Detailed Modeling and Reliability Analysis of Fault-Tolerant Processor Arrays

Recent advances in VLSI/WSI technology have led to the design of processor arrays with a large number of processing elements confined in small areas. The use of redundancy to increase fault-tolerance has the effect of reducing the ratio of area dedicated to processing elements over the area occupied by other resources in the array. The assumption of fault-free hardware support (switches, buses, interconnection links, etc.,), leads at best to conservative reliability estimates. However, detailed modeling entails not only an explosive growth in the model state space but also a difficult model construction process. To address the latter problem, a systematic method to construct Markov models for the reliability evaluation of processor arrays is proposed. This method is based on the premise that the fault behavior of a processor array can be modeled by a Stochastic Petri Net (SPN). However, in order to obtain a more compact representation, a set of attributes is associated with each transition in the Petri net model. This representation is referred to as a Modified Stochastic Petri Net (MSPN) model. A MSPN allows the construction of the corresponding Markov model as the reachability graph is being generated. The Markov model generated can include the effect of failures of several different components of the array as well as the effect of a peculiar distribution of faults when the reconfiguration occurs. Specific reconfiguration schemes such as Successive Row Elimination (SRE), Alternate Row-Column Elimination (ARCE) and Direct Reconfiguration (DR), are analyze

Length-mass allometries in amphibians

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Real-time HSPA emulator for end-to-edge QoS evaluation in all-IP beyond 3G heterogeneous wireless networks

This paper is aimed at presenting the real-time High Speed Packet Access (HSPA) emulator that has been developed in the framework of the AROMA project. Real-time emula- tors allow reproducing realistic scenarios to test algorithms, strategies, protocols and applications under realistic condi- tions. Therefore, real-time emulators constitute a powerful tool to evaluate the end-user's Quality of Experience (QoE), which could not be achieved by means of o -line simulations. The presented emulator is integrated in the AROMA real- time testbed, which has been developed to provide a frame- work for demonstrating the bene ts of the common radio re- source management algorithms as well as the proposed end- to-edge Quality of Service (QoS) management techniques developed for all-IP beyond 3G heterogeneous wireless net- works in the context of the AROMA project. This paper presents a qualitative description of the developed tool, em- phasizing some interesting implementation details that may result helpful in the development of similar emulation plat- forms. Some illustrative results, showing the capabilities of the developed tool, are also presented and analyzed.Postprint (published version

Assessing the reliability of species distribution projections in climate change research

Aim: Forecasting changes in species distribution under future scenarios is one of the most prolific areas of application for species distribution models (SDMs). However, no consensus yet exists on the reliability of such models for drawing conclusions on species’ distribution response to changing climate. In this study, we provide an overview of common modelling practices in the field and assess the reliability of model predictions using a virtual species approach. Location: Global. Methods: We first review papers published between 2015 and 2019. Then, we use a virtual species approach and three commonly applied SDM algorithms (GLM, MaxEnt and random forest) to assess the estimated and actual predictive performance of models parameterized with different modelling settings and violations of modelling assumptions. Results: Most SDM papers relied on single models (65%) and small samples (N < 50, 62%), used presence-only data (85%), binarized models' output (74%) and used a split-sample validation (94%). Our simulation reveals that the split-sample validation tends to be over-optimistic compared to the real performance, whereas spatial block validation provides a more honest estimate, except when datasets are environmentally biased. The binarization of predicted probabilities of presence reduces models’ predictive ability considerably. Sample size is one of the main predictors of the real model accuracy, but has little influence on estimated accuracy. Finally, the inclusion of ecologically irrelevant predictors and the violation of modelling assumptions increases estimated accuracy but decreases real accuracy of model projections, leading to biased estimates of range contraction and expansion. Main conclusions: Our ability to predict future species distribution is low on average, particularly when models’ predictions are binarized. A robust validation by spatially independent samples is required, but does not rule out inflation of model accuracy by assumption violation. Our findings call for caution in the application and interpretation of SDM projections under different climates

Intact but empty forests? Patterns of hunting-induced mammal defaunation in the tropics

Tropical forests are increasingly degraded by industrial logging, urbanization, agriculture, and infrastructure, with only 20% of the remaining area considered intact. However, this figure does not include other, more cryptic but pervasive forms of degradation, such as overhunting. Here, we quantified and mapped the spatial patterns of mammal defaunation in the tropics using a database of 3,281 mammal abundance declines from local hunting studies. We simultaneously accounted for population abundance declines and the probability of local extirpation of a population as a function of several predictors related to human accessibility to remote areas and species’ vulnerability to hunting. We estimated an average abundance decline of 13% across all tropical mammal species, with medium-sized species being reduced by >27% and large mammals by >40%. Mammal populations are predicted to be partially defaunated (i.e., declines of 10%–100%) in ca. 50% of the pantropical forest area (14 million km2), with large declines (>70%) in West Africa. According to our projections, 52% of the intact forests (IFs) and 62% of the wilderness areas (WAs) are partially devoid of large mammals, and hunting may affect mammal populations in 20% of protected areas (PAs) in the tropics, particularly in West and Central Africa and Southeast Asia. The pervasive effects of overhunting on tropical mammal populations may have profound ramifications for ecosystem functioning and the livelihoods of wild-meat-dependent communities, and underscore that forest coverage alone is not necessarily indicative of ecosystem intactness. We call for a systematic consideration of hunting effects in (large-scale) biodiversity assessments for more representative estimates of human-induced biodiversity loss

Combined effects of land use and hunting on distributions of tropical mammals

Land use and hunting are 2 major pressures on biodiversity in the tropics. Yet, their combined impacts have not been systematically quantified at a large scale. We estimated the effects of both pressures on the distributions of 1884 tropical mammal species by integrating species’ range maps, detailed land-use maps (1992 and 2015), species-specific habitat preference data, and a hunting pressure model. We further identified areas where the combined impacts were greatest (hotspots) and least (coolspots) to determine priority areas for mitigation or prevention of the pressures. Land use was the main driver of reduced distribution of all mammal species considered. Yet, hunting pressure caused additional reductions in large-bodied species’ distributions. Together, land use and hunting reduced distributions of species by 41% (SD 30) on average (year 2015). Overlap between impacts was only 2% on average. Land use contributed more to the loss of distribution (39% on average) than hunting (4% on average). However, hunting reduced the distribution of large mammals by 29% on average; hence, large mammals lost a disproportional amount of area due to the combination of both pressures. Gran Chaco, the Atlantic Forest, and Thailand had high levels of impact across the species (hotspots of area loss). In contrast, the Amazon and Congo Basins, the Guianas, and Borneo had relatively low levels of impact (coolspots of area loss). Overall, hunting pressure and human land use increased from 1992 to 2015 and corresponding losses in distribution increased from 38% to 41% on average across the species. To effectively protect tropical mammals, conservation policies should address both pressures simultaneously because their effects are highly complementary. Our spatially detailed and species-specific results may support future national and global conservation agendas, including the design of post-2020 protected area targets and strategies

Applying habitat and population-density models to land-cover time series to inform IUCN Red List assessments

The IUCN (International Union for Conservation of Nature) Red List categories and criteria are the most widely used framework for assessing the relative extinction risk of species. The criteria are based on quantitative thresholds relating to the size, trends, and structure of species’ distributions and populations. However, data on these parameters are sparse and uncertain for many species and unavailable for others, potentially leading to their misclassification or classification as data deficient. We devised an approach that combines data on land-cover change, species-specific habitat preferences, population abundance, and dispersal distance to estimate key parameters (extent of occurrence, maximum area of occupancy, population size and trend, and degree of fragmentation) and hence predict IUCN Red List categories for species. We applied our approach to nonpelagic birds and terrestrial mammals globally (∼15,000 species). The predicted categories were fairly consistent with published IUCN Red List assessments, but more optimistic overall. We predicted 4.2% of species (467 birds and 143 mammals) to be more threatened than currently assessed and 20.2% of data deficient species (10 birds and 114 mammals) to be at risk of extinction. Incorporating the habitat fragmentation subcriterion reduced these predictions 1.5–2.3% and 6.4–14.9% (depending on the quantitative definition of fragmentation) for threatened and data deficient species, respectively, highlighting the need for improved guidance for IUCN Red List assessors on the application of this aspect of the IUCN Red List criteria. Our approach complements traditional methods of estimating parameters for IUCN Red List assessments. Furthermore, it readily provides an early-warning system to identify species potentially warranting changes in their extinction-risk category based on periodic updates of land-cover information. Given our method relies on optimistic assumptions about species distribution and abundance, all species predicted to be more at risk than currently evaluated should be prioritized for reassessment

On the Sample Size for the Estimation of Primary Activity Statistics Based on Spectrum Sensing

Dynamic spectrum access (DSA)/cognitive radio (CR) systems can benefit from the knowledge of the activity statistics of primary channels, which can use this information to intelligently adapt their spectrum use to the operating environment. Particularly relevant statistics are the minimum, mean and variance of the on/off period durations, the channel duty cycle and the governing distribution. However, most DSA/CR systems have limited resources (power consumption, memory capacity, computational capability) and an important question arises of how many on/off period observations are required (i.e., the number of observed on/off periods, referred to as observation sample size in this paper) to estimate the statistics of the primary channel to a certain desired level of accuracy. In this paper, closed-form expressions to link such sample size with the accuracy of the observed primary activity statistics are proposed. A comprehensive theoretical analysis is performed on the required number of observed on/off periods to obtain a specific estimation accuracy. The accuracy of the obtained analytical results is validated and corroborated with both simulation and experimental results, showing a perfect agreement. The analytical results derived in this paper can be used in the design and dimensioning of DSA/CR systems in which the spectrum awareness function relies on spectrum sensing