Economic Small-World Behavior in Weighted Networks

The small-world phenomenon has been already the subject of a huge variety of papers, showing its appeareance in a variety of systems. However, some big holes still remain to be filled, as the commonly adopted mathematical formulation suffers from a variety of limitations, that make it unsuitable to provide a general tool of analysis for real networks, and not just for mathematical (topological) abstractions. In this paper we show where the major problems arise, and how there is therefore the need for a new reformulation of the small-world concept. Together with an analysis of the variables involved, we then propose a new theory of small-world networks based on two leading concepts: efficiency and cost. Efficiency measures how well information propagates over the network, and cost measures how expensive it is to build a network. The combination of these factors leads us to introduce the concept of {\em economic small worlds}, that formalizes the idea of networks that are "cheap" to build, and nevertheless efficient in propagating information, both at global and local scale. This new concept is shown to overcome all the limitations proper of the so-far commonly adopted formulation, and to provide an adequate tool to quantitatively analyze the behaviour of complex networks in the real world. Various complex systems are analyzed, ranging from the realm of neural networks, to social sciences, to communication and transportation networks. In each case, economic small worlds are found. Moreover, using the economic small-world framework, the construction principles of these networks can be quantitatively analyzed and compared, giving good insights on how efficiency and economy principles combine up to shape all these systems.Comment: 17 pages, 10 figures, 4 table

Characteristics of US Adults Who Have Positive and Negative Perceptions of Doctors of Chiropractic and Chiropractic Care

AbstractObjectiveThe purpose of this study was to compare characteristics, likelihood to use, and actual use of chiropractic care for US survey respondents with positive and negative perceptions of doctors of chiropractic (DCs) and chiropractic care.MethodsFrom a 2015 nationally representative survey of 5422 adults (response rate, 29%), we used respondents' answers to identify those with positive and negative perceptions of DCs or chiropractic care. We used the χ2 test to compare other survey responses for these groups.ResultsPositive perceptions of DCs were more common than those for chiropractic care, whereas negative perceptions of chiropractic care were more common than those for DCs. Respondents with negative perceptions of DCs or chiropractic care were less likely to know whether chiropractic care was covered by their insurance, more likely to want to see a medical doctor first if they were experiencing neck or back pain, less likely to indicate that they would see a DC for neck or back pain, and less likely to have ever seen a DC as a patient, particularly in the recent past. Positive perceptions of chiropractic care and negative perceptions of DCs appear to have greater influence on DC utilization rates than their converses.ConclusionWe found that US adults generally perceive DCs in a positive manner but that a relatively high proportion has negative perceptions of chiropractic care, particularly the costs and number of visits required by such care. Characteristics of respondents with positive and negative perceptions were similar, but those with positive perceptions were more likely to plan to use—and to have already received—chiropractic care

Efficient Behavior of Small-World Networks

We introduce the concept of efficiency of a network, measuring how efficiently it exchanges information. By using this simple measure small-world networks are seen as systems that are both globally and locally efficient. This allows to give a clear physical meaning to the concept of small-world, and also to perform a precise quantitative a nalysis of both weighted and unweighted networks. We study neural networks and man-made communication and transportation systems and we show that the underlying general principle of their construction is in fact a small-world principle of high efficiency.Comment: 1 figure, 2 tables. Revised version. Accepted for publication in Phys. Rev. Let

Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation

Magnetic Resonance Imaging (MRI) is widely used in routine clinical diagnosis and treatment. However, variations in MRI acquisition protocols result in different appearances of normal and diseased tissue in the images. Convolutional neural networks (CNNs), which have shown to be successful in many medical image analysis tasks, are typically sensitive to the variations in imaging protocols. Therefore, in many cases, networks trained on data acquired with one MRI protocol, do not perform satisfactorily on data acquired with different protocols. This limits the use of models trained with large annotated legacy datasets on a new dataset with a different domain which is often a recurring situation in clinical settings. In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, 1) How much data from the new domain is required for a decent adaptation of the original network?; and, 2) What portion of the pre-trained model parameters should be retrained given a certain number of the new domain training samples? To address these questions, we conducted extensive experiments in white matter hyperintensity segmentation task. We trained a CNN on legacy MR images of brain and evaluated the performance of the domain-adapted network on the same task with images from a different domain. We then compared the performance of the model to the surrogate scenarios where either the same trained network is used or a new network is trained from scratch on the new dataset.The domain-adapted network tuned only by two training examples achieved a Dice score of 0.63 substantially outperforming a similar network trained on the same set of examples from scratch.Comment: 8 pages, 3 figure

Geração de indicadores para periódicos científicos: um estudo na AtoZ

It presents the results from a proposal funded by an Institucional Notice. The funds received from the Federal University of Paraná were used to create a database that could generate some indicators for the academic online journal "AtoZ: new practices in information and knowledge". Based on a BibTeX set and the metadata extracted from the Open Journal System plataform, the results allowed an earnest data analysis that can support the decision-making process, as well as the potential use of the methodological procedures proposed for other journals

Geração de indicadores para periódicos científicos: um estudo na AtoZ

It presents the results from a proposal funded by an Institucional Notice. The funds received from the Federal University of Paraná were used to create a database that could generate some indicators for the academic online journal "AtoZ: new practices in information and knowledge". Based on a BibTeX set and the metadata extracted from the Open Journal System plataform, the results allowed an earnest data analysis that can support the decision-making process, as well as the potential use of the methodological procedures proposed for other journals

Data-Driven Structural Health Monitoring in Laminated Composite Structures: Characterisation of Impact Damage

There is a high level of uncertainty for detecting damage, such as barely visible impact damage, hidden manufacturing defects, and subsurface cracks in laminated composites, which is a main limiting factor in wider use of these materials. This highlights the necessity of developing innovative structural health monitoring (SHM) strategies to meet the safety and reliability of current composite structures. In this research, a wide range of laminated composite specimens were designed, manufactured and tested under drop-weight impact with several impact energies to generate visual evidence of such impact events. The dataset was then used to train a user developed artificial intelligence (AI)-based algorithm to identify and predict damaged areas. The results showed that the developed algorithm could well identify the impact damage on both front and back faces of the specimens. The results obtained from the new AI-based platform were in good agreement with visual observations. The research highlights the importance of a high quality dataset in training the AI-based algorithms for visual SHM