Expert feedback

Expert feedback mainly consists of correct mappings between the schemas to be matched. These mappings can be seen as a bootstrap for the schema matcher, i.e., knowledge is taken as input by machine learning algorithms to classify schema instances. It may be a compulsory parameter such as in LSD/Glue [Doan et al. 2001, 2003] and APFEL [Ehrig et al. 2005],... 

In this section, we have mainly presented user inputs, i.e., optional preferences and parameters applied to data. To sum up, the quality can be improved by using external resources and expert feedback. Several tools are based on machine learning techniques either as a similarity measure (mostly at the instance level) or as a means of combining the results of similarity measures. In both cases, training data is a crucial issue. Finally, many tools propose preferences or options which add more flexibility or may improve the matching quality. The next section focuses on the parameters at the similarity measure level. 

A Bounce back Information to reporter Acknowledge report filed (e.g. automated response) Debrief reporter (e.g. telephone debriefing) Provide advice from safety experts (feedback on issue type) OutI ine issue process (and decision to escalate)... 

Millions of people in the United States currently require rehabilitation therapy due to neurologic injury and disease. In the case of stroke alone, there are approximately 600,000 new survivors each year and over 2 million survivors with chronic movement deficits. Recent evidence suggests that intensive therapy improves movement recoveryHowever, such therapy is expensive because it relies on individualized interaction with rehabilitation therapists. The type and quality of therapy also vary greatly between clinics and therapists. Little technology is available to assist patients in practicing therapy on their own. Therapy techniques that require expert feedback or that incorporate manual manipulation of the patient s limbs are often inaccessible once formal therapy is discontinued. 

There are many caveats that must be taken into account when designing a curriculum and training schedule around the use of phantoms and box trainers. Expert feedback and guidance must be adequately provided and distributed learning sessions should be incorporated to optimize learning. 

Some effective team structures distinguish between a core group and a standing advisory group. This can be helpful in assuring consistent feedback from key people (such as facility-based safety experts) who may be unable to participate in regular working sessions but whose input and endorsement will be critical to success. 

To make the process practical for community hospitals, this technology must be inexpensive and easily operated in the ED. Transmission of CT images to experienced radiologists for formal and final interpretation is essential for quality control and feedback. As a result, systems that permit remote decision-making by expert physicians reduce the manpower needed to provide acute stroke team coverage in ED without around-the-clock access to in-house stroke neurology. 

Clinical trials are costly to conduct, and results are often critical to the commercial viability of a phytochemical product. Seemingly minor decisions, such as which measurement tool to use or a single entry criterion, can produce thousands of dollars in additional costs. Likewise, a great deal of time, effort and money can be saved by having experts review the study protocol to provide feedback regarding ways to improve efficiency, reduce subject burden and insure that the objectives are being met in the most scientifically sound and cost-effective manner possible. In particular, I recommend that an expert statistician is consulted regarding sample size and power and that the assumptions used in these calculations are reviewed carefully with one or more clinicians. It is not uncommon to see two studies with very similar objectives, which vary by two-fold in the number of subjects under study. Often this can be explained by differences in the assumptions employed in the sample size calculations. 

External review is of major importance in ensuring the outcome and reportability of LSMBS study results. Additional experts have the opportunity to review the data and results just after their generation, at a point where corrections can be easily proposed and made. In addition, external review aids in achieving consistency in the results reported by different laboratories. Finally, external review provides feedback for optimization of the analytical and instrumental parameters at each laboratory. 

The publication of this book was made possible thanks to the contributions of numerous experts. Mostofthe Members ofthe lEA Hydrogen Co-ordination Croup (HCC) actively contributed providing basic information through questionnaires and short reports, as well as feedback and additions in the course ofthe work through several review and editing rounds. 

Evolving from efforts [22] to use the best features of trial-and-error, process model, expert system, and expert model approaches, QPA [23-25] combines KBES traits with online dielectric, pressure, and temperature data to implement autoclave curing control. QPA combines extensive sensor data with KBES rules to determine control actions. These rules determine curing progress based upon process feedback, and implement control action. QPA adjusts production parameters on-line as such—within the limits of its heuristics—QPA can accommodate batch-to-batch prepreg variations. 

In the development of the new experiments in this edition, we acknowledge expert advice from Prof. M. Bawendi (MIT), Prof. J. Thoen (Katholieke Universiteit Leuven), and Dr. B. Weiner (Brookhaven Instruments) as well as the assistance of faculty, teaching assistants, and undergraduate students (especially Nicole Baker, Matthew Martin, Colin Shear, Brain Theobald, and Robert Zaworski) at Oregon State University. Helpful comments also have come from a number of reviewers and from those who have used this book at other universities, and for these we are very appreciative. We encourage and welcome feedback from all who use this book, either as students or instructors. 

To initiate the global simulation, the CHEOPS server as well as the wrappers of all participating simulators are started. Next, the simulation expert specifies the input file for CHEOPS and runs the simulation. CHEOPS solves the simulation in a sequential-modular mode Each simulator is run in turn, where the simulation results are passed from one simulator to the next. In the case of feedback loops, this process is iterated until a steady state is reached (i.e., the global simulation converges). 

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