tríplice fronteira de Foz do Iguaçu, Puerto Iguazu e Ciudad del Leste. Os filhos dos migrantes acessam o ensino público da cidade e conformam a escola como espaços de transnacionalidade. Os objetivos da pesquisa consistem em identificar os problemas de adaptação e preconceitos sofridos; investigar a percepção dos professores da rede estadual de ensino sobre os contextos dos estudantes migrantes; analisar os impactos do contexto de migração no espaço escolar; e propor ações que a escola possa utilizar no processo de acolhimento desses estudantes. A pesquisa tem uma abordagem teórica-metodológica embasada na pesquisa coletiva com grupos focais. Para tanto, foi realizada uma oficina com estudantes imigrantes e outra com professores da rede pública estadual de Medianeira. As oficinas articulam uma síntese entre teoria e prática, e contribuem para levantamentos dos desafios frente a presença de imigrantes no espaço escolar, bem como possíveis soluções para os desafios enfrentados.El artículo analiza los procesos migratorios con destino a Medianeira/PR, tomando en cuenta, la región fronteriza, uno de los campos de inmigración en busca de trabajo. La investigación muestra cómo las ciudades del oeste de Paraná, de modo específico de Medianeira, también se invite en la región de frontera de Foz do Iguaçu, Puerto Iguazú y Ciudad del Leste. Los hijos de los migrantes acesan al enseño público de la ciudad y conforman la escuela como espacios de transnacionalidad. Los objetivos de la investigación son identificar los problemas de adaptación y preconceptos sufridos; investigar la percepción de los maestros de la red estadual de enseño sobre los contextos de los estudiantes migrantes; analizar los impactos del contexto de migración en el espacio escolar; y proponer acciones que la escuela pueda usar en el proceso de acollida de estos estudiantes. La investigación tiene un enfoque teórico- metodológico basado y la investigación colectiva con grupos focales. Con este fin, se realizó una oficina con estudiantes inmigrantes y otra con maestros de la red pública de Medianeira. Las oficinas articulan una síntesis entre teoría y práctica, y contribuyen para levantamientos de desafíos ante la presencia de inmigrantes en el espacio escolar, bien cómo las posibles de soluciones a los desafíos enfrentado