scholarly journals Do highly ornamented and less parasitized males have high quality sperm? - an experimental test for parasite-induced reproductive trade-offs in European minnow (Phoxinus phoxinus)

2014 ◽  
pp. n/a-n/a ◽  
Author(s):  
Jukka Kekäläinen ◽  
Juhani Pirhonen ◽  
Jouni Taskinen
Keyword(s):  
Experimental Test ◽  
High Quality ◽  
Phoxinus Phoxinus ◽  
European Minnow ◽  
Trade Offs

scholarly journals Oxidant Trade-Offs in Immunity: An Experimental Test in a Lizard

PLoS ONE ◽  
2015 ◽  
Vol 10 (5) ◽  
pp. e0126155 ◽  
Author(s):  
Michael Tobler ◽  
Cissy Ballen ◽  
Mo Healey ◽  
Mark Wilson ◽  
Mats Olsson
Keyword(s):  
Experimental Test ◽  
Trade Offs

You Only Traverse Twice: A YOTT Placement, Routing, and Timing Approach for CGRAs

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-25
Author(s):  
Michael Canesche ◽  
Westerley Carvalho ◽  
Lucas Reis ◽  
Matheus Oliveira ◽  
Salles Magalhães ◽  
...  
Keyword(s):  
Execution Time ◽  
High Performance ◽  
Coarse Grained ◽  
Optimal Placement ◽  
Greedy Heuristics ◽  
High Quality ◽  
Solution Quality ◽  
Graph Traversal ◽  
Trade Offs ◽  
Graph Properties

Coarse-grained reconfigurable architecture (CGRA) mapping involves three main steps: placement, routing, and timing. The mapping is an NP-complete problem, and a common strategy is to decouple this process into its independent steps. This work focuses on the placement step, and its aim is to propose a technique that is both reasonably fast and leads to high-performance solutions. Furthermore, a near-optimal placement simplifies the following routing and timing steps. Exact solutions cannot find placements in a reasonable execution time as input designs increase in size. Heuristic solutions include meta-heuristics, such as Simulated Annealing (SA) and fast and straightforward greedy heuristics based on graph traversal. However, as these approaches are probabilistic and have a large design space, it is not easy to provide both run-time efficiency and good solution quality. We propose a graph traversal heuristic that provides the best of both: high-quality placements similar to SA and the execution time of graph traversal approaches. Our placement introduces novel ideas based on “you only traverse twice” (YOTT) approach that performs a two-step graph traversal. The first traversal generates annotated data to guide the second step, which greedily performs the placement, node per node, aided by the annotated data and target architecture constraints. We introduce three new concepts to implement this technique: I/O and reconvergence annotation, degree matching, and look-ahead placement. Our analysis of this approach explores the placement execution time/quality trade-offs. We point out insights on how to analyze graph properties during dataflow mapping. Our results show that YOTT is 60.6 , 9.7 , and 2.3 faster than a high-quality SA, bounding box SA VPR, and multi-single traversal placements, respectively. Furthermore, YOTT reduces the average wire length and the maximal FIFO size (additional timing requirement on CGRAs) to avoid delay mismatches in fully pipelined architectures.

scholarly journals TRADE-OFFS BETWEEN EPISTEMIC AND MORAL VALUES IN EVIDENCE-BASED POLICY

2018 ◽  
Vol 35 (1) ◽  
pp. 49-78 ◽  
Author(s):  
Donal Khosrowi
Keyword(s):  
Controlled Trials ◽  
Evidence Based ◽  
Epistemic Values ◽  
High Quality ◽  
Political Values ◽  
Randomized Controlled ◽  
High Quality Evidence ◽  
Evidence Based Policy ◽  
Trade Offs ◽  
Quality Evidence

Abstract:Proponents of evidence-based policy (EBP) call for public policy to be informed by high-quality evidence from randomized controlled trials. This methodological preference aims to promote several epistemic values, e.g. rigour, unbiasedness, precision, and the ability to obtain causal conclusions. I argue that there is a trade-off between these epistemic values and several non-epistemic, moral and political values. This is because the evidence afforded by standard EBP methods is differentially useful for pursuing different moral and political values. I expand on how this challenges ideals of value-freedom and -neutrality in EBP, and offer suggestions for how EBP methodology might be revised.

scholarly journals Crafting Efficient Neural Graph of Large Entropy

2019 ◽  
Author(s):  
Minjing Dong ◽  
Hanting Chen ◽  
Yunhe Wang ◽  
Chang Xu
Keyword(s):  
Information Flow ◽  
Network Architecture ◽  
High Performance ◽  
Global Information ◽  
High Quality ◽  
Network Pruning ◽  
Initial Network ◽  
Trade Offs ◽  
Graph Properties ◽  
Deep Cnn

Network pruning is widely applied to deep CNN models due to their heavy computation costs and achieves high performance by keeping important weights while removing the redundancy. Pruning redundant weights directly may hurt global information flow, which suggests that an efficient sparse network should take graph properties into account. Thus, instead of paying more attention to preserving important weight, we focus on the pruned architecture itself. We propose to use graph entropy as the measurement, which shows useful properties to craft high-quality neural graphs and enables us to propose efficient algorithm to construct them as the initial network architecture. Our algorithm can be easily implemented and deployed to different popular CNN models and achieve better trade-offs.

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