Automata modeling for cognitive interference in users' relevance judgement

Quantum theory has recently been employed to further advance the theory of information retrieval (IR). A challenging research topic is to investigate the so called quantum-like interference in users’ relevance judgement process, where users are involved to judge the relevance degree of each document with respect to a given query. In this process, users’ relevance judgement for the current document is often interfered by the judgement for previous documents, due to the interference on users’ cognitive status. Research from cognitive science has demonstrated some initial evidence of quantum-like cognitive interference in human decision making, which underpins the user’s relevance judgement process. This motivates us to model such cognitive interference in the relevance judgement process, which in our belief will lead to a better modeling and explanation of user behaviors in relevance judgement process for IR and eventually lead to more user-centric IR models. In this paper, we propose to use probabilistic automaton(PA) and quantum finite automaton (QFA), which are suitable to represent the transition of user judgement states, to dynamically model the cognitive interference when the user is judging a list of documents.

A tissue engineering solution for segmental defect regeneration in load-bearing long bones

The reconstruction of large defects (>10 mm) in humans usually relies on bone graft transplantation. Limiting factors include availability of graft material, comorbidity, and insufficient integration into the damaged bone. We compare the gold standard autograft with biodegradable composite scaffolds consisting of medical-grade polycaprolactone and tricalcium phosphate combined with autologous bone marrow-derived mesenchymal stem cells (MSCs) or recombinant human bone morphogenetic protein 7 (rhBMP-7). Critical-sized defects in sheep - a model closely resembling human bone formation and structure - were treated with autograft, rhBMP-7, or MSCs. Bridging was observed within 3 months for both the autograft and the rhBMP-7 treatment. After 12 months, biomechanical analysis and microcomputed tomography imaging showed significantly greater bone formation and superior strength for the biomaterial scaffolds loaded with rhBMP-7 compared to the autograft. Axial bone distribution was greater at the interfaces. With rhBMP-7, at 3 months, the radial bone distribution within the scaffolds was homogeneous. At 12 months, however, significantly more bone was found in the scaffold architecture, indicating bone remodeling. Scaffolds alone or with MSC inclusion did not induce levels of bone formation comparable to those of the autograft and rhBMP-7 groups. Applied clinically, this approach using rhBMP-7 could overcome autograft-associated limitations.

Learning with FIRST LEGO League

Robotics is a valuable tool for engaging students in the hands-on application of science, technology, engineering, and mathematics (STEM) concepts. Robotics competitions such as FIRST LEGO League (FLL) can increase students’ interest in the STEM subjects and can foster their problem solving and teamwork skills. This paper reports on a study investigating students’ perceptions on the influence of participating in a FLL competition on their learning. The students completed questionnaires regarding their perceptions of their learning during the FLL challenge and were also interviewed to gain a deeper understanding of their questionnaire responses. The results show that the students were engaged with the FLL challenge and held positive views regarding their experience. The results also suggest that students involved with the FLL challenge improved their learning about real-world applications, problem solving, engagement, communication, and the application of the technology/engineering cycle.

Using real-time authentic online learning scenarios to teach criminal intelligence

Criminal intelligence is an area of expertise highly sought-after internationally and within a variety of justice-related professions; however, producing university graduates with the requisite professional knowledge, as well as analytical, organisational and technical skills presents a pedagogical and technical challenge to university educators. The situation becomes even more challenging when students are undertaking their studies by distance education. This best practice session showcases the design of an online undergraduate unit for final year justice students which uses an evolving real-time criminal scenario as the focus of authentic learning activities in order to prepare students for graduate roles within the criminal intelligence and justice professions. Within the unit, students take on the role of criminal intelligence analysts, applying relevant theories, models and strategies to solve a complex but realistic crime and complete briefings and documentation to industry standards as their major summative assessment task. The session will demonstrate how the design of the online unit corresponds to authentic learning principles, and will specifically map the elements of the unit design to Herrington & Oliver’s instructional design framework for authentic learning (2000; Herrington & Herrington 2006). The session will show how a range of technologies was used to create a rich learning experience for students that could be easily maintained over multiple unit iterations without specialist technical support. The session will also discuss the unique pedagogical affordances and challenges implicated in the location of the unit within an online learning environment, and will reflect on some of the lessons learned from the development which may be relevant to other authentic online learning contexts.

Timed Probabilistic Automaton : a bridge between Raven and Song Scope for automatic species recognition

Raven and Song Scope are two automated sound anal-ysis tools based on machine learning technique for en-vironmental monitoring. Many research works have been conducted upon them, however, no or rare explo-ration mentions about the performance and comparison between them. This paper investigates the comparisons from six aspects: theory, software interface, ease of use, detection targets, detection accuracy, and potential application. Through deep exploration one critical gap is identified that there is a lack of approach to detect both syllables and call structures, since Raven only aims to detect syllables while Song Scope targets call structures. Therefore, a Timed Probabilistic Automata (TPA) system is proposed which separates syllables first and clusters them into complex structures after.

Viruses face a double defense by plant small RNAs

Plants fight viral infections with enzymes that digest viral RNA, but viruses retaliate with proteins that suppress these enzymes. To boost their antiviral response plants deploy enzymes with redundant functions.

David L. Rosenhan (1929–2012)

Presents an obituary for David L. Rosenhan (1929–2012). A distinguished psychologist and professor emeritus at Stanford University, Rosenhan died February 6, 2012, at the age of 82, after a long illness. Born in Jersey City, New Jersey, on November 22, 1929, he received a bachelor’s degree in mathematics (1951) from Yeshiva College and a master’s degree in economics (1953) and a doctorate in psychology (1958) from Columbia University. A professor of law and of psychology at Stanford University from 1971 until his retirement in 1998, Rosenhan was a pioneer in applying psychological methods to the practice of law, including the examination of expert witnesses, jury selection, and jury deliberation. A former president of the American Psychology–Law Society and of the American Board of Forensic Psychology, Rosenhan was a fellow of the American Association for the Advancement of Science, of the American Psychological Association, and of the American Psychological Society. Before joining the Stanford Law School faculty, he was a member of the faculties of Swarthmore College, Princeton University, Haverford College, and the University of Pennsylvania. He also served as a research psychologist at the Educational Testing Service. As generations of Stanford students can attest, David Rosenhan was a spellbinding lecturer who managed to convey the sense that he was speaking to each individual, no matter how large the group. To his graduate students, he was consistently encouraging and optimistic, always ready to share a joke or story, and gently encouraging of their creativity and progressive independence as researchers. The lessons he cared most about offering, in the classroom as in his research, were about human dignity and the need to confront abuse of power and human frailties.

Retooling Chinese primary school teachers to use technology creatively to promote innovation and problem solving skills in science classrooms

This paper reports on the initial phase of a Professional Learning Program (PLP) undertaken by 100 primary school teachers in China that aimed to facilitate the development of adaptive expertise in using technology to facilitate innovative science teaching and learning such as that envisaged by the Chinese Ministry of Education’s (2010-2020) education reforms. Key principles derived from literature about professional learning and scaffolding of learning informed the design of the PLP. The analysis of data revealed that the participants had made substantial progress towards the development of adaptive expertise. This was manifested not only by advances in the participants’ repertoires of Subject Matter Knowledge and Pedagogical Content Knowledge but also in changes to their levels of confidence and identities as teachers. By the end of the initial phase of the PLP, the participants had coalesced into a professional learning community that readily engaged in the sharing, peer review, reuse and adaption, and collaborative design of innovative science learning and assessment activities. The findings from the study indicate that those engaged in the development of PLPs for teachers in China need to take cognizance of certain cultural factors and traditions idiosyncratic to the Chinese educational system. A set of revised principles is then presented to inform the future design and implementation of PLPs for teachers in China.

Physical activity recognition from accelerometer data using a multi-scale ensemble method

Accurate and detailed measurement of an individual's physical activity is a key requirement for helping researchers understand the relationship between physical activity and health. Accelerometers have become the method of choice for measuring physical activity due to their small size, low cost, convenience and their ability to provide objective information about physical activity. However, interpreting accelerometer data once it has been collected can be challenging. In this work, we applied machine learning algorithms to the task of physical activity recognition from triaxial accelerometer data. We employed a simple but effective approach of dividing the accelerometer data into short non-overlapping windows, converting each window into a feature vector, and treating each feature vector as an i.i.d training instance for a supervised learning algorithm. In addition, we improved on this simple approach with a multi-scale ensemble method that did not need to commit to a single window size and was able to leverage the fact that physical activities produced time series with repetitive patterns and discriminative features for physical activity occurred at different temporal scales.

Targeting cancer with a lupus autoantibody

Systemic lupus erythematosus (SLE) is distinct among autoimmune diseases because of its association with circulating autoantibodies reactive against host DNA. The precise role that anti-DNA antibodies play in SLE pathophysiology remains to be elucidated, and potential applications of lupus autoantibodies in cancer therapy have not previously been explored. We report the unexpected finding that a cell-penetrating lupus autoantibody, 3E10, has potential as a targeted therapy for DNA repair–deficient malignancies. We find that 3E10 preferentially binds DNA single-strand tails, inhibits key steps in DNA single-strand and double-strand break repair, and sensitizes cultured tumor cells and human tumor xenografts to DNA-damaging therapy, including doxorubicin and radiation. Moreover, we demonstrate that 3E10 alone is synthetically lethal to BRCA2-deficient human cancer cells and selectively sensitizes such cells to low-dose doxorubicin. Our results establish an approach to cancer therapy that we expect will be particularly applicable to BRCA2-related malignancies such as breast, ovarian, and prostate cancers. In addition, our findings raise the possibility that lupus autoantibodies may be partly responsible for the intrinsic deficiencies in DNA repair and the unexpectedly low rates of breast, ovarian, and prostate cancers observed in SLE patients. In summary, this study provides the basis for the potential use of a lupus anti-DNA antibody in cancer therapy and identifies lupus autoantibodies as a potentially rich source of therapeutic agents.

Wicked problems in designing open online learning

This paper will report on the “wicked” problems encountered when designing an online course with bounded content in an unbounded learning environment. It will describe the dilemmas faced and decisions made by academics in an Australian university challenged by an institutional initiative to design radical, disruptive learning experiences making use of readily available online media. This bounded/unbounded environment demands new roles for instructors in adopting innovative pedagogies and teaching and learning strategies. It also creates changing and challenging roles for course designers as they deal with ill-defined parameters and unknown audiences. In this paper, we propose a novel methodology for making curricular decisions in ill-defined spaces.