On Complexity, Energy- and Implementation-Efficiency of Channel Decoders

Future wireless communication systems require efficient and flexible baseband receivers. Meaningful efficiency metrics are key for design space exploration to quantify the algorithmic and the implementation complexity of a receiver. Most of the current established efficiency metrics are based on counting operations, thus neglecting important issues like data and storage complexity. In this paper we introduce suitable energy and area efficiency metrics which resolve the afore-mentioned disadvantages. These are decoded information bit per energy and throughput per area unit. Efficiency metrics are assessed by various implementations of turbo decoders, LDPC decoders and convolutional decoders. New exploration methodologies are presented, which permit an appropriate benchmarking of implementation efficiency, communications performance, and flexibility trade-offs. These exploration methodologies are based on efficiency trajectories rather than a single snapshot metric as done in state-of-the-art approaches.Comment: Submitted to IEEE Transactions on Communication

Effects of abandonment on plant diversity in seminatural grasslands along soil and climate gradients

Questions: What are the effects of abandonment on plant diversity in semi-natural grasslands? Do the effects of abandonment on taxonomic and functional diversity vary along environmental gradients of climate and soil? Location: West and mid-Norway. Methods: Plant composition was surveyed in 110 subplots of 4 m2 in 14 sites across grazed and abandoned semi-natural grasslands. Climate data were extracted and soil composition analysed. To reduce the number of explanatory variables and deal with collinearity, we performed PCA. Data on the plant species vegetative height (H), leaf dry matter content (LDMC), specific leaf area (SLA), seed mass (SM) and number of seeds per plant (SNP) for 175 species were extracted from the LEDA database. Measures of plant diversity (species richness, CWM of functional traits and functional diversity (evenness and range)) were calculated for each subplot. To estimate the effects of abandonment on plant diversity and examine how these effects are moderated by gradients in soil and climate, we fitted mixed models to the data including site as a random effect. Results: Species richness in the subplots was lower in abandoned semi-natural grasslands, especially on more calcareous soils. CWM H, LDMC and SM were higher in abandoned semi-natural grasslands. CWM LDMC was only higher in the driest subplots. The ranges in H, SLA and SM, as well as evenness in LDMC were also higher in abandoned semi-natural grasslands, but the range in LDMC was lower. Conclusions: It is important to assess both taxonomic and functional diversity to understand ecosystem processes. The species pool in ecosystems influenced by a long history of intermediate grazing includes a high proportion of low stature, grazing-tolerant plant species. Abandonment of extensive land-use practices will cause a decline in taxonomic diversity (plant species richness) in such systems due to increased abundance of plants with high stature that outcompete the lower, grazing-tolerant plants. This process is predominant especially if moisture, soil fertility and pH are at intermediate levels. Changes in species communities due to abandonment will also influence ecosystem functioning, such as nutrient turnover and fodder production resilience. (Résumé d'auteur

Der Schutz der geographischen Nahrungsmittelherkunft in Norwegen als Übersetzungs- und Transformationsprozess

Hegnes, Atle Wehn: Der Schutz der geographischen Nahrungsmittel in Norwegen als Übersetzungs- und Transformationsprozess, in: Susanne Bauer/Christine Bischof/Stephan Gabriel Haufe/Stefan Beck/Leonore Scholze-Irrlitz (Hg.), Essen in Europa. Kulturelle Rückstände in Nahrung und Körper, Bielefeld: transcript 2010, S. 43-64. Copyright 2010 Transcript Verla

TensorQuant - A Simulation Toolbox for Deep Neural Network Quantization

Recent research implies that training and inference of deep neural networks (DNN) can be computed with low precision numerical representations of the training/test data, weights and gradients without a general loss in accuracy. The benefit of such compact representations is twofold: they allow a significant reduction of the communication bottleneck in distributed DNN training and faster neural network implementations on hardware accelerators like FPGAs. Several quantization methods have been proposed to map the original 32-bit floating point problem to low-bit representations. While most related publications validate the proposed approach on a single DNN topology, it appears to be evident, that the optimal choice of the quantization method and number of coding bits is topology dependent. To this end, there is no general theory available, which would allow users to derive the optimal quantization during the design of a DNN topology. In this paper, we present a quantization tool box for the TensorFlow framework. TensorQuant allows a transparent quantization simulation of existing DNN topologies during training and inference. TensorQuant supports generic quantization methods and allows experimental evaluation of the impact of the quantization on single layers as well as on the full topology. In a first series of experiments with TensorQuant, we show an analysis of fix-point quantizations of popular CNN topologies

Stakeholder engagement in water governance as social learning: lessons from practice

The OECD Principles on Water Governance set out various requirements for stakeholder engagement. Coupled with conceptualizations of social learning, this article asks how we define and enact stakeholder engagement and explores the actual practice of engagement of stakeholders in three fields of water governance. The results suggest that a key consideration is the purpose of the stakeholder engagement, requiring consideration of its ethics, process, roles and expected outcomes. While facilitators cannot be held accountable if stakeholder engagement ‘fails’ in terms of social learning, they are responsible for ensuring that the enabling conditions for social learning are met

Capacity development evaluation:The challenge of the results agenda and measuring return on investment in the global south

This study reviews the evaluation of capacity development, identifying capacity development (CD) modalities and the schools of evaluation currently in place. The research joins the results agenda debate, arguing that in dealing with CD interventions, pre-defined indicators fail to represent the process and the key elements that take CD recipients toward patterns of change. The study highlights the fact that CD deals with projects that, by their nature (consisting of change processes designed to initiate change in people, organizations, and/or their enabling environment), rely more on non-planned changes than on the pre-defined indicators and results to contribute to livelihood improvements and social transformation. The study recognizes the difficulty of evaluating CD under straightforward mechanisms. It concludes that the existing approaches are not adequate to truly capture or measure impact, as CD projects, restricted by previously agreed budgets, resources, and time frames, are usually not designed to evaluate the sustainability of change and its impact over the medium or long term. As resources are scarce, donor agencies and policy-makers need to know the value of CD in order to best prioritize their investments. However, due to the nature of these projects, measuring the return rate between the project cost and its impact remains a difficult task. There is a need for new, multi-path approaches to capturing changes in capacity in order to serve as a basis for decision-making regarding CD investments