123 research outputs found
Adaptive Transmission Techniques for Mobile Satellite Links
Adapting the transmission rate in an LMS channel is a challenging task
because of the relatively fast time variations, of the long delays involved,
and of the difficulty in mapping the parameters of a time-varying channel into
communication performance. In this paper, we propose two strategies for dealing
with these impairments, namely, multi-layer coding (MLC) in the forward link,
and open-loop adaptation in the return link. Both strategies rely on
physical-layer abstraction tools for predicting the link performance. We will
show that, in both cases, it is possible to increase the average spectral
efficiency while at the same time keeping the outage probability under a given
threshold. To do so, the forward link strategy will rely on introducing some
latency in the data stream by using retransmissions. The return link, on the
other hand, will rely on a statistical characterization of a physical-layer
abstraction measure.Comment: Presented at the 30th AIAA International Communications Satellite
Systems Conference (ICSSC), Ottawa, Canada, 2012. Best Professional Paper
Awar
Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability
Post-hoc model-agnostic interpretation methods such as partial dependence
plots can be employed to interpret complex machine learning models. While these
interpretation methods can be applied regardless of model complexity, they can
produce misleading and verbose results if the model is too complex, especially
w.r.t. feature interactions. To quantify the complexity of arbitrary machine
learning models, we propose model-agnostic complexity measures based on
functional decomposition: number of features used, interaction strength and
main effect complexity. We show that post-hoc interpretation of models that
minimize the three measures is more reliable and compact. Furthermore, we
demonstrate the application of these measures in a multi-objective optimization
approach which simultaneously minimizes loss and complexity
Banse, K. and S.A. Piontkovsky (eds.). The mesoscale structure of the epipelagic ecosystem of the open Northern Arabian Sea
Book review: BANSE, K. and S.A. PIONTKOVSKY (eds.). â 2006. The mesoscale structure of the epipelagic ecosystem of the open Northern Arabian Sea. Universities Press, Hyderabad, India. 237 pp. ISBN 81 7371 496 7This book presents an extensive body of information obtained mainly from the thirtieth cruise of the R/V Professor Bodyanitsky to the Arabian Sea, carried out in 1990. It is part of a series published by the Universities Press, India, with the support of the Indian Academy of Sciences in Bangalore, whose aim is to narrow the English-Russian language gap concerning scientific literature on low-latitude oceansPeer reviewe
Analyzing the BBOB Results by Means of Benchmarking Concepts
We present methods to answer two basic questions that arise when benchmarking optimization algorithms. The first one is: which algorithm is the "best" one? and the second one is: which algorithm should I use for my real-world problem? Both are connected and neither is easy to answer. We present a theoretical framework for designing and analyzing the raw data of such benchmark experiments. This represents a first step in answering the aforementioned questions. The 2009 and 2010 BBOB benchmark results are analyzed by means of this framework and we derive insight regarding the answers to the two questions. Furthermore, we discuss how to properly aggregate rankings from algorithm evaluations on individual problems into a consensus, its theoretical background and which common pitfalls should be avoided. Finally, we address the grouping of test problems into sets with similar optimizer rankings and investigate whether these are reflected by already proposed test problem characteristics, finding that this is not always the case.FWN â Publicaties zonder aanstelling Universiteit Leide
Targetâoriented habitat and wildlife management: estimating forage quantity and quality of semiânatural grasslands with Sentinelâ1 and Sentinelâ2 data
Semiânatural grasslands represent ecosystems with high biodiversity. Their conservation depends on the removal of biomass, for example, through grazing by livestock or wildlife. For this, spatially explicit information about grassland forage quantity and quality is a prerequisite for efficient management. The recent advancements of the Sentinel satellite mission offer new possibilities to support the conservation of semiânatural grasslands. In this study, the combined use of radar (Sentinelâ1) and multispectral (Sentinelâ2) data to predict forage quantity and quality indicators of semiânatural grassland in Germany was investigated. Field data for organic acid detergent fibre concentration (oADF), crude protein concentration (CP), compressed sward height (CSH) and standing biomass dry weight (DM) collected between 2015 and 2017 were related to remote sensing data using the random forest regression algorithm. In total, 102 opticalâ and radarâbased predictor variables were used to derive an optimized dataset, maximizing the predictive power of the respective model. High R2 values were obtained for the grassland quality indicators oADF (R2 = 0.79, RMSE = 2.29%) and CP (R2 = 0.72, RMSE = 1.70%) using 15 and 8 predictor variables respectively. Lower R2 values were achieved for the quantity indicators CSH (R2 = 0.60, RMSE = 2.77 cm) and DM (R2 = 0.45, RMSE = 90.84 g/mÂČ). A permutationâbased variable importance measure indicated a strong contribution of simple ratioâbased optical indices to the model performance. In particular, the ratios between the narrow nearâinfrared and redâedge region were among the most important variables. The model performance for oADF, CP and CSH was only marginally increased by adding Sentinelâ1 data. For DM, no positive effect on the model performance was observed by combining Sentinelâ1 and Sentinelâ2 data. Thus, optical Sentinelâ2 data might be sufficient to accurately predict forage quality, and to some extent also quantity indicators of semiânatural grassland
Evaluation of random forest and ensemble methods at predicting complications following cardiac surgery
Cardiac patients undergoing surgery face increased risk of postoperative complications, due to a combination of factors, including higher risk surgery, their age at time of surgery and the presence of co-morbid conditions. They will therefore require high levels of care and clinical resources throughout their perioperative journey (i.e. before, during and after surgery). Although surgical mortality rates in the UK have remained low, postoperative complications on the other hand are common and can have a significant impact on patientsâ quality of life, increase hospital length of stay and healthcare costs. In this study we used and compared several machine learning methods â random forest, AdaBoost, gradient boosting model and stacking â to predict severe postoperative complications after cardiac surgery based on preoperative variables obtained from a surgical database of a large acute care hospital in Scotland. Our results show that AdaBoost has the best overall performance (AUC = 0.731), and also outperforms EuroSCORE and EuroSCORE II in other studies predicting postoperative complications. Random forest (Sensitivity = 0.852, negative predictive value = 0.923), however, and gradient boosting model (Sensitivity = 0.875 and negative predictive value = 0.920) have the best performance at predicting severe postoperative complications based on sensitivity and negative predictive value
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