Productive Theory-Ladenness in fMRI

Several developments for diverse scientific goals, mostly in physics and physiology, had to take place, which eventually gave us fMRI as one of the central research paradigms of contemporary cognitive neuroscience. This technique stands on solid foundations established by the physics of magnetic resonance and the physiology of hemodynamics and is complimented by computational and statistical techniques. I argue, and support using concrete examples, that these foundations give rise to a productive theory-ladenness in fMRI, which enables researchers to identify and control for the types of methodological and inferential errors. Consequently, this makes it possible for researchers to represent and investigate cognitive phenomena in terms of hemodynamic data and for experimental knowledge to grow independently of large scale theories of cognition

Using GitHub as a Teaching Tool for Programming Courses

GitHub has become the most popular code management platform in the software development industry. It allows developers to manage their software development projects and collaborate with each other. Recently, educators also started using GitHub as a teaching tool for programming courses by hosting code samples and managing student assignments. In this study, we examine how GitHub is being used in academia, and we discuss the motivations and the benefits of using this platform. We also present authors’ experience of using GitHub in programming courses of a software engineering program. We discuss the benefits and challenges of using GitHub and GitHub classroom in the classroom.Cockrell School of Engineerin

Productive Theory-Ladenness in fMRI

Several developments for diverse scientific goals, mostly in physics and physiology, had to take place, which eventually gave us fMRI as one of the central research paradigms of contemporary cognitive neuroscience. This technique stands on solid foundations established by the physics of magnetic resonance and the physiology of hemodynamics and is complimented by computational and statistical techniques. I argue, and support using concrete examples, that these foundations give rise to a productive theory-ladenness in fMRI, which enables researchers to identify and control for the types of methodological and inferential errors that may arise in the use of fMRI. Consequently, this makes it possible for researchers to represent and investigate cognitive phenomena in terms of hemodynamic data and for experimental knowledge to grow independently of large scale theories of cognition

Productive Theory-Ladenness in fMRI

Several developments for diverse scientific goals, mostly in physics and physiology, had to take place, which eventually gave us fMRI as one of the central research paradigms of contemporary cognitive neuroscience. This technique stands on solid foundations established by the physics of magnetic resonance and the physiology of hemodynamics and is complimented by computational and statistical techniques. I argue, and support using concrete examples, that these foundations give rise to a productive theory-ladenness in fMRI, which enables researchers to identify and control for the types of methodological and inferential errors. Consequently, this makes it possible for researchers to represent and investigate cognitive phenomena in terms of hemodynamic data and for experimental knowledge to grow independently of large scale theories of cognition

Productive Theory-Ladenness in fMRI

Several developments for diverse scientific goals, mostly in physics and physiology, had to take place, which eventually gave us fMRI as one of the central research paradigms of contemporary cognitive neuroscience. This technique stands on solid foundations established by the physics of magnetic resonance and the physiology of hemodynamics and is complimented by computational and statistical techniques. I argue, and support using concrete examples, that these foundations give rise to a productive theory-ladenness in fMRI, which enables researchers to identify and control for the types of methodological and inferential errors that may arise in the use of fMRI. Consequently, this makes it possible for researchers to represent and investigate cognitive phenomena in terms of hemodynamic data and for experimental knowledge to grow independently of large scale theories of cognition

Effectiveness of three different types of educational methods on implementation of proper oral hygiene behaviour prior to orthodontic treatment

Objective: The aim of this study was to compare three teaching methods’ time and personnel requirements, and their effects on plaque and gingival indices. Methods: This study was a single-blind randomized controlled trial on fixed orthodontic appliance candidates (n = 90), assigned into a control group (n = 30) and two different study groups (n = 30 each). The control group received standard printed educational material and was assisted with verbal information. The study groups either received video-assisted or hands-on training about fixed orthodontic appliance and oral hygiene. The time requirements for all three educational interventions was recorded during the initial visit. The adequacy of oral hygiene was documented through plaque and gingival indices during the initial visit and eighth week of the treatment. The continuous variables were analyzed using 1-way ANOVA. Tukey HSD and Student t-tests were used for post-hoc comparisons (α?#8197;= 0.05). Also, a chi-square test was used for the analysis of categorical variables. Results: Standard education failed to maintain the plaque and gingival indices at the eighth week of the treatment. Although both video-assisted and hands-on training took a considerable amount of time, they served well in preserving both of the indices at the eighth week. The longer the educational intervention was, the better the preservation of the plaque and gingival indices. Conclusion: Educational intervention, either with video-assisted or hands-on programs, provided better results in oral hygiene depending on the time and personnel constraints of the orthodontist

Productive Theory-Ladenness in fMRI

Several developments for diverse scientific goals, mostly in physics and physiology, had to take place, which eventually gave us fMRI as one of the central research paradigms of contemporary cognitive neuroscience. This technique stands on solid foundations established by the physics of magnetic resonance and the physiology of hemodynamics and is complimented by computational and statistical techniques. I argue, and support using concrete examples, that these foundations give rise to a productive theory-ladenness in fMRI, which enables researchers to identify and control for the types of methodological and inferential errors. Consequently, this makes it possible for researchers to represent and investigate cognitive phenomena in terms of hemodynamic data and for experimental knowledge to grow independently of large scale theories of cognition

A Fast and Efficient Incremental Approach toward Dynamic Community Detection

Community detection is a discovery tool used by network scientists to analyze the structure of real-world networks. It seeks to identify natural divisions that may exist in the input networks that partition the vertices into coherent modules (or communities). While this problem space is rich with efficient algorithms and software, most of this literature caters to the static use-case where the underlying network does not change. However, many emerging real-world use-cases give rise to a need to incorporate dynamic graphs as inputs. In this paper, we present a fast and efficient incremental approach toward dynamic community detection. The key contribution is a generic technique called $\Delta-screening$, which examines the most recent batch of changes made to an input graph and selects a subset of vertices to reevaluate for potential community (re)assignment. This technique can be incorporated into any of the community detection methods that use modularity as its objective function for clustering. For demonstration purposes, we incorporated the technique into two well-known community detection tools. Our experiments demonstrate that our new incremental approach is able to generate performance speedups without compromising on the output quality (despite its heuristic nature). For instance, on a real-world network with 63M temporal edges (over 12 time steps), our approach was able to complete in 1056 seconds, yielding a 3x speedup over a baseline implementation. In addition to demonstrating the performance benefits, we also show how to use our approach to delineate appropriate intervals of temporal resolutions at which to analyze an input network

Detection of Expenditure Trends in the Telecommunication Sector

In the telecommunication sector, particularly in the cellular phone service area, customer expenditures have been in the areas of voice, short messages, and internet usage, leading to a pattern of more or less regular monthly bills. Recently, telecommunication companies started to associate retail stores to their billed commercial activities, resulting in unusual variations in the monthly payment sequences of their customers. In the present work we propose a method for detecting retail expenditure in monthly bills. We then code the information of the discretized version into a binary hierarchical tree and we classify them as positive or negative with respect to complaint potential