Multi-level DEA Approach in Research Evaluation

It is well known that the discrimination power of DEA models will be diminishing if too many inputs or outputs are used. It is a dilemma if the decision makers want to select comprehensive indicators to present a relatively holistic evaluation using DEA. In this work we show that by utilizing hierarchical structures of input-output data DEA can handle quite large numbers of inputs and outputs. We present two approaches in a pilot evaluation of 15 institutes for basic research in Chinese Academy of Sciences using DEA models

System Reliability Evaluation Using Concurrent Multi-Level Simulation of Structural Faults

This paper provides a methodology that leverages state-of-the-art techniques for efficient fault simulation of structural faults together with transaction level modeling. This way it is possible to accurately evaluate the impact of the faults on the entire hardware/software syste

The Quest for Scalability and Accuracy in the Simulation of the Internet of Things: an Approach based on Multi-Level Simulation

This paper presents a methodology for simulating the Internet of Things (IoT) using multi-level simulation models. With respect to conventional simulators, this approach allows us to tune the level of detail of different parts of the model without compromising the scalability of the simulation. As a use case, we have developed a two-level simulator to study the deployment of smart services over rural territories. The higher level is base on a coarse grained, agent-based adaptive parallel and distributed simulator. When needed, this simulator spawns OMNeT++ model instances to evaluate in more detail the issues concerned with wireless communications in restricted areas of the simulated world. The performance evaluation confirms the viability of multi-level simulations for IoT environments.Comment: Proceedings of the IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications (DS-RT 2017

Interval simulation: raising the level of abstraction in architectural simulation

Detailed architectural simulators suffer from a long development cycle and extremely long evaluation times. This longstanding problem is further exacerbated in the multi-core processor era. Existing solutions address the simulation problem by either sampling the simulated instruction stream or by mapping the simulation models on FPGAs; these approaches achieve substantial simulation speedups while simulating performance in a cycle-accurate manner This paper proposes interval simulation which rakes a completely different approach: interval simulation raises the level of abstraction and replaces the core-level cycle-accurate simulation model by a mechanistic analytical model. The analytical model estimates core-level performance by analyzing intervals, or the timing between two miss events (branch mispredictions and TLB/cache misses); the miss events are determined through simulation of the memory hierarchy, cache coherence protocol, interconnection network and branch predictor By raising the level of abstraction, interval simulation reduces both development time and evaluation time. Our experimental results using the SPEC CPU2000 and PARSEC benchmark suites and the MS multi-core simulator show good accuracy up to eight cores (average error of 4.6% and max error of 11% for the multi-threaded full-system workloads), while achieving a one order of magnitude simulation speedup compared to cycle-accurate simulation. Moreover interval simulation is easy to implement: our implementation of the mechanistic analytical model incurs only one thousand lines of code. Its high accuracy, fast simulation speed and ease-of-use make interval simulation a useful complement to the architect's toolbox for exploring system-level and high-level micro-architecture trade-offs

Farm-level Economic Evaluation of Net Feed Efficiency in Australia’s Southern Beef Cattle Production System: A Multi-period Linear Programming Approach

Selection of beef cattle for increased net feed efficiency is a current major focus for research. At present the trait seems to be more apparent in Australia’s southern beef production system which is dominated by mixed farming enterprises. Farm-level evaluation of net feed efficiency should take account of the farming system for which it is proposed along with the dynamic nature of genetic selection. Gross margin, linear programming and multi-period linear programming approaches to evaluation of the trait at the farm-level using a representative farm are compared. Implications of the trait for researchers and beef producers are identifiedfarm-level evaluation, genetic traits, linear programming, Farm Management,

A multi level model of school effectiveness in a developing country

What makes one school more effective than another - particularly which inputs and management practices most efficiently enhance student achievement - has become the center of lively debate in the literature. Which method to use to compare school effects particularly concerns analysts. The model developed by the authors is able to explain most variance between schools but significantly less within schools. Only one variable slope is observed: the relationship between educational aspirations and achievement. The authors apply multi level techniques to longitudinal data recently collected by the International Association for the Evaluation of Educational Achievement in Thailand. One question they try to answer is : how do estimates obtained from the new multi level techniques compare with those obtained from ordinary regression models?Teaching and Learning,Gender and Education,Health Monitoring&Evaluation,Statistical&Mathematical Sciences,Educational Sciences

Advances in forecast evaluation

This paper surveys recent developments in the evaluation of point forecasts. Taking West's (2006) survey as a starting point, we briefly cover the state of the literature as of the time of West's writing. We then focus on recent developments, including advancements in the evaluation of forecasts at the population level (based on true, unknown model coefficients), the evaluation of forecasts in the finite sample (based on estimated model coefficients), and the evaluation of conditional versus unconditional forecasts. We present original results in a few subject areas: the optimization of power in determining the split of a sample into in-sample and out-of-sample portions; whether the accuracy of inference in evaluation of multi-step forecasts can be improved with judicious choice of HAC estimator (it can); and the extension of West's (1996) theory results for population-level, unconditional forecast evaluation to the case of conditional forecast evaluation.Forecasting

A Boolean complete neural model of adaptive behavior

A multi-layered neural assembly is developed which has the capability of learning arbitrary Boolean functions. Though the model neuron is more powerful than those previously considered, assemblies of neurons are needed to detect non-linearly separable patterns. Algorithms for learning at the neuron and assembly level are described. The model permits multiple output systens to share a common memory. Learned evaluation allows sequences of actions to be organized. Computer simulations demonstrate the capabilities of the model