Modelling collective learning in design

In this paper, a model of collective learning in design is developed in the context of team design. It explains that a team design activity uses input knowledge, environmental information, and design goals to produce output knowledge. A collective learning activity uses input knowledge from different agents and produces learned knowledge with the process of knowledge acquisition and transformation between different agents, which may be triggered by learning goals and rationale triggers. Different forms of collective learning were observed with respect to agent interactions, goal(s) of learning, and involvement of an agent. Three types of links between team design and collective learning were identified, namely teleological, rationale, and epistemic. Hypotheses of collective learning are made based upon existing theories and models in design and learning, which were tested using a protocol analysis approach. The model of collective learning in design is derived from the test results. The proposed model can be used as a basis to develop agent-based learning systems in design. In the future, collective learning between design teams, the links between collective learning and creativity, and computational support for collective learning can be investigated

The CMS Level-1 Trigger at LHC and Super-LHC

The Level-1 trigger of the CMS experiment at CERN has been designed to select proton-proton interactions whose final state includes signatures of new physics in the form of high transverse energy electrons, photons, jets, or high missing transverse energy. The Level-1 trigger system process data in a pipeline fashion at a rate of 40 MHz, has a design latency of 128 bunch crossings and an output rate of 100 KHz. The design of this system is presented with emphasis on the calorimeter triggers. After a long period of testing and validation of its performance the Level-1 trigger system has been installed and commissioned at the CMS experiment at CERN. Cosmic ray data and Monte Carlo events have been used to compare the actual performance of the trigger with expectations from off-line emulation models. Results from these studies are presented here. The limitations of this system to cope with future luminosity upgrades of the LHC, the Super-LHC, are discussed. The current CMS plan for a new CMS Level-1 trigger system at the Super-LHC is presented. The center point of the new system is a Level-1 tracking trigger which uses data from a new CMS silicon tracking detector.Comment: 8 pages 4 figure

Event-triggered Learning

The efficient exchange of information is an essential aspect of intelligent collective behavior. Event-triggered control and estimation achieve some efficiency by replacing continuous data exchange between agents with intermittent, or event-triggered communication. Typically, model-based predictions are used at times of no data transmission, and updates are sent only when the prediction error grows too large. The effectiveness in reducing communication thus strongly depends on the quality of the prediction model. In this article, we propose event-triggered learning as a novel concept to reduce communication even further and to also adapt to changing dynamics. By monitoring the actual communication rate and comparing it to the one that is induced by the model, we detect a mismatch between model and reality and trigger model learning when needed. Specifically, for linear Gaussian dynamics, we derive different classes of learning triggers solely based on a statistical analysis of inter-communication times and formally prove their effectiveness with the aid of concentration inequalities

Trigger and data acquisition

The lectures address some of the issues of triggering and data acquisition in large high-energy physics experiments. Emphasis is placed on hadron-collider experiments that present a particularly challenging environment for event selection and data collection. However, the lectures also explain how T/DAQ systems have evolved over the years to meet new challenges. Some examples are given from early experience with LHC T/DAQ systems during the 2008 single-beam operations.Comment: 32 pages, Lectures given at the 5th CERN-Latin-American School of High-Energy Physics, Recinto Quirama, Colombia, 15 - 28 Mar 200

Temporary Programs to Extend Unemployment Compensation

[From Summary] The federal/state unemployment compensation (UC) system is designed to provide temporary and partial wage replacement to workers who have become involuntarily unemployed. UC also helps to stabilize the economy by providing unemployed workers with additional purchasing power, which serves as an economic stimulus when unemployment rises during recessions. The UC system generally provides sufficient duration of benefits during periods of economic prosperity, as most UC beneficiaries experience fewer weeks of unemployment than their maximum entitlements and return to work before their benefit rights are exhausted. However, during periods of economic decline, people tend to remain unemployed longer because of the greater difficulty in finding new jobs, and a rising proportion of jobless workers exhaust UC benefits without finding new work. Thus, programs have been established to increase the number of weeks of assistance during periods of high unemployment

Applying persuasive design in a diabetes mellitus application

This paper describes persuasive design methods and compares this to an application currently under development for diabetes mellitus patients. Various elements of persuasion and a categorization of persuasion types are mentioned. Also discussed are principles of how successful persuasion should be designed, as well as the practical applications and ethics of persuasive design. This paper is not striving for completeness of theories on the topic, but uses the theories to compare it to an application intended for diabetes mellitus patients. The results of this comparison can be used for improvements of the application