Coarse-grained Data Research Articles

Agricultural potential and actualized development in Hawai’i: an airborne LiDAR survey of the leeward Kohala field system (Hawai’i Island)

Archaeological investigations of Hawaiian agriculture have relied on relatively coarse-grained data to investigate archipelago-wide processes, or on fine-grained data to examine patterning within localized zones of agricultural production. These trade-offs between spatial coverage and data resolution have inhibited understanding of both spatial patterns and temporal trends. Our analysis of 173 km 2 of high-resolution airborne Light Detection and Ranging (LiDAR) data for leeward Kohala, Hawai’i Island identifies spatial and temporal patterning in regional agricultural development. Differential densities of alignments suggest variable levels of agricultural intensity. Agricultural processes of expansion, segmentation, and intensification can also be discriminated, with distinct zones of the field system having undergone different mixes of development. Areas within the field system with moderate to high levels of both average production and variability in production (determined using a climate-driven productivity model) were utilized relatively early in a highly intensified manner; these areas often underwent processes of segmentation and intensification. Less productive areas were developed later and exhibit evidence of expansion with lower amounts of segmentation and intensification, at set levels of intensity. The spatial and temporal variability in agricultural activities was influenced by the diverse environmental conditions across the landscape as well as variation in cultivars and cultivation techniques. Combining the high-resolution LiDAR data from a large area with potential productivity modeling allows for a more fine-grained understanding of agricultural development in this region of the Hawaiian archipelago.

Multiscale simulation of microbe structure and dynamics

A multiscale mathematical and computational approach is developed that captures the hierarchical organization of a microbe. It is found that a natural perspective for understanding a microbe is in terms of a hierarchy of variables at various levels of resolution. This hierarchy starts with the N -atom description and terminates with order parameters characterizing a whole microbe. This conceptual framework is used to guide the analysis of the Liouville equation for the probability density of the positions and momenta of the N atoms constituting the microbe and its environment. Using multiscale mathematical techniques, we derive equations for the co-evolution of the order parameters and the probability density of the N-atom state. This approach yields a rigorous way to transfer information between variables on different space-time scales. It elucidates the interplay between equilibrium and far-from-equilibrium processes underlying microbial behavior. It also provides framework for using coarse-grained nanocharacterization data to guide microbial simulation. It enables a methodical search for free-energy minimizing structures, many of which are typically supported by the set of macromolecules and membranes constituting a given microbe. This suite of capabilities provides a natural framework for arriving at a fundamental understanding of microbial behavior, the analysis of nanocharacterization data, and the computer-aided design of nanostructures for biotechnical and medical purposes. Selected features of the methodology are demonstrated using our multiscale bionanosystem simulator DeductiveMultiscaleSimulator. Systems used to demonstrate the approach are structural transitions in the cowpea chlorotic mosaic virus, RNA of satellite tobacco mosaic virus, virus-like particles related to human papillomavirus, and iron-binding protein lactoferrin.

Identifying climatic niche shifts using coarse-grained occurrence data: a test with non-native freshwater fish

Aim We tested whether coarse-grained occurrence data can be used to detect climatic niche shifts between native and non-native ranges for a set of widely introduced freshwater fishes. Location World-wide. Methods We used a global database of freshwater fish occurrences at the river basin scale to identify native and non-native ranges for 18 of the most widely introduced fish species. We also examined climatic conditions within each river basin using fine-grained climate data. We combined this information to test whether climatic niche shifts have occurred between native and non-native ranges. We defined climatic niche shifts as instances where the ranges of a climatic variable within native and non-native basins exhibit zero overlap. Results We detected at least one climatic niche shift for each of the 18 studied species. However, we did not detect common patterns in the thermal preference or biogeographic origin of the non-native fish, hence suggesting a species-specific response. Main conclusions Coarse-grained occurrence data can be used to detect climatic niche shifts. They also enable the identification of the species experiencing niche shifts, although the mechanisms responsible for these shifts (e.g. local adaptation, dispersal limitation or physiological constraints) have yet to be determined. Furthermore, the coarse-grained approach, which highlights regions where climatic niche shifts have occurred, can be used to select specific river basins for more detailed, fine-grained studies.

The identification of critical fluctuations and phase transitions in short term and coarse-grained time series—a method for the real-time monitoring of human change processes

We introduce two complementary measures for the identification of critical instabilities and fluctuations in natural time series: the degree of fluctuations F and the distribution parameter D. Both are valid measures even of short and coarse-grained data sets, as demonstrated by artificial data from the logistic map (Feigenbaum-Scenario). A comparison is made with the application of the positive Lyapunov exponent to time series and another recently developed complexity measure-the Permutation Entropy. The results justify the application of the measures within computer-based real-time monitoring systems of human change processes. Results from process-outcome research in psychotherapy and functional neuroimaging of psychotherapy processes are provided as examples for the practical and scientific applications of the proposed measures.

From Mesoscale Back to Microscale: Reconstruction Schemes for Coarse-Grained Stochastic Lattice Systems

Starting from a microscopic stochastic lattice spin system and the corresponding coarse-grained model we introduce a mathematical strategy to recover microscopic information given the coarse-grained data. We define “reconstructed microscopic measures satisfying two conditions: (i) they are close in specific relative entropy to the initial microscopic equilibrium measure conditioned on the coarse-grained, data, and (ii) their sampling is computationally advantageous when compared to sampling directly from the conditioned microscopic equilibrium measure. By using different techniques we consider the cases of both short and long range microscopic models.

Lithologic data improve plant species distribution models based on coarse-grained ocurrence data

The aim of this study was to assess the improvement of plant species distribution models based on coarse-grained occurrence data when adding lithologic data to climatic models. The distributions of 40 woody plant species from continental Spain were modelled. A logistic regression model with climatic predictors was fitted for each species and compared to a second model with climatic and lithologic predictors. Improvements on model likelihood and prediction accuracy on validation subsamples were assessed, as well as the effect of calcicole–calcifuge habit on model improvemenClimatic models had reasonable mean prediction accuracy, but adding lithologic data improved model likelihood in most cases and increased mean prediction accuracy. Therefore, we recommend utilizing lithologic data for species distribution models based on coarse-grained occurrence data. Our data did not support the hypothesis that calcicole–calcifuge habit may explain model improvement when adding lithologic data to climatic models, but further research is needed.

Downscaling of coarse‐grained geomorphological data

AbstractOne of the basic limitations to the use of geomorphological maps is their coarse resolution relative to the needs of pure and applied geomorphological research. In response to this, attempts have been made to ‘downscale’ geomorphological information to finer spatial resolutions. However, the potential of statistical downscaling in geomorphology has been insufficiently examined. We downscaled four different periglacial features (wind deflation, palsa mire, earth hummock and sorted solifluction sheet) from a 100 ha grid to a 1 ha grid resolution utilizing two different techniques: point sampling (PSA) and direct (DA) approaches. We assessed the predictive accuracy of the models with the area under the curve (AUC) of a receiver operating characteristic plot using independent evaluation data. The PSA technique yielded encouraging results with a mean accuracy of 0·81, whereas the performance of DA was poorer. The predictive performance of the palsa mire and solifluction sheet models was excellent (AUC values from 0·89 to 0·96), whereas the AUC values of deflation and earth hummock models were lower (AUC = 0·57–0·81). The application of a point sampling approach as used here provides an efficient method to translate geomorphological information to finer resolution. However, further testing of the downscaling approaches is required before they can be applied to real‐world situations. Copyright © 2007 John Wiley & Sons, Ltd.

Exploiting coarse-grained task, data, and pipeline parallelism in stream programs

As multicore architectures enter the mainstream, there is a pressing demand for high-level programming models that can effectively map to them. Stream programming offers an attractive way to expose coarse-grained parallelism, as streaming applications (image, video, DSP, etc.) are naturally represented by independent filters that communicate over explicit data channels.In this paper, we demonstrate an end-to-end stream compiler that attains robust multicore performance in the face of varying application characteristics. As benchmarks exhibit different amounts of task, data, and pipeline parallelism, we exploit all types of parallelism in a unified manner in order to achieve this generality. Our compiler, which maps from the StreamIt language to the 16-core Raw architecture, attains a 11.2x mean speedup over a single-core baseline, and a 1.84x speedup over our previous work.

Exploiting coarse-grained task, data, and pipeline parallelism in stream programs

As multicore architectures enter the mainstream, there is a pressing demand for high-level programming models that can effectively map to them. Stream programming offers an attractive way to expose coarse-grained parallelism, as streaming applications (image, video, DSP, etc.) are naturally represented by independent filters that communicate over explicit data channels.In this paper, we demonstrate an end-to-end stream compiler that attains robust multicore performance in the face of varying application characteristics. As benchmarks exhibit different amounts of task, data, and pipeline parallelism, we exploit all types of parallelism in a unified manner in order to achieve this generality. Our compiler, which maps from the StreamIt language to the 16-core Raw architecture, attains a 11.2x mean speedup over a single-core baseline, and a 1.84x speedup over our previous work.

Exploiting coarse-grained task, data, and pipeline parallelism in stream programs

As multicore architectures enter the mainstream, there is a pressing demand for high-level programming models that can effectively map to them. Stream programming offers an attractive way to expose coarse-grained parallelism, as streaming applications (image, video, DSP, etc.) are naturally represented by independent filters that communicate over explicit data channels.In this paper, we demonstrate an end-to-end stream compiler that attains robust multicore performance in the face of varying application characteristics. As benchmarks exhibit different amounts of task, data, and pipeline parallelism, we exploit all types of parallelism in a unified manner in order to achieve this generality. Our compiler, which maps from the StreamIt language to the 16-core Raw architecture, attains a 11.2x mean speedup over a single-core baseline, and a 1.84x speedup over our previous work.

RESERVE NETWORKS BASED ON RICHNESS HOTSPOTS AND REPRESENTATION VARY WITH SCALE

While the importance of spatial scale in ecology is well established, few studies have investigated the impact of data grain on conservation planning outcomes. In this study, we compared species richness hotspot and representation networks developed at five grain sizes. We used species distribution maps for mammals and birds developed by the Arizona and New Mexico Gap Analysis Programs (GAP) to produce 1-km2, 100-kmn2, 625-km2, 2500-km2, and 10,000-km2 grid cell resolution distribution maps. We used these distribution maps to generate species richness and hotspot (95th quantile) maps for each taxon in each state. Species composition information at each grain size was used to develop two types of representation networks using the reserve selection software MARXAN. Reserve selection analyses were restricted to Arizona birds due to considerable computation requirements. We used MARXAN to create best reserve networks based on the minimum area required to represent each species at least once and equal area networks based on irreplaceability values. We also measured the median area of each species' distribution included in hotspot (mammals and birds of Arizona and New Mexico) and irreplaceability (Arizona birds) networks across all species. Mean area overlap between richness hotspot reserves identified at the five grain sizes was 29% (grand mean for four within-taxon/state comparisons), mean overlap for irreplaceability reserve networks was 32%, and mean overlap for best reserve networks was 53%. Hotspots for mammals and birds showed low overlap with a mean of 30%. Comparison of hotspots and irreplaceability networks showed very low overlap with a mean of 13%. For hotspots, median species distribution area protected within reserves declined monotonically from a high of 11% for 1-km2 networks down to 6% for 10,000-km2 networks. Irreplaceability networks showed a similar, but more variable, pattern of decline. This work clearly shows that map resolution has a profound effect on conservation planning outcomes and that hotspot and representation outcomes may be strikingly dissimilar. Thus, conservation planning is scale dependent, such that reserves developed using coarse-grained data do not subsume fine-grained reserves. Moreover, preserving both full species representation and species rich areas may require combined reserve design strategies.

BIOGEOGRAPHY AND DECLINE OF RARE PLANTS IN NEW ENGLAND: HISTORICAL EVIDENCE AND CONTEMPORARY MONITORING

Detecting range shifts and contractions is critical for determining the conservation priority of rare and declining taxa. However, data on rare species occurrences frequently lack precise information on locations and habitats and may present a biased picture of biogeographic distributions and presumed habitat preferences. Herbarium or museum specimen data, which otherwise could be useful proxies for detecting temporal trends and spatial patterns in species distributions, pose particular challenges. Using data from herbaria and Natural Heritage Programs on numbers of occurrences within individual municipalities (towns, cities, or townships), we quantified temporal changes in the estimated distributions of 110 rare plant species in the six New England (USA) states. We used the partial Solow equation and a nonparametric test to estimate the probability of observing multiple absences (gaps in the collection record) if a given population was actually still extant. Bayes' Theorem was used to estimate the probability that occurrences were misclassified as extinct. Using the probabilities obtained from these three methods, we eliminated taxa with high probabilities of pseudo-absence (that would yield an inaccurate profile of species distributions), narrowing the set for final analysis to 71 taxa. We then expressed occurrences as centroids of town polygons and estimated current and historical range areas (extents of occurrence as defined by alpha-hulls inscribing occurrences), mean distances between occurrences, and latitudinal and longitudinal range boundaries. Using a geographic information system, we modeled first, second, and third circular standard deviational polygons around the mean center of the historical range. Examining the distribution of current occurrences within each standard deviational polygon, we asked whether ranges were collapsing to a center, expanding, fragmenting, or contracting to a margin of the former range. Extant ranges of the species were, on average, almost 67% smaller than their historical ranges, and distances among occurrences decreased. Five New England hotspots were observed to contain >35% of rare plant populations. Extant occurrences were more frequently marginalized at the periphery of the historical range than would be expected by chance. Coarse-grained data on current and historical occurrences can be used to examine large suites of species to prioritize taxa and sites for conservation.

Using coarse-grained occurrence data to predict species distributions at finer spatial resolutions—possibilities and limitations

For effective conservation planning, resource managers around the world require information on species distributions. Available data, however, are often too coarse in spatial resolution to be of practical use. We describe four modelling techniques that harness the information contained in coarse-scale occurrence records to predict species’ distributions at the finer resolutions relevant to conservation officers in the field. The techniques are primarily statistical in nature, but can incorporate expert knowledge on species’ habitat preferences. We highlight the conceptual strengths and weaknesses of each, and perform a preliminary test of their comparative performance, using empirical data on the distribution of nine bird species in Uganda and satellite-derived environmental indices. Two techniques, the point sampling approach and iterative approach, yield encouraging results. Because the dominance of environmental factors controlling species’ distributions shifts with spatial scale, however, cross-scale predictions remain a formidable challenge. Further testing and refinement of the techniques we present here are undoubtedly necessary before they can be applied with any confidence.

Web traffic modeling at finer time scales and performance implications

The performance of Web sites continues to be an important research topic. Such studies are invariably based on the access logs from the servers comprising the Web site. A problem with existing access logs is the coarse granularity of the timestamps, e.g., arrival times. In this study we demonstrate and quantify the significant differences in performance obtained under diverse assumptions about the arrival process of user requests derived from the access logs, where the corresponding user response times can differ by more than an order of magnitude. This motivates the need for a general methodology to construct accurate representations of the actual arrival process of user requests from existing coarse-grained access-log data. Our analysis of the access logs from representative commercial Web sites illustrates self-similar behavior of the arrival process. We propose a drill-down methodology for constructing the arrival process at finer time scales based on the self-similar properties of the arrival process observed at coarse logging time scales. The advantage of our approach is that it maintains consistency between the properties of the arrival processes at both coarser and finer time scales. In addition, our analysis of the request size distribution from commercial Web sites demonstrates a subexponential, but not heavy-tail (power-law) distribution. Through simulations, we investigate the impact of these different traffic models on user response times.

Statistical and integrative approach for constructing biological network maps.

A goal of systems biology is to build a concrete biochemical network map, which provides an important instruction to trace the pathways of interest or to understand the mechanism of a biological system. In the postgenomic era, not only the concrete biochemical maps, but also postgenomic maps (mRNA coexpression and protein-protein interaction networks) have been extensively produced. In the biochemical map, the individual reactions are reliable, but the number of the reactions is limited, because molecular biology requires extensive experiments to verify them. By contrast, postgenomic data provide much information regarding interactions, but are coarse-grained. To expand the biochemical network, an intuitional approach, which superposes postgenomic data on the map one by one, has been carried out, but it is not effective when a large amount of the coarse-grained data is handled. In order to effectively integrate such postgenomic interactions into a biochemical map, a statistical approach would be suitable rather than intuition. In this article, we proposed a novel statistical approach that integrates postgenomic interaction networks into the biochemical network, predicting novel pathways. A statistical correlation for such different types of networks identifies functional modules; subsequently the superposition of the different networks on the functional modules predicts inter-modular relations, which are the key pathways to construct a large-scale biochemical network.

Hands-On Earth Rotation

A Foucault pendulum equipped with a low-cost computer interface is described. It allows one to measure the precession rate of the plane of oscillation due to Earth rotation within a few minutes. A graphic interface (PC tablet) serves as a frictionless two-dimensional position sensor. By using standard software tools it is possible to record the position of the pendulum up to 50 samples/s. Although the sensor provides only rather coarse-grained data, the method is remarkably precise due to statistical analysis of the data.

Emergence and collapse of money through reciprocity

In this paper we discuss that monetary exchange emerges in a reciprocal system which consists of donations and returns. Agents reciprocate goods alternately without bilateral agreement in the system, that is constructed not from the viewpoint of the recipient, but from the one of the reciprocator. Moreover, reciprocity is regarded as interaction with incomplete identification consisting of the alternate procedure of constructing a map from coarse-grained data by a contraction mapping and of applying the map to fine-grained data. We show that reciprocity gives rise to the monetary exchange which implies globality without the concept of metric space or global semantics. It seems that interaction with incomplete identification does not carry the notion of globality. On the contrary, in our model globality paradoxically occurs from the accumulation of indefinite incomplete identification.

Scaling down: On the Challenge of Estimating Abundance from Occurrence Patterns

Scaling down: On the Challenge of Estimating Abundance from Occurrence Patterns

A model of incomplete identification illustrating schooling behavior

We propose a model of interaction based upon incomplete identification consisting of alternate procedure of constructing a map from coarse-grained data by a contraction mapping and of applying the map to fine-grained data. In considering schooling of fish which is a typical emergent global behavior derived from local interactions, we argue that schooling paradoxically results from incomplete or indefinite identification of others. It suggests a system without boundary because any interaction is destined to be incomplete in this framework.

Post-pass partitioning of signal processing programs

Symmetric multiprocessor systems are increasingly common, not only as high-throughput servers, but as a vehicle for executing a single application in parallel in order to reduce its execution latency. This article presents Pedigree, a compilation tool that employs a new partitioning heuristic based on the program dependence graph (PDG). Pedigree creates overlapping, potentially interdependent threads, each executing on a subset of the SMP processors that matches the thread’s available parallelism. A unified framework is used to build threads from procedures, loop nests, loop iterations, and smaller constructs. Pedigree does not require any parallel language support; it is post-compilation tool that reads in object code. The SDIO Signal and Data Processing Benchmark Suite has been selected as an example of real-time, latency-sensitive code. Its coarse-grained data flow parallelism is naturally exploited by Pedigree to achieve speedups of 1.63×/2.13× (mean/max) and 1.71×/2.41× on two and four processors, respectively. There is roughly a 20% improvement over existing techniques that exploit only data parallelism. By exploiting the unidirectional flow of data for coarse-grained pipelining, the synchronization overhead is typically limited to less than 6% for synchronization latency of 100 cycles, and less than 2% for 10 cycles.