Sentic Computing for social media marketing

In a world in which millions of people express their opinions about commercial products in blogs, wikis, fora, chats and social networks, the distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product, brand or organization. Opinion mining for product positioning, in fact, is getting a more and more popular research field but the extraction of useful information from social media is not a simple task. In this work we merge AI and Semantic Web techniques to extract, encode and represent this unstructured information. In particular, we use Sentic Computing, a multi-disciplinary approach to opinion mining and sentiment analysis, to semantically and affectively analyze text and encode results in a semantic aware format according to different web ontologies. Eventually we represent this information as an interconnected knowledge base which is browsable through a multi-faceted classification website.     

Digital Intuition: Applying Common Sense Using Dimensionality Reduction

Understanding the world we live in requires access to a large amount of background knowledge: the commonsense knowledge that most people have and most computer systems don't. Many of the limitations of artificial intelligence today relate to the problem of acquiring and understanding common sense. The Open Mind Common Sense project began to collect common sense from volunteers on the Internet starting in 2000. The collected information is converted to a semantic network called ConceptNet. Reducing the dimensionality of ConceptNet's graph structure gives a matrix representation called AnalogySpace, which reveals large-scale patterns in the data, smoothes over noise, and predicts new knowledge. Extending this work, we have created a method that uses singular value decomposition to aid in the integration of systems or representations. This technique, called blending, can be harnessed to find and exploit correlations between different resources, enabling commonsense reasoning over a broader domain.     

The convergence of diagonally implicit Runge–Kutta methods combined with Richardson extrapolation

Runge–Kutta methods are widely used in the solution of systems of ordinary differential equations. Richardson extrapolation is an efficient tool for enhancing the accuracy of time integration schemes. In this paper we investigate the convergence of the combination of any of the diagonally implicit (including also the explicit) Runge–Kutta methods with active Richardson extrapolation and show that the numerical solution obtained converges under rather natural conditions.     

Stochastic View Registration of Overlapping Cameras Based on Arbitrary Motion

A new motion-based method is presented for automatic registration of images in multicamera systems, to permit synthesis of wide-baseline composite views. Unlike existing static-image and motion-based methods, our approach does not need any a priori information about the scene, the appearance of objects in the scene, or their motion. We introduce an entropy-based preselection of motion histories and an iterative Bayesian assignment of corresponding image areas. Finally, correlated point-histories and data-set optimization lead to the matching of the different views. The method is validated by demonstrating its successful use on several real-life indoor and outdoor stereo video image-sequence pairs     

Current and New Challenges in the Management of Pancreatic Neuroendocrine Tumors: The Role of miRNA-Based Approaches as New Reliable Biomarkers

Pancreatic neuroendocrine tumors (PanNETs) are rare tumors; however, their incidence greatly increases with age, and they occur more frequently among the elderly. They represent 5% of all pancreatic tumors, and despite the fact that low-grade tumors often have an indolent evolution, they portend a poor prognosis in an advanced stages and undifferentiated tumors. Additionally, functional pancreatic neuroendocrine tumors greatly impact quality of life due to the various clinical syndromes that result from abnormal hormonal secretion. With limited therapeutic and diagnostic options, patient stratification and selection of optimal therapeutic strategies should be the main focus. Modest improvements in the management of pancreatic neuroendocrine tumors have been achieved in the last years. Therefore, it is imperative to find new biomarkers and therapeutic strategies to improve patient survival and quality of life, limiting the disease burden. MicroRNAs (miRNAs) are small endogenous molecules that modulate the expression of thousands of genes and control numerous critical processes involved in tumor development and progression. New data also suggest the implication of miRNAs in treatment resistance and their potential as prognostic or diagnostic biomarkers and therapeutic targets. In this review, we discusses the current and new challenges in the management of PanNETs, including genetic and epigenetic approaches. Furthermore, we summarize the available data on miRNAs as potential prognostic, predictive, or diagnostic biomarkers and discuss their function as future therapeutic targets.