Artificial intelligence : A powerful paradigm for scientific research

Y Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.Peer reviewe

Free to be Accountable: Extended Self as a Moderator of Cheating Among Those Primed with Determinism

The idea that free will may be an illusion has been a source of great concern. It has led to suggestions that it may be wise to avoid public discussion of this topic lest it lead to a general moral decay. This concern has seemingly been supported by research demonstrating that individuals, when primed with the notion they lack free will, tend to cheat more and prefer less retributive punishment. The current research suggests that these effects can be moderated by the introduction of a second prime. In experiment one, participants believed they were being tested on note-taking and the subsequent recall of the content of two articles when, in fact, the dependent measure was actually the degree to which, after being primed with the articles, they cheated on a math task. It was hypothesized that the cheating effect noted in prior research would be moderated by the introduction of a second prime – one that extends the concept of self beyond our dualistic intuitions. In a second experiment, it was hypothesized that this prime would also moderate the reported reduction of preference toward retributivist punishment. In each experiment, the results trended in the direction hypothesized but in neither case were they statistically significant. The difficulties surrounding methodology and reproducibility in this type of research is discussed and suggestions for improvements in experiment design are offered

Brain Functional and Structural Networks Underpinning Musical Creativity

Musical improvisation is one of the most complex forms of creative behavior, which offers a realistic task paradigm for the investigation of real-time creativity. Despite previous studies on the topics of musical improvisation, brain activations, and creativity, the main questions about the neural mechanisms for musical improvisation in efforts to unlocking the mystery of human creativity remain unanswered. What are the brain regions that are activated during the improvised performances of music? How do these brain areas coordinate activity among themselves and others during such performances? Whether and how does the brain connectivity structure encapsulate such creative skills? In attempts to contribute to answering these questions, this dissertation examines the brain activity dynamics during musical improvisation, explores white matter fiber architecture in advanced jazz improvisers using functional and structural magnetic resonance imaging (MRI) techniques. A group of advanced jazz musicians underwent functional and structural magnetic resonance brain imaging. While the functional MRI (fMRI) of their brains were collected, these expert improvisers performed vocalization and imagery improvisation and pre-learned melody tasks. The activation and connectivity analysis of the fMRI data showed that musical improvisation is characterized by higher brain activity with less functional connectivity compared to pre-learned melody in the brain network consisting of the dorsolateral prefrontal cortex (dlPFC), supplementary motor area (SMA), lateral premotor cortex (lPMC), Cerebellum (Cb) and Broca’s Area (BCA). SMA received a dominant causal information flow from dlPFC during improvisation and prelearned melody tasks. The deterministic fiber tractography analysis also revealed that the underlying white matter structure and fiber pathways in advanced jazz improvisers were enhanced in advanced jazz improvisers compared to the control group of nonmusicians, specifically the dlPFC - SMA network. These results point to the notion that an expert\u27s performance under real-time constraints is an internally directed behavior controlled primarily by a specific brain network, that has enhanced task-supportive structural connectivity. Overall, these findings suggest that a creative act of an expert is functionally controlled by a specific cortical network as in any internally directed attention and is encapsulated by the long-timescale brain structural network changes in support of the related cognitive underpinnings

Neuroeconomics: How Neuroscience Can Inform Economics

Neuroeconomics uses knowledge about brain mechanisms to inform economic analysis, and roots economics in biology. It opens up the "black box" of the brain, much as organizational economics adds detail to the theory of the firm. Neuroscientists use many tools— including brain imaging, behavior of patients with localized brain lesions, animal behavior, and recording single neuron activity. The key insight for economics is that the brain is composed of multiple systems which interact. Controlled systems ("executive function") interrupt automatic ones. Emotions and cognition both guide decisions. Just as prices and allocations emerge from the interaction of two processes—supply and demand— individual decisions can be modeled as the result of two (or more) processes interacting. Indeed, "dual-process" models of this sort are better rooted in neuroscientific fact, and more empirically accurate, than single-process models (such as utility-maximization). We discuss how brain evidence complicates standard assumptions about basic preference, to include homeostasis and other kinds of state-dependence. We also discuss applications to intertemporal choice, risk and decision making, and game theory. Intertemporal choice appears to be domain-specific and heavily influenced by emotion. The simplified ß-d of quasi-hyperbolic discounting is supported by activation in distinct regions of limbic and cortical systems. In risky decision, imaging data tentatively support the idea that gains and losses are coded separately, and that ambiguity is distinct from risk, because it activates fear and discomfort regions. (Ironically, lesion patients who do not receive fear signals in prefrontal cortex are "rationally" neutral toward ambiguity.) Game theory studies show the effect of brain regions implicated in "theory of mind", correlates of strategic skill, and effects of hormones and other biological variables. Finally, economics can contribute to neuroscience because simple rational-choice models are useful for understanding highly-evolved behavior like motor actions that earn rewards, and Bayesian integration of sensorimotor information

Applying science of learning in education: Infusing psychological science into the curriculum

The field of specialization known as the science of learning is not, in fact, one field. Science of learning is a term that serves as an umbrella for many lines of research, theory, and application. A term with an even wider reach is Learning Sciences (Sawyer, 2006). The present book represents a sliver, albeit a substantial one, of the scholarship on the science of learning and its application in educational settings (Science of Instruction, Mayer 2011). Although much, but not all, of what is presented in this book is focused on learning in college and university settings, teachers of all academic levels may find the recommendations made by chapter authors of service. The overarching theme of this book is on the interplay between the science of learning, the science of instruction, and the science of assessment (Mayer, 2011). The science of learning is a systematic and empirical approach to understanding how people learn. More formally, Mayer (2011) defined the science of learning as the “scientific study of how people learn” (p. 3). The science of instruction (Mayer 2011), informed in part by the science of learning, is also on display throughout the book. Mayer defined the science of instruction as the “scientific study of how to help people learn” (p. 3). Finally, the assessment of student learning (e.g., learning, remembering, transferring knowledge) during and after instruction helps us determine the effectiveness of our instructional methods. Mayer defined the science of assessment as the “scientific study of how to determine what people know” (p.3). Most of the research and applications presented in this book are completed within a science of learning framework. Researchers first conducted research to understand how people learn in certain controlled contexts (i.e., in the laboratory) and then they, or others, began to consider how these understandings could be applied in educational settings. Work on the cognitive load theory of learning, which is discussed in depth in several chapters of this book (e.g., Chew; Lee and Kalyuga; Mayer; Renkl), provides an excellent example that documents how science of learning has led to valuable work on the science of instruction. Most of the work described in this book is based on theory and research in cognitive psychology. We might have selected other topics (and, thus, other authors) that have their research base in behavior analysis, computational modeling and computer science, neuroscience, etc. We made the selections we did because the work of our authors ties together nicely and seemed to us to have direct applicability in academic settings

Empathizing with the End User : Effect of Empathy and Emotional Intelligence on Ideation

Trait emotional intelligence and evoked empathy may help in a task where emotion-evoking source material is utilized to ideate solutions and services for the end-user. Participants of the current study read life stories of different persons, with perspective-taking instruction to evoke either high or low empathy. The reading was followed with ideation tasks, first identifying problems that the person of the story is facing, and then creating initial ideas for products or services to help with these problems. The perspective-taking empathy manipulation had an expected effect to the self-reported state empathy; however, it did not have an effect on the performance in the ideation tasks. Trait emotional intelligence was related to the detection of the problems and to the generating of more ideas. The results imply that emotional intelligence may be beneficial in ideation process where perspective of the customer or end user has to be considered.Peer reviewe

The Boston University Photonics Center annual report 2015-2016

This repository item contains an annual report that summarizes activities of the Boston University Photonics Center in the 2015-2016 academic year. The report provides quantitative and descriptive information regarding photonics programs in education, interdisciplinary research, business innovation, and technology development. The Boston University Photonics Center (BUPC) is an interdisciplinary hub for education, research, scholarship, innovation, and technology development associated with practical uses of light.This has been a good year for the Photonics Center. In the following pages, you will see that this year the Center’s faculty received prodigious honors and awards, generated more than 100 notable scholarly publications in the leading journals in our field, and attracted $18.9M in new research grants/contracts. Faculty and staff also expanded their efforts in education and training, and cooperated in supporting National Science Foundation sponsored Sites for Research Experiences for Undergraduates and for Research Experiences for Teachers. As a community, we emphasized the theme of “Frontiers in Plasmonics as Enabling Science in Photonics and Beyond” at our annual symposium, hosted by Bjoern Reinhard. We continued to support the National Photonics Initiative, and contributed as a cooperating site in the American Institute for Manufacturing Integrated Photonics (AIM Photonics) which began this year as a new photonics-themed node in the National Network of Manufacturing Institutes. Highlights of our research achievements for the year include an ambitious new DoD-sponsored grant for Development of Less Toxic Treatment Strategies for Metastatic and Drug Resistant Breast Cancer Using Noninvasive Optical Monitoring led by Professor Darren Roblyer, continued support of our NIH-sponsored, Center for Innovation in Point of Care Technologies for the Future of Cancer Care led by Professor Cathy Klapperich, and an exciting confluence of new grant awards in the area of Neurophotonics led by Professors Christopher Gabel, Timothy Gardner, Xue Han, Jerome Mertz, Siddharth Ramachandran, Jason Ritt, and John White. Neurophotonics is fast becoming a leading area of strength of the Photonics Center. The Industry/University Collaborative Research Center, which has become the centerpiece of our translational biophotonics program, continues to focus onadvancing the health care and medical device industries, and has entered its sixth year of operation with a strong record of achievement and with the support of an enthusiastic industrial membership base