Consistent practices in artifact-mediated collaboration

The design of collaborative representations faces a challenge in integrating theoretical communication models with the context-sensitive and creative practices of human interaction. This paper presents results from a study that identified multiple, invariant communicative practices in how dyads appropriated flexible, paper-based media in discussions of wicked problems. These invariants, identified across media, participants and topics are a promising first step towards creating an abstract model for design that connects representational affordances and communicative functions. The authors identify areas where this model may challenge conventional design wisdom and discuss directions for further research.     

Parameter adaptation for generalized multiframe tasks: schedulability analysis,case study,and applications to self-suspending tasks

The generalized multiframe task model (GMF) extends the sporadic task model and multiframe task model. Each frame in the GMF model contains an execution time, a relative deadline, and a minimum inter-arrival time. These parameters are fixed after task specification time in the GMF model. However, multimedia and adaptive control systems may be overloaded and no longer stabilized when the task parameters in such systems are not flexible. In order to address this problem, deadlines and periods of frames may change to alleviate temporal overload, e.g., in the parameter adaptation and elastic scheduling model. In this paper, we propose a new model GMF-PA (the GMF model with parameter adaptation). This model allows task parameters to be flexible in arbitrary-deadline systems. A necessary schedulability test based on mixed-integer linear programming is given to check the schedulability under EDF scheduling and optimally assign frame deadlines and periods at the same time. We also prove that the test is a sufficient and necessary schedulability test when frame deadlines and periods must be integers. An approximation algorithm is also deployed to reduce computational running time and indicates a sufficient schedulability test in general. The speed-up factor of our approximation algorithm is \(1+\epsilon \) where \(\epsilon \) can be arbitrarily small, with respect to the exact schedulability test of GMF-PA tasks under EDF. We also apply the GMF model to self-suspending tasks. By extending recent work on scheduling self-suspending tasks, we remove the assumption that frame deadlines are equally assigned in self-suspending tasks, and the system is extended from constrained-deadline systems to arbitrary-deadline systems. We have done extensive experiments to show that the schedulability ratio is improved using our techniques in our GMF-PA model.     

The feasibility of general task systems with precedence constraints on multiprocessor platforms

Feasibility analysis determines (prior to system execution-time) whether a specified collection of hard-real-time jobs executed on a processing platform can meet all deadlines. In this paper, we derive near-optimal sufficient tests for determining whether a given collection of jobs can feasibly meet all deadlines upon a specified multiprocessor platform assuming job migration is permitted. The collection of jobs may contain precedence constraints upon the order of execution of these jobs. The derived tests are general enough to be applied even when the collection of jobs is incompletely specified. We discuss the applicability of these tests to the scheduling of collections of jobs that are generated by systems of recurrent real-time tasks. We also show that our feasibility conditions may be used to obtain global-EDF schedulability conditions.

Nonlinear ultimate boundedness control and stabilization of a flexible robotic arm

We treat the question of control of an elastic robotic arm of two links. Of course, this approach can be extended to other elastic arms. A nonlinear ultimate boundedness controller (UBC) is synthesized such that in the closed-loop system the joint angle tracking error is uniformly bounded and tends to a certain small neighborhood of the origin. The controller includes a reference joint angle trajectory generator and integral error feedback. Although the joint angles are controlled using the UBC, elastic modes are excited. A feedback stabilizer is designed for the linearized model including the UBC about the terminal state that is switched only in the vicinity of the equilibrium state for stabilization. Simulation results are presented to show that in the closed-loop system including the UBC and the stabilizer accurate joint angle trajectory following and elastic mode stabilization are accomplished in the presence of uncertainty.     

A meta-analytic review of depression prevention programs for children and adolescents: Factors that predict magnitude of intervention effects.

In this meta-analytic review, the authors summarized the effects of depression prevention programs for youth as well as investigated participant, intervention, provider, and research design features associated with larger effects. They identified 47 trials that evaluated 32 prevention programs, producing 60 intervention effect sizes. The average effect for depressive symptoms from pre-to-posttreatment (r = .15) and pretreatment to-follow-up (r = .11) were small, but 13 (41%) prevention programs produced significant reductions in depressive symptoms and 4 (13%) produced significant reductions in risk for future depressive disorder onset relative to control groups. Larger effects emerged for programs targeting high-risk individuals, samples with more females, samples with older adolescents, programs with a shorter duration and with homework assignments, and programs delivered by professional interventionists. Intervention content (e.g., a focus on problem-solving training or reducing negative cognitions) and design features (e.g., use of random assignment and structured interviews) were unrelated to effect sizes. Results suggest that depression prevention efforts produce a higher yield if they incorporate factors associated with larger intervention effects (e.g., selective programs with a shorter duration that include homework). (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Chimpanzees solve the trap problem when the confound of tool-use is removed.

The trap-tube problem is difficult for chimpanzees to solve; in several studies only 1 to 2 subjects learn the solution. The authors tested eight chimpanzees on a non-tool-using version of the problem to investigate whether the inclusion of a tool in previous tests of the trap problem may have masked the ability of chimpanzees to solve it. All eight learned to avoid the trap, in 40 to 100 trials. One transferred to two tasks that had no visual cue in common. The authors examined the performance of 15 chimpanzees on a new task in a 2 × 2 design: seven had experience on the two-trap box, eight had not; half of each group was tested with a tool, half without one. An ANOVA revealed a significant effect of tool-inclusion and experience (p     

Applying artificial neural network models to clinical decision making.

Because psychological assessment typically lacks biological gold standards, it traditionally has relied on clinicians' expert knowledge. A more empirically based approach frequently has applied linear models to data to derive meaningful constructs and appropriate measures. Statistical inferences are then used to assess the generality of the findings. This article introduces artificial neural networks (ANNs), flexible nonlinear modeling techniques that test a model's generality by applying its estimates against future data. ANNs have potential for overcoming some shortcomings of linear models. The basics of ANNs and their applications to psychological assessment are reviewed. Two examples of clinical decision making are described in which an ANN is compared with linear models, and the complexity of the network performance is examined. Issues salient to psychological assessment are addressed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)