Rapid scalar value classification and volume clipping for?interactive 3D medical image visualization

In many clinical scenarios, medical data visualization and interaction are important to physicians for exploring inner anatomical structures and extracting meaningful diagnostic information. Real-time high-quality volume rendering, artifact-free clipping, and rapid scalar value classification are important techniques employed in this process. Unfortunately, in practice, it is still difficult to achieve an optimal balance. In this paper, we present some strategies to address this issue, which are based on the calculation of segment-based post color attenuation and dynamic ray–plane intersection (RPI) respectively. When implemented within our visualization system, the new classification algorithm can deliver real-time performance while avoiding the “color over-accumulation” artifacts suffered by the commonly used acceleration algorithms that employ pre-integrated classification. Our new strategy can achieve an optimized balance between image quality and classification speed. Next, the RPI algorithm is used with opacity adjustment technique to effectively remove the “striping” artifacts on the clipping plane caused by the nonuniform integration length. Furthermore, we present techniques for multiple transfer function (TF) based anatomical feature enhancement and “keyhole” based endoscopic inner structure view. Finally, the algorithms are evaluated subjectively by radiologists and quantitatively compared using image power spectrum analysis.     

Janet W. Astington,Paul L. Harris and David R. Olson,eds., Developing Theories of Mind; Henry M. Wellman,The Child's Theory of Mind; Douglas Frye and Chris Moore,eds., Children's Theories of Mind: Mental States and Social Understanding JUDITH FELSON DUCHAN

Minds and Machines -     

Using narratives in conferences to improve the CMC learning environment

Abstract This paper reports on the use of short stories in Internet discussions to promote student learning. It describes off-campus teacher education students CMC discussions of short stories concerning issues in human development. The content of students' discussions is analysed, as is their perceptions of the value of the discussion stories. The results indicate that the use of narratives can improve the social environment of online conferences and contribute to collaborative student learning.     

Computational properties of networks of synchronous groups of spiking neurons

We demonstrate a model in which synchronously firing ensembles of neurons are networked to produce computational results. Each ensemble is a group of biological integrate-and-fire spiking neurons, with probabilistic interconnections between groups. An analogy is drawn in which each individual processing unit of an artificial neural network corresponds to a neuronal group in a biological model. The activation value of a unit in the artificial neural network corresponds to the fraction of active neurons, synchronously firing, in a biological neuronal group. Weights of the artificial neural network correspond to the product of the interconnection density between groups, the group size of the presynaptic group, and the postsynaptic potential heights in the synchronous group model. All three of these parameters can modulate connection strengths between neuronal groups in the synchronous group models. We give an example of nonlinear classification (XOR) and a function approximation example in which the capability of the artificial neural network can be captured by a neural network model with biological integrate-and-fire neurons configured as a network of synchronously firing ensembles of such neurons. We point out that the general function approximation capability proven for feedforward artificial neural networks appears to be approximated by networks of neuronal groups that fire in synchrony, where the groups comprise integrate-and-fire neurons. We discuss the advantages of this type of model for biological systems, its possible learning mechanisms, and the associated timing relationships.     

Work routinization and implications for ergonomic exposure assessment

Jobs in many modern settings, including manufacturing, service, agriculture and construction, are variable in their content and timing. This prompts the need for exposure assessment methods that do not assume regular work cycles. A scheme is presented for classifying levels of routinization to inform development of an appropriate exposure assessment strategy for a given occupational setting. Five levels of routinization have been defined based on the tasks of which the job is composed: 1) a single scheduled task with a regular work cycle; 2) multiple cyclical tasks; 3) a mix of cyclical and non-cyclical tasks; 4) one non-cyclical task; 5) multiple non-cyclical tasks. This classification, based primarily on job observation, is illustrated through data from a study of automobile manufacturing workers (n = 1200), from which self-assessed exposures to physical and psychosocial stressors were also obtained. In this cohort, decision latitude was greater with higher routinization level (p < 0.0001), and the least routinized jobs showed the lowest self-reported exposure to physical ergonomic stressors. The job analysis checklist developed for non-routinized jobs is presented, and limitations of the task analysis method utilized in the study are discussed. A work sampling approach to job analysis is recommended as the most efficient way to obtain a comparable unbiased exposure estimate across all routinization levels.     

Structuring the Interaction Between it and Business Units: Prototypes for Service Delivery

Companies have introduced innovative organizational structures to deal with rapidity of change, the globalization of markets, and expectations for increased quality and customer service. During this process, many have discovered that the interaction between IT and business units is key to success. This article presents the results of a pilot study that examined the structure of this interaction in eight Fortune 500 manufacturing companies. It answers the questions: What prototypes describe the interaction between IT corporate business units? What outcomes are associated with the various prototypes? What are the implications of these models of structure for the effective delivery of IT services?     

Investigation of soybean oil as a diesel fuel extender: Endurance tests

Engine performance and crankcase lubricant viscosity were followed with 1∶2 and 1∶1 fuel mixtures of degummed soybean oil in No. 2 diesel fuel in tests with a John Deere 6-cylinder, 404 cubic in. displacement, direct-injection, turbocharged engine for a total of 600 running hours. A crankcase oil contamination problem resulting in an unacceptable thickening and a potential for gelling did exist with a 50/50 blend or a greater concentration of soybean oil, but it did not occur with the 1∶2 blend. The data accumulated during the initial 600 hr running time indicates that a fuel blend of one-third degummed soybean oil and two-thirds No. 2 diesel (1∶2 blend) may be a suitable fuel for agricultural equipment during periods of diesel fuel shortages or allocations. Additional data are being accumulated and will be analyzed in the future.     

Reinforcement learning to adjust parametrized motor primitives to new situations

Humans manage to adapt learned movements very quickly to new situations by generalizing learned behaviors from similar situations. In contrast, robots currently often need to re-learn the complete movement. In this paper, we propose a method that learns to generalize parametrized motor plans by adapting a small set of global parameters, called meta-parameters. We employ reinforcement learning to learn the required meta-parameters to deal with the current situation, described by states. We introduce an appropriate reinforcement learning algorithm based on a kernelized version of the reward-weighted regression. To show its feasibility, we evaluate this algorithm on a toy example and compare it to several previous approaches. Subsequently, we apply the approach to three robot tasks, i.e., the generalization of throwing movements in darts, of hitting movements in table tennis, and of throwing balls where the tasks are learned on several different real physical robots, i.e., a Barrett WAM, a BioRob, the JST-ICORP/SARCOS CBi and a Kuka KR?6.