Promoting Progress with Fair Use

The Intellectual Property (IP) Clause provides that Congress has the power to promote the Progress of Science and useful Arts by securing for limited Times to Authors and Inventors the exclusive Right to their respective Writings and Discoveries. In the realm of copyright, Congress and the courts have interpreted the clause as granting Congress a power not to promote progress but to establish limited IP monopolies. To return to an understanding of the IP power better grounded in the constitutional text, Congress and the courts should ensure that any IP enactment promote[s] ... Progress by considering whether it improves the quality or quantity of knowledge and aids the dissemination of knowledge, and whether it does so better than prior IP enactments. The courts can exercise the fair-use doctrine to aid in this re-constitutionalization of IP law by applying a fifth fair-use factor. This proposed fifth factor would balance the progress-promoting value of the alleged infringer\u27s use against the progress-promoting value of enforcing the copyright holder\u27s rights. Reviewing courts should presume that any alleged infringement is fair if it promotes progress better than the enforcement of the copyright

College Students’ Knowledge of Suicide Risk Factors and Prevention Strategies

Suicide is one of the leading causes of death among college aged students (Hirsch & Barton, 2011). Several risk factors for suicidal ideation have been identified, but little work has focused on awareness of suicide prevention resources. The focus of this study is to assess a college population’s knowledge on suicide risk factors and determine whether they feel strongly about one method of prevention over another. This study is focused on assessing individual knowledge of risk factors and identification of appropriate prevention strategies. It was hypothesized that participants who are more successful at identifying risk factors will be more knowledgeable about the appropriate course of action to take to prevent a suicide attempt. The results suggest that there are no main effects for identifying suicide risk factors between: genders, year-in-school, and majors. Significance was found for relationships between suggested prevention methods and comfort of performing prevention

OPERATIONAL ANALYSIS OF TRADITIONAL ACCESS MANAGEMENT STRATEGIES & DEMAND-RESPONSIVE ACCESS CONTROL ON ARTERIALS

Arterials are typically characterized by closely-spaced signalized intersections, high driveway density, and high traffic volumes. These characteristics contribute to congestion, as well as crashes. Access management strategies can address both operational and safety issues on urban arterials. This research focuses on the operational impacts of access management with two objectives: (1) quantify the impacts of ‘traditional’ access management strategies and (2) quantify the impacts of demand-responsive access control. To satisfy Objective 1, four traditional access management strategies were tested – (i) access spacing, (ii) corner clearance, (iii) access restriction, and (iv) raised median implementation. These were analyzed in four respective alternative scenarios using microscopic simulation (VISSIM) of two existing corridors; one 5-lane and one 7-lane and measures of effectiveness (MOEs) of mainline travel times and driveway ingress and egress traffic total and stopped delay were compared. The analysis revealed that operational impacts of traditional access management techniques are site-specific. However, considering both sites, the access spacing strategy, which consolidates driveways such that they achieve the SCDOT ARMS Manual spacing requirements, performed best from the standpoint of the MOE’s observed and is most recommended for implementation. In order to test demand-responsive access control for Objective 2, simulation of the same two existing corridors used for traditional access management tests was conducted for a period including both peak and off-peak traffic conditions for three scenarios (i) existing conditions, (ii) a raised median (permanent access control), and (iii) dynamic access control, which includes restriction of driveways to right-in, right-out enforced during intervals in which traffic volumes exceed given thresholds. Simulation analysis indicated that while the raised median performed differently on each corridor, the demand-responsive strategy lowered travel times and delays. Therefore, it is the conclusion of this research that alternating access between fully-open to right-in/right-out based on prevailing traffic conditions, has the potential to improve traffic operations on a corridor, by producing lower travel times and delays during both peak and off-peak traffic conditions

Computational Tools for the Untargeted Assignment of FT-MS Metabolomics Datasets

Metabolomics is the study of metabolomes, the sets of metabolites observed in living systems. Metabolism interconverts these metabolites to provide the molecules and energy necessary for life processes. Many disease processes, including cancer, have a significant metabolic component that manifests as differences in what metabolites are present and in what quantities they are produced and utilized. Thus, using metabolomics, differences between metabolomes in disease and non-disease states can be detected and these differences improve our understanding of disease processes at the molecular level. Despite the potential benefits of metabolomics, the comprehensive investigation of metabolomes remains difficult. A popular analytical technique for metabolomics is mass spectrometry. Advances in Fourier transform mass spectrometry (FT-MS) instrumentation have yielded simultaneous improvements in mass resolution, mass accuracy, and detection sensitivity. In the metabolomics field, these advantages permit more complicated, but more informative experimental designs such as the use of multiple isotope-labeled precursors in stable isotope-resolved metabolomics (SIRM) experiments. However, despite these potential applications, several outstanding problems hamper the use of FT-MS for metabolomics studies. First, artifacts and data quality problems in FT-MS spectra can confound downstream data analyses, confuse machine learning models, and complicate the robust detection and assignment of metabolite features. Second, the assignment of observed spectral features to metabolites remains difficult. Existing targeted approaches for assignment often employ databases of known metabolites; however, metabolite databases are incomplete, thus limiting or biasing assignment results. Additionally, FT-MS provides limited structural information for observed metabolites, which complicates the determination of metabolite class (e.g. lipid, sugar, etc. ) for observed metabolite spectral features, a necessary step for many metabolomics experiments. To address these problems, a set of tools were developed. The first tool identifies artifacts with high peak density observed in many FT-MS spectra and removes them safely. Using this tool, two previously unreported types of high peak density artifact were identified in FT-MS spectra: fuzzy sites and partial ringing. Fuzzy sites were particularly problematic as they confused and reduced the accuracy of machine learning models trained on datasets containing these artifacts. Second, a tool called SMIRFE was developed to assign isotope-resolved molecular formulas to observed spectral features in an untargeted manner without a database of expected metabolites. This new untargeted method was validated on a gold-standard dataset containing both unlabeled and 15N-labeled compounds and was able to identify 18 of 18 expected spectral features. Third, a collection of machine learning models was constructed to predict if a molecular formula corresponds to one or more lipid categories. These models accurately predict the correct one of eight lipid categories on our training dataset of known lipid and non-lipid molecular formulas with precisions and accuracies over 90% for most categories. These models were used to predict lipid categories for untargeted SMIRFE-derived assignments in a non-small cell lung cancer dataset. Subsequent differential abundance analysis revealed a sub-population of non-small cell lung cancer samples with a significantly increased abundance in sterol lipids. This finding implies a possible therapeutic role of statins in the treatment and/or prevention of non-small cell lung cancer. Collectively these tools represent a pipeline for FT-MS metabolomics datasets that is compatible with isotope labeling experiments. With these tools, more robust and untargeted metabolic analyses of disease will be possible

The Role of Religious and Social Organizations in the Lives of Disadvantaged Youth

This paper examines whether participation in religious or other social organizations can help offset the negative effects of growing up in a disadvantaged environment. Using the National Survey of Families and Households, we collect measures of disadvantage as well as parental involvement with religious and other social organizations when the youth were ages 3 to 19 and we observe their outcomes 13 to 15 years later. We consider a range of definitions of disadvantage in childhood (family income and poverty measures, family characteristics including parental education, and child characteristics including parental assessments of the child) and a range of outcome measures in adulthood (including education, income, and measures of health and psychological wellbeing). Overall, we find strong evidence that youth with religiously active parents are less affected later in life by childhood disadvantage than youth whose parents did not frequently attend religious services. These buffering effects of religious organizations are most pronounced when outcomes are measured by high school graduation or non-smoking and when disadvantage is measured by family resources or maternal education, but we also find buffering effects for a number of other outcome-disadvantage pairs. We generally find much weaker buffering effects for other social organizations.

Topological Chaos in a Three-Dimensional Spherical Fluid Vortex

In chaotic deterministic systems, seemingly stochastic behavior is generated by relatively simple, though hidden, organizing rules and structures. Prominent among the tools used to characterize this complexity in 1D and 2D systems are techniques which exploit the topology of dynamically invariant structures. However, the path to extending many such topological techniques to three dimensions is filled with roadblocks that prevent their application to a wider variety of physical systems. Here, we overcome these roadblocks and successfully analyze a realistic model of 3D fluid advection, by extending the homotopic lobe dynamics (HLD) technique, previously developed for 2D area-preserving dynamics, to 3D volume-preserving dynamics. We start with numerically-generated finite-time chaotic-scattering data for particles entrained in a spherical fluid vortex, and use this data to build a symbolic representation of the dynamics. We then use this symbolic representation to explain and predict the self-similar fractal structure of the scattering data, to compute bounds on the topological entropy, a fundamental measure of mixing, and to discover two different mixing mechanisms, which stretch 2D material surfaces and 1D material curves in distinct ways.Comment: 14 pages, 11 figure

Personality Profiling: How informative are social media profiles in predicting personal information?

Personality profiling has been utilised by companies for targeted advertising, political campaigns and vaccine campaigns. However, the accuracy and versatility of such models still remains relatively unknown. Consequently, we aim to explore the extent to which peoples' online digital footprints can be used to profile their Myers-Briggs personality type. We analyse and compare the results of four models: logistic regression, naive Bayes, support vector machines (SVMs) and random forests. We discover that a SVM model achieves the best accuracy of 20.95% for predicting someones complete personality type. However, logistic regression models perform only marginally worse and are significantly faster to train and perform predictions. We discover that many labelled datasets present substantial class imbalances of personal characteristics on social media, including our own. As a result, we highlight the need for attentive consideration when reporting model performance on these datasets and compare a number of methods for fixing the class-imbalance problems. Moreover, we develop a statistical framework for assessing the importance of different sets of features in our models. We discover some features to be more informative than others in the Intuitive/Sensory (p = 0.032) and Thinking/Feeling (p = 0.019) models. While we apply these methods to Myers-Briggs personality profiling, they could be more generally used for any labelling of individuals on social media.Comment: 8 pages, 6 figures. Dataset available at https://figshare.com/articles/dataset/Self-Reported_Myers-Briggs_Personality_Types_on_Twitter/2362055