Selection Strategy
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2021 ◽  
Vol 11 (18) ◽  
pp. 8693
Yifei Li ◽  
Jinlin Wang ◽  
Xiao Chen ◽  
Jinghong Wu

With the development of SDN, packet classifiers nowadays need to be provided with low update latency besides fast lookup performance because switches need to respond to update control messages from controllers in time to guarantee real-time service in SDN implementations. Classification in this scenario is called online packet classification. In this paper, we put forward an improved trie-based algorithm for online packet classification (ITOC), in which we provide a trie selection strategy to avoid occasional high update latency in the update process of online trie-based algorithms. Experiments are conducted to validate the effectiveness of our optimization and compare the performance of ITOC with the offline methods, DPDK ACL. Experimental results demonstrate that ITOC has the same level of lookup speed with DPDK ACL and greatly decreased the update latency as well. The update latency of ITOC is only 6.85% of DPDK ACL library in the best case.

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Mohammad Trik ◽  
Saadat Pour Mozaffari ◽  
Amir Massoud Bidgoli

Effective and efficient routing is one of the most important parts of routing in NoC-based neuromorphic systems. In fact, this communication structure connects different units through the packets routed by routers and switches embedded in the network on a chip. With the help of this capability, not only high scalability and high development can be created, but by decreasing the global wiring to the chip level, power consumption can be reduced. In this paper, an adaptive routing algorithm for NoC-based neuromorphic systems is proposed along with a hybrid selection strategy. Accordingly, a traffic analyzer is first used to determine the type of local or nonlocal traffic depending on the number of hops. Then, considering the type of traffic, the RCA and NoP selection strategies are used for the nonlocal and local strategies, respectively. Finally, using the experiments that performed in the simulator environment, it has been shown that this solution can well reduce the average delay time and power consumption.

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2236
Vasilis Papastefanopoulos ◽  
Pantelis Linardatos ◽  
Sotiris Kotsiantis

Outlier detection refers to the problem of the identification and, where appropriate, the elimination of anomalous observations from data. Such anomalous observations can emerge due to a variety of reasons, including human or mechanical errors, fraudulent behaviour as well as environmental or systematic changes, occurring either naturally or purposefully. The accurate and timely detection of deviant observations allows for the early identification of potentially extensive problems, such as fraud or system failures, before they escalate. Several unsupervised outlier detection methods have been developed; however, there is no single best algorithm or family of algorithms, as typically each relies on a measure of `outlierness’ such as density or distance, ignoring other measures. To add to that, in an unsupervised setting, the absence of ground-truth labels makes finding a single best algorithm an impossible feat even for a single given dataset. In this study, a new meta-learning algorithm for unsupervised outlier detection is introduced in order to mitigate this problem. The proposed algorithm, in a fully unsupervised manner, attempts not only to combine the best of many worlds from the existing techniques through ensemble voting but also mitigate any undesired shortcomings by employing an unsupervised feature selection strategy in order to identify the most informative algorithms for a given dataset. The proposed methodology was evaluated extensively through experimentation, where it was benchmarked and compared against a wide range of commonly-used techniques for outlier detection. Results obtained using a variety of widely accepted datasets demonstrated its usefulness and its state-of-the-art results as it topped the Friedman ranking test for both the area under receiver operating characteristic (ROC) curve and precision metrics when averaged over five independent trials.

2021 ◽  
Vol 53 (1) ◽  
Tristan Kistler ◽  
Benjamin Basso ◽  
Florence Phocas

Abstract Background Efficient breeding programs are difficult to implement in honeybees due to their biological specificities (polyandry and haplo-diploidy) and complexity of the traits of interest, with performances being measured at the colony scale and resulting from the joint effects of tens of thousands of workers (called direct effects) and of the queen (called maternal effects). We implemented a Monte Carlo simulation program of a breeding plan designed specifically for Apis mellifera’s populations to assess the impact of polyandry versus monoandry on colony performance, inbreeding level and genetic gain depending on the individual selection strategy considered, i.e. complete mass selection or within-family (maternal lines) selection. We simulated several scenarios with different parameter setups by varying initial genetic variances and correlations between direct and maternal effects, the selection strategy and the polyandry level. Selection was performed on colony phenotypes. Results All scenarios showed strong increases in direct breeding values of queens after 20 years of selection. Monoandry led to significantly higher direct than maternal genetic gains, especially when a negative correlation between direct and maternal effects was simulated. However, the relative increase in these genetic gains depended also on their initial genetic variability and on the selection strategy. When polyandry was simulated, the results were very similar with either 8 or 16 drones mated to each queen. Across scenarios, polyandrous mating resulted in equivalent or higher gains in performance than monoandrous mating, but with considerably lower inbreeding rates. Mass selection conferred a ~ 20% increase in performance compared to within-family selection, but was also accompanied by a strong increase in inbreeding levels (25 to 50% higher). Conclusions Our study is the first to compare the long-term effects of polyandrous versus monoandrous mating in honeybee breeding. The latter is an emergent strategy to improve specific traits, such as resistance to varroa, which can be difficult or expensive to phenotype. However, if used during several generations in a closed population, monoandrous mating increases the inbreeding level of queens much more than polyandrous mating, which is a strong limitation of this strategy.

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1558
Stefan Haase ◽  
Cesar A. de Araujo Filho ◽  
Johan Wärnå ◽  
Dmitry Yu. Murzin ◽  
Tapio Salmi

This work presents an advanced reactor selection strategy that combines elements of a knowledge-based expert system to reduce the number of feasible reactor configurations with elaborated and automatised process simulations to identify reactor performance parameters. Special focus was given to identify optimal catalyst loadings and favourable conditions for each configuration to enable a fair comparison. The workflow was exemplarily illustrated for the Ru/C-catalysed hydrogenation of arabinose and galactose to the corresponding sugar alcohols. The simulations were performed by using pseudo-2D reactor models implemented in Aspen Custom Modeler® and automatised by using the MS-Excel interface and VBA. The minichannel packings, namely wall-coated minichannel reactor (MCWR), minichannel reactor packed with catalytic particles (MCPR), and minichannel reactor packed with a catalytic open-celled foam (MCFR), outperform the conventional and miniaturised trickle-bed reactors (TBR and MTBR) in terms of space-time yield and catalyst use. However, longer reactor lengths are required to achieve 99% conversion of the sugars in MCWR and MCPR. Considering further technical challenges such as liquid distribution, packing the reactor, as well as the robustness and manufacture of catalysts in a biorefinery environment, miniaturised trickle beds are the most favourable design for a production scenario of galactitol. However, the minichannel configurations will be more advantageous for reaction systems involving consecutive and parallel reactions and highly exothermic systems.

2021 ◽  
Peder Mortvedt Isager ◽  
Anna Elisabeth van 't Veer ◽  
Daniel Lakens

Researchers seeking to replicate original research often need to decide which of several relevant candidates to select for replication. Several strategies for study selection have been proposed, utilizing a variety of observed indicators as criteria for selection. However, few strategies clearly specify the goal of study selection and how that goal is related to the indicators that are utilized. We have previously formalized a decision model of replication study selection in which the goal of study selection is to maximize the expected utility gain of the replication e?ort. We further define the concept of replication value as a proxy for expected utility gain (Isager et al., 2020). In this article, we propose a quantitative operationalization of replication value. Wefirst discuss how value and uncertainty - the two concepts used to determine replication value – could be estimated via information about citation count and sample size. Second, we propose an equation for combining these indicators into an overall estimate of replication value, which we denote RVCn. Third, we suggest how RVCn could be implemented as part of a broader study selection procedure. Finally, we provide preliminary data suggesting that studies that were in fact selected for replication tend to have relatively high RVCn estimates. The goal of this article is to explain how RVCn is intended to work and, in doing so, demonstrate the many assumptions that should be explicit in any replication study selection strategy.

2021 ◽  
Vol 184 ◽  
pp. 108344
Yi Li ◽  
Xinhua Chen ◽  
Enming Zheng ◽  
He Yang

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