Janice D. Bordeaux

Attention in HIV-infected children: Results from the Hemophilia Growth and Development Study

Attentional functioning was examined in three groups of 7- to 19-year-old male participants with hemophilia: (1) HIV seronegative controls (HIV-, N = 66), (2) HIV seropositive participants with CD4+ lymphocyte counts greater than or equal to 200 (HIV+ CD4+ > or = 200, N = 79), and (3) severely immune suppressed HIV seropositive participants (HIV+ CD4+ < 200, N = 28). Two measures sensitive to attention deficits were used: the Continuous Performance Test (CPT) and the Span of Apprehension (Span). On the CPT, there was a decrement in attention in both HIV+ groups, as indexed by an increase in false alarm rate from Block 1 to Block 3, that was not present in the HIV- group. The longer the HIV+ children were required to sustain attention to the CPT, the more they responded to the incorrect stimulus. This effect decreased as age increased. Span percent correct and latency to correct were associated with the presence of a premorbid history of intracerebral hemorrhage, but were not sensitive to HIV status or degree of immune suppression in the HIV+ children, suggesting morbidity related to hemophilia. The remaining CPT and Span variables--hit rate, sensitivity, latency, percent correct, and latency to correct--showed the expected associations with age, but none showed conclusive associations with HIV status or immune suppression in the HIV+ participants.

Teaching Motion Planning Concepts to Undergraduate Students

Motion planning is a central problem in robotics. Although it is an engaging topic for undergraduate students, it is difficult to teach, and as a result, the material is often only covered at an abstract level. Deep learning could be achieved by having students implement and test different algorithms. However, there is usually no time within a single class to have students completely implement several motion planning algorithms as they require the development of many lower-level data structures. We present an ongoing project to develop a teaching module for robotic motion planning centered around an integrated software environment. The module can be taught early in the undergraduate curriculum, after students have taken an introductory programming class.

Software for project-based learning of robot motion planning

Motion planning is a core problem in robotics concerned with finding feasible paths for a given robot. Motion planning algorithms perform a search in the high-dimensional continuous space of robot configurations and exemplify many of the core algorithmic concepts of search algorithms and associated data structures. Motion planning algorithms can be explained in a simplified two-dimensional setting, but this masks many of the subtleties and complexities of the underlying problem. We have developed software for project-based learning of motion planning that enables deep learning. The projects that we have developed allow advanced undergraduate students and graduate students to reflect on the performance of existing textbook algorithms and their own variations on such algorithms. Formative assessment has been conducted at three institutions. The core of the software used for this teaching module is also used within the Robot Operating System, a widely adopted platform by the robotics research community. This allows for transfer of knowledge and skills to robotics research projects involving a large variety robot hardware platforms.