This is an independent study module, in which the emphasis is on independent learning of an area of level-7 mathematics by working through an appropriate textbook, examined by a combination of assessments. It is distinguished from a project module by the inclusion of regular assessed coursework, an in-term test and an assessed presentation and oral examination. As in a project module, students will need to liaise with academic staff for choice of topic and selection of supervisor. They will be accepted onto this module only after agreement between adviser, module organiser, and module supervisor. They will normally be expected to have a third-year average of at least 60% to be accepted.
Each MSc Mathematics student is required to complete a 60 credit project dissertation. A student must find a potential supervisor and fill out an MSc Mathematics Project Approval Form by the end of Semester B. The supervisor and project must be approved by the MSc Mathematics Exam Board Chair, in consultation with the MSc Mathematics Programme Director, and the process for this, which may involve an interview with the student, takes place as approval forms are submitted.
A typical MSc project dissertation consists of about 30 word-processed pages, securely bound, covering a specific research-level topic in mathematics or statistics, usually requiring the student to understand, explain and elaborate on results from one or more journal articles. An MSc project may also involve computation. An MSc project should help prepare a good student for PhD research and even allow an excellent student the possibility of doing some research.
The module focuses on the field of complex networked systems in its infancy and presents the structure of networks and their dynamics as a key concept across disciplines. Examples of networked systems include the Internet, the World Wide Web, social networks of acquaintance or other connections between individuals, inter-organisational networks, neural networks, metabolic networks, food webs, and many others. There is increasing evidence that such diverse networks share common topological and dynamical features, indicating the existence of robust self-organising principles and evolutionary laws that govern many natural and social systems. The course aims to develop a unified theoretical framework for the analysis of these common properties shared by a wide range of networked systems. This framework will then be used for the discussion of sociologically relevant phenomena that exhibit complex network structures and dynamics, such as epidemics of disease, cultural fads, financial crises, organisational innovation and inter-firm coordination. If public health authorities want to minimise the danger of a viral epidemic, but have limited vaccinations, how should they be distributed throughout the population? If a firm wants to initiate a word-of-mouth campaign for a new product, but can hand out free samples to only a few people, who should they pick? How vulnerable are large infrastructure networks like the power grid or the Internet to random failure or even deliberate attacks? How do new ideas become crazes, or small shocks get blown out of all proportion in the form of cascades throughout a financial system? To address these and many other problems, the course will develop a highly interdisciplinary approach to social science by combining current research literature on complex systems and social networks with contributions of relevant organisational and sociological research.
The module focuses on the structure and dynamics of a variety of complex networks, including the Internet, the World Wide Web, online social networks, inter- and intra-organisational networks, and import-export trade networks among countries. The module aims to develop a unified theoretical framework for the analysis of sociologically relevant phenomena that exhibit complex network structures and dynamics, such as information diffusion, cultural fads, financial crises, and viral marketing. Special emphasis will be placed on innovation, with a view to uncovering the structural foundations of knowledge creation, transfer, sharing, and diffusion in various empirical domains. To this end, the module will develop an interdisciplinary perspective by combining current research on complex networks with contributions from relevant organisational and sociological research.
Critical Health Geographies will enable students to deepen their understanding of, and critical response to, health-related topics covered in the Level 5 module GEG511 Health, Biomedicine and Society. Students will undertake in-depth reading of the research literature on a particular theme coupled with a mini research project on the same topic. It is anticipated that students will develop their own ideas for the mini research project with the guidance of the module tutor. Learning will primarily take place through independent study and research, supported by group seminars and one-to-one tutorials. Students will be assessed via a 6,000 word report. This will comprise a 3,000 word 'literature review' submitted part way through the module for formative feedback and a final report of 6,000 words, incorporating a revised version of the 'literature review' and a discussion of the findings of the student's research project. Good achievement (2.1 and above) on GEG5113 is a necessary prerequisite for the module.
The intended audience for this class is both those students who are CS majors as well as those intending to be CS majors. Specifically, it will be assumed that the students will know: Set Theory, Mathematical Induction, Number Theory, Functions, Equivalence Relations, Partial-Order Relations, Combinatorics, and Graph Theory at the level currently covered in CIS 160. This course could be taken immediately following CIS 160. Computation and Programming will play an essential role in this course. The students will be expected to use the Maple programming environment in homework exercises which will include: numerical and symbolic computations, simulations, and graphical displays.
These two modules (Drive, Sequencing) govern the nonexecutive posterior/basal functional systems, such as attention, alertness, visual-spatial, autonomic emotional, memory, sensory/perception, language, motor, and cognition. Interestingly, perched above the EF level and atop all of these components is Self-Awareness, believed to be the highest attribute of the frontal lobes (see Figure 17-4 in Stuss & Benson, 1986). It is viewed as separable from EF and hierarchically placed above it (p. 246-247). Noteworthy from this perspective is that self-awareness is implied, if not declared, to be the central executive that determines the activities of the lower level functions, including the EF level.
To Martha Denckla, "EF has become a useful shorthand phrase for a set of domain-general control processes . . . ." (p. 263; Denckla, 1996) She then notes what these processes are likely to involve, such as inhibition and delay of responding, anticipatory set, preparedness to act, freedom from interference, and the ability to sequence behavioral outputs. (p. 265) as well as planning (p. 265). She attributes "distinctive future tense aspects of EF constructs: 'attention to the future,' 'prospective memory,' ('remembering to remember'), or 'memory for the future' are some of the catchphrases employed." (p. 266). Denckla critically notes that EF begins with the unquestioned premise that it is whatever the frontal lobes do. The more functions we learn the frontal lobes do, she states, the more will be added to EF and vice versa. She rightly notes that the linkage of the two levels is a hypothesis, not a given. She also acknowledged a paper by Heilman (1994; cited in Denckla, 1996) presented at a scientific meeting in which he referred to EF as the "how" and "when" of intentionality, fractionated into initiate, sustain, inhibit, shift.
Quantitative and computational methods are central to the advancement of biology and medicine in the 21st century. These methods span the analysis of biomedical data, the construction of computational models for biological systems, and the design of computer systems that help biologists and physicians create and administer treatments to patients. The Biomedical Computation major prepares students to work at the cutting edge of this interface between computer science, biology, and medicine. Students begin their journey by acquiring foundational knowledge in the underlying biological and computational disciplines. They learn techniques in informatics and simulation and their numerous applications in understanding and analyzing biology at all levels, from individual molecules in cells to entire organs, organisms, and populations. Students then focus their efforts in a depth area of their choosing, and participate in a substantial research project with a Stanford faculty member. Upon graduation, students are prepared to enter a range of disciplines in either academia or industry.
"Cognition is all the knowledge and strategies that exist in long-term memory; this reservoir of information is critical to effective problem solving. The metacognitive level is aware of this lower level and contains models of the various cognitive processes as well as an understanding of how knowledge and strategies interconnect. Executive functioning coordinates these two levels of cognition by monitoring and controlling the use of the knowledge and strategies in concordance with the metacognitive level." (p. 241, Borkowski & Burke, 1996)
The mission of the undergraduate program in Engineering Physics is to provide students with a strong foundation in physics and mathematics, together with engineering and problem-solving skills. All majors take high-level math and physics courses as well as engineering courses. This background prepares them to tackle complex problems in multidisciplinary areas that are at the forefront of 21st-century technology such as aerospace physics, biophysics, computational science, quantum science & engineering, materials science, nanotechnology, electromechanical systems, energy systems, renewable energy, and any other engineering field that requires a solid background in physics. Because the program emphasizes science, mathematics, and engineering, students are well prepared to pursue graduate work in engineering, physics, or applied physics.