Enhanced spectral discrimination through the exploitation of interface effects in photon dose data

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134954/1/mp7731.pd

Multi-stakeholder Perspective on Responsible Artificial Intelligence and Acceptability in Education

This study investigates the acceptability of different artificial intelligence (AI) applications in education from a multi-stakeholder perspective, including students, teachers, and parents. Acknowledging the transformative potential of AI in education, it addresses concerns related to data privacy, AI agency, transparency, explainability and the ethical deployment of AI. Through a vignette methodology, participants were presented with four scenarios where AI's agency, transparency, explainability, and privacy were manipulated. After each scenario, participants completed a survey that captured their perceptions of AI's global utility, individual usefulness, justice, confidence, risk, and intention to use each scenario's AI if available. The data collection comprising a final sample of 1198 multi-stakeholder participants was distributed through a partner institution and social media campaigns and focused on individual responses to four AI use cases. A mediation analysis of the data indicated that acceptance and trust in AI varies significantly across stakeholder groups. We found that the key mediators between high and low levels of AI's agency, transparency, and explainability, as well as the intention to use the different educational AI, included perceived global utility, justice, and confidence. The study highlights that the acceptance of AI in education is a nuanced and multifaceted issue that requires careful consideration of specific AI applications and their characteristics, in addition to the diverse stakeholders' perceptions.Comment: 28 pages, 2 appendices, 3 figures, 5 tables, original researc

Probabilistic Daily ILI Syndromic Surveillance with a Spatio-Temporal Bayesian Hierarchical Model

BACKGROUND: For daily syndromic surveillance to be effective, an efficient and sensible algorithm would be expected to detect aberrations in influenza illness, and alert public health workers prior to any impending epidemic. This detection or alert surely contains uncertainty, and thus should be evaluated with a proper probabilistic measure. However, traditional monitoring mechanisms simply provide a binary alert, failing to adequately address this uncertainty. METHODS AND FINDINGS: Based on the Bayesian posterior probability of influenza-like illness (ILI) visits, the intensity of outbreak can be directly assessed. The numbers of daily emergency room ILI visits at five community hospitals in Taipei City during 2006-2007 were collected and fitted with a Bayesian hierarchical model containing meteorological factors such as temperature and vapor pressure, spatial interaction with conditional autoregressive structure, weekend and holiday effects, seasonality factors, and previous ILI visits. The proposed algorithm recommends an alert for action if the posterior probability is larger than 70%. External data from January to February of 2008 were retained for validation. The decision rule detects successfully the peak in the validation period. When comparing the posterior probability evaluation with the modified Cusum method, results show that the proposed method is able to detect the signals 1-2 days prior to the rise of ILI visits. CONCLUSIONS: This Bayesian hierarchical model not only constitutes a dynamic surveillance system but also constructs a stochastic evaluation of the need to call for alert. The monitoring mechanism provides earlier detection as well as a complementary tool for current surveillance programs

Return of the Great Spaghetti Monster : Learnings from a Twelve-Year Adventure in Web Software Development

The widespread adoption of the World Wide Web has fundamentally changed the landscape of software development. Only ten years ago, very few developers would write software for the Web, let alone consider using JavaScript or other web technologies for writing any serious software applications. In this paper, we reflect upon a twelve-year adventure in web development that began with the development of the Lively Kernel system at Sun Microsystems Labs in 2006. Back then, we also published some papers that identified important challenges in web-based software development based on established software engineering principles. We will revisit our earlier findings and compare the state of the art in web development today to our earlier learnings, followed by some reflections and suggestions for the road forward.Peer reviewe

Consciousness and complexity during unresponsiveness induced by propofol, xenon, and ketamine

A common endpoint of general anesthetics is behavioral unresponsiveness [1], which is commonly associated with loss of consciousness. However, subjects can become disconnected from the environment while still having conscious experiences, as demonstrated by sleep states associated with dreaming [2]. Among anesthetics, ketamine is remarkable [3] in that it induces profound unresponsiveness, but subjects often report "ketamine dreams" upon emergence from anesthesia [4-9]. Here, we aimed at assessing consciousness during anesthesia with propofol, xenon, and ketamine, independent of behavioral responsiveness. To do so, in 18 healthy volunteers, we measured the complexity of the cortical response to transcranial magnetic stimulation (TMS)-an approach that has proven helpful in assessing objectively the level of consciousness irrespective of sensory processing and motor responses [10]. In addition, upon emergence from anesthesia, we collected reports about conscious experiences during unresponsiveness. Both frontal and parietal TMS elicited a low-amplitude electroencephalographic (EEG) slow wave corresponding to a local pattern of cortical activation with low complexity during propofol anesthesia, a high-amplitude EEG slow wave corresponding to a global, stereotypical pattern of cortical activation with low complexity during xenon anesthesia, and a wakefulness-like, complex spatiotemporal activation pattern during ketamine anesthesia. Crucially, participants reported no conscious experience after emergence from propofol and xenon anesthesia, whereas after ketamine they reported long, vivid dreams unrelated to the external environment. These results are relevant because they suggest that brain complexity may be sensitive to the presence of disconnected consciousness in subjects who are considered unconscious based on behavioral responses. Sarasso, Boly, etal. show that the complexity of the cortical response to TMS is low during propofol and xenon anesthesia but high during ketamine. Crucially, no reports are obtained upon awakening from both propofol and xenon while after ketamine, all subjects report long, vivid dreams, possibly indicating a state of disconnected consciousness

Brain data:Scanning, scraping and sculpting the plastic learning brain through neurotechnology

Neurotechnology is an advancing field of research and development with significant implications for education. As 'postdigital' hybrids of biological and informational codes, novel neurotechnologies combine neuroscience insights into the human brain with advanced technical development in brain imaging, brain-computer interfaces, neurofeedback platforms, brain stimulation and other neuroenhancement applications. Merging neurobiological knowledge about human life with computational technologies, neurotechnology exemplifies how postdigital science will play a significant role in societies and education in decades to come. As neurotechnology developments are being extended to education, they present potential for businesses and governments to enact new techniques of 'neurogovernance' by 'scanning' the brain, 'scraping' it for data and then 'sculpting' the brain toward particular capacities. The aim of this article is to critically review neurotechnology developments and implications for education. It examines the purposes to which neurotechnology development is being put in education, interrogating the commercial and governmental objectives associated with it and the neuroscientific concepts and expertise that underpin it. Finally, the article raises significant ethical and governance issues related to neurotechnology development and postdigital science that require concerted attention from education researchers

Meditation-induced near-death experiences: a 3-year longitudinal study

Near-death experiences (NDEs) are life transformational events that are increasingly being subjected to empirical research. However, to date, no study has investigated the phenomenon of a meditation-induced near-death experience (MI-NDE) that is referred to in ancient Buddhist texts. Given that some advanced Buddhist meditators can induce NDEs at a pre-planned point in time, the MI-NDE may make NDEs more empirically accessible and thus advance understanding into the psychology of death-related processes. The present study recruited 12 advanced Buddhist meditators and compared the MI-NDE against two other meditation practices (i.e. that acted as control conditions) in the same participant group. Changes in the content and profundity of the MI-NDE were assessed longitudinally over a 3-year period. Findings demonstrated that compared to the control conditions, the MI-NDE prompted significantly greater pre-post increases in NDE profundity, mystical experiences and non-attachment. Furthermore, participants demonstrated significant increases in NDE profundity across the 3-year study period. Findings from an embedded qualitative analysis (using grounded theory) demonstrated that participants (i) were consciously aware of experiencing NDEs, (ii) retained volitional control over the content and duration of NDEs and (iii) elicited a rich array of non-worldly encounters and spiritual experiences. In addition to providing corroborating evidence in terms of the content of a “regular” (i.e. non-meditation-induced) NDE, novel NDE features identified in the present study indicate that there exist unexplored and/or poorly understood dimensions to NDEs. Furthermore, the study indicates that it would be feasible - including ethically feasible - for future research to recruit advanced meditators in order to assess real-time changes in neurological activity during NDEs

Geodemographics profiling of influenza A and B virus infections in community neighborhoods in Japan

<p>Abstract</p> <p>Background</p> <p>The spread of influenza viruses in a community are influenced by several factors, but no reports have focused on the relationship between the incidence of influenza and characteristics of small neighborhoods in a community. We aimed to clarify the relationship between the incidence of influenza and neighborhood characteristics using GIS and identified the type of small areas where influenza occurs frequently or infrequently.</p> <p>Methods</p> <p>Of the 19,077 registered influenza cases, we analyzed 11,437 influenza A and 5,193 influenza B cases that were diagnosed by the rapid antigen test in 66-86 medical facilities in Isahaya City, Japan, from 2004 to 2008. We used the commercial geodemographics dataset, Mosaic Japan to categorize and classify each neighborhood. Furthermore, we calculated the index value of influenza in crude and age adjusted rates to evaluate the incidence of influenza by Mosaic segmentation. Additional age structure analysis was performed to geodemographics segmentation to explore the relationship between influenza and family structure.</p> <p>Results</p> <p>The observed number of influenza A and B patients in the neighborhoods where young couples with small children lived was approximately 10-40% higher than the expected number (p < 0.01) during all seasons. On the contrary, the number of patients in the neighborhoods of the aging society in a rural area was 20-50% lower than the expected number (p < 0.01) during all seasons. This tendency was consistent after age adjustment except in the case of influenza B, which lost significance in higher incidence areas, but the overall results indicated high transmission of influenza in areas where young families with children lived.</p> <p>Conclusions</p> <p>Our analysis indicated that the incidence of influenza A and B in neighborhood groups is related to the family structure, especially the presence of children in households. Simple statistical analysis of geodemographics data is an effective method to understand the differences in the incidence of influenza among neighborhood groups, and it provides a valuable basis for community strategies to control influenza.</p