Measuring Learning Outcomes in Library Instruction

The author uses clicker technology to incorporate polling and multiple choice question techniques into library instruction classes. Clickers can be used to give a keener understanding of how many students grasp the concepts presented in a specific class session. Typically, a student that aces a definition-type question will fail to answer an application-type question correctly. Immediate, electronic feedback helps to calibrate teaching approaches and gather data about learning outcomes. This presentation will analyze learning outcomes specific to scientific disciplines, and demonstrate the usefulness of clickers to engage and sustain student learning.

Improved sampling techniques for the direct simulation Monte Carlo method

The direct simulation Monte Carlo (DSMC) method is a widely used approach for flow simulations having rarefied or nonequilibrium effects. It involves heavily to sample instantaneous values from prescribed distributions using random numbers. In this note, we briefly review the sampling techniques typically employed in the DSMC method and present two techniques to speedup related sampling processes. One technique is very efficient for sampling geometric locations of new particles and the other is useful for the Larsen-Borgnakke energy distribution.

Techniques of producing monosex or sterile population of fish for aquaculture -- a review of selected literature

The need to develop techniques that can make the male grow faster in many species of fish as well as the female in some other species cannot be over-emphasized. Monosex culture of the faster growing sex can increase production if the method is reliable. The use of such techniques as manual sexing, sterilisation, hybridization, gynogenesis, androgenesis polyploidy and sex-reversal can provide solutions or partial solutions to the problems associated with sexual difference, sexual maturation and unwanted reproduction

Application of participatory rural appraisal (PRA) techniques in the verification of fisheries data and trend on Lake Kainji

After several years of surveys on the Kainji Lake fisheries activities by the Nigerian German Kainji Lake Fish promotion Project (KLFPP) trends regarding catches, yield and other parameter begin to emerge. However, it became obvious that some of the data were not quite as accurate as they were believed to be. Looking at the different editions of the statistical bulletin of Kainji Lake, concerning one given fisheries parameter, sometimes it is possible to reveal inconsistencies and unexplained trends. As compared to the survey method, PRA is primarily for analysis of differences in local phenomenon and processes. Therefore, PRA was used as a complementary tool to enhance the knowledge on issues like fisher women, entrepreneurs, gear ownership structure, mode of operation by owners of large gear number, preference in the use of twine and nylon gill nets, and reasons for misinformation on the number of fishing equipment owned by entrepreneurs, which cannot be done with frame survey. PRA techniques like timeline, mapping, seasonal calendar, transect walk and key informant interviews were utilized in the study process

Sentence comprehension before and after 1970: Topics, debates and techniques

[EN] Language Down the Garden Path traces the lines of research that grew out of Bever's classic paper. Leading scientists review over 40 years of debates on the factors at play in language comprehension, production, and acquisition (the role of prediction, grammar, working memory, prosody, abstractness, syntax and semantics mapping); the current status of universals and narrow syntax; and virtually every topic relevant in psycholinguistics since 1970. Written in an accessible and engaging style, the book will appeal to all those interested in understanding the questions that shaped, and are still shaping, this field and the ways in which linguists, cognitive scientists, psychologists, and neuroscientists are seeking to answer them.

Shipboard Techniques for Oceanographic Observations

This report gives the details of water sampling methods and chemical analyses used during MLML participation in the EOS MODIS investigations. It is intended to be used as a reference manual for those engaged in shipboard work. (PDF contains 50 pages)

Latent variable models for neural data analysis

The brain is perhaps the most complex system to have ever been subjected to rigorous scientific investigation. The scale is staggering: over 10^11 neurons, each making an average of 10^3 synapses, with computation occurring on scales ranging from a single dendritic spine, to an entire cortical area. Slowly, we are beginning to acquire experimental tools that can gather the massive amounts of data needed to characterize this system. However, to understand and interpret these data will also require substantial strides in inferential and statistical techniques. This dissertation attempts to meet this need, extending and applying the modern tools of latent variable modeling to problems in neural data analysis.

It is divided into two parts. The first begins with an exposition of the general techniques of latent variable modeling. A new, extremely general, optimization algorithm is proposed - called Relaxation Expectation Maximization (REM) - that may be used to learn the optimal parameter values of arbitrary latent variable models. This algorithm appears to alleviate the common problem of convergence to local, sub-optimal, likelihood maxima. REM leads to a natural framework for model size selection; in combination with standard model selection techniques the quality of fits may be further improved, while the appropriate model size is automatically and efficiently determined. Next, a new latent variable model, the mixture of sparse hidden Markov models, is introduced, and approximate inference and learning algorithms are derived for it. This model is applied in the second part of the thesis.

The second part brings the technology of part I to bear on two important problems in experimental neuroscience. The first is known as spike sorting; this is the problem of separating the spikes from different neurons embedded within an extracellular recording. The dissertation offers the first thorough statistical analysis of this problem, which then yields the first powerful probabilistic solution. The second problem addressed is that of characterizing the distribution of spike trains recorded from the same neuron under identical experimental conditions. A latent variable model is proposed. Inference and learning in this model leads to new principled algorithms for smoothing and clustering of spike data.