Tracking multiple objects is important in many application domains. We propose a novel algorithm for multi-object tracking that is capable of working under very challenging conditions such as minimal hardware equipment, uncalibrated monocular camera, occlusions and severe background clutter. To address this problem we propose a new method that jointly estimates object tracks, estimates correspo...

Due to technical limitations, it is impossible to have high resolution in both spatial and temporal dimensions for current NDVI datasets. Therefore, several methods are developed to produce high resolution (spatial and temporal) NDVI time-series datasets, which face some limitations including high computation loads and unreasonable assumptions. In this study, an unmixing-based method, NDVI Line...

The SNP-SNP interactome has rarely been explored in the context of neuroimaging genetics (or quantitative genetics, in general) mainly due to the complexity of conducting ~10 pairwise statistical tests. However, recent advances in machine learning, specifically the iterative sure independence screening (SIS) method, have enabled the analysis of datasets where the number of predictors is much la...

In recent years, several pathway analyses of genome-wide association studies reported the involvement of metabolic and immune pathways in Alzheimer's disease (AD). Until now, the exact mechanisms of these pathways in AD are still unclear. Here, we conducted a pathway analysis of a whole genome AD case-control expression dataset (n=41, 25 AD cases and 16 controls) from the human temporal cortex ...

Fruiting phenologies of two species of palms,Astrocaryum standleyanum L. H. Bailey and Attalea butyracea (Mutis ex L. f) Wess. Boer, isolated on eight small (1.7–3.7 ha) forested islands in the Panama Canal were studied over a 33-month period. Individual palms were permanently marked with numbered aluminum tags and censused each month for the presence of ripe fruits. The dataset consisted of 11...

Web APIs enjoy significant increase in popularity and usage in the last decade. They have became the core technology for exposing functionalities and data. Nevertheless, due to the lack of semantic Web API descriptions their discovery, sharing, integration, and assessment of their quality and consumption is limited. In this paper, we present the Linked Web APIs dataset, an RDF dataset with sema...

Understanding the magnitude and distribution of mixes near-ground carbon dioxide (CO2) components spatially (related to surface characteristics) temporally (over seasonal timescales) is critical evaluating present future climate impacts. Thus, application in situ measurement approaches, combined with spatial interpolation methods, will help explore variations source contribution total CO2 mixin...

Data mining is the process of exploring and analyzing data from different perspective, using automatic or semiautomatic techniques to extract knowledge or useful information and discover correlations or meaningful patterns and rules from large databases. One of the most vital characteristic missed by the traditional data mining systems is their capability to record and process time-varying aspe...

Data Mining is most commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binaryvalued transactions, however the data in real-world applications usually consists of quantitative values. In the last few years, many researchers have proposed Evolutionary Algorithms for mining interesting association rules from quantitative data. In this pape...

Users’ time-related information can may be multi-faceted, leading to temporal intent ambiguity. Here, we present an overview of our submissions to the Temporalia-2’s Temporal Intent Disambiguation subtask. Our approach focused on the question of whether temporal signals, extracted from publicly available, external data sources (in this case the Wikipedia page view stream), as features in a mach...