Non-negative matrix factorization for parameter estimation in hidden Markov models

Hidden Markov models are well-known in analysis of random processes, which exhibit temporal or spatial structure and have been successfully applied to a wide variety of applications such as but not limited to speech recognition, musical scores, handwriting, and bio-informatics. We present a novel algorithm for estimating the parameters of a hidden Markov model through the application of a non-negative matrix factorization to the joint probability distribution of two consecutive observations. We start with the discrete observation model and extend the results to the continuous observation model through a non-parametric approach of kernel density estimation. For both the cases, we present results on a toy example and compare the performance with the Baum-Welch algorithm. ©2010 IEEE

A Syllable-Level Probabilistic Framework for Bird Species Identification

In this paper, we present new probabilistic models for identifying bird species from audio recordings. We introduce the independent syllable model and consider two ways of aggregating frame level features within a syllable. We characterize each syllable as a probability distribution of its frame level features. The independent frame independent syllable (IFIS) model allows us to distinguish syllables whose feature distributions are different from one another. The Markov chain frame independent syllable (MCFIS) model is introduced for scenarios where the temporal structure within the syllable provides significant amount of discriminative information. We derive the Bayes risk minimizing classifier for each model and show that it can be approximated as a nearest neighbour classifier. Our experiments indicate that the IFIS and MCFIS models achieve 88.26% and 90.61% correct classification rates, respectively, while the equivalent SVM implementation achieves 86.15%. © 2009 IEEE

Characterization of Central Iowa Forests with Permanent Plots

We describe a standard methodology for establishing and inventorying the woody vegetation in 0.1-ha permanent forest plots and we utilize the data from 86 plots to characterize the mature forest vegetation of central Iowa. Obvious differences existed between bottomland hardwoods and upland forest stands, but disturbed uplands contained species typical of bottomlands. Little-disturbed upland forest plots included a variety of species associations, but variation among plots was continuous. The trees dominating the mature upland forests of central Iowa grow in a wide variety of habitats, with the result that predictability of stand structure at any location is limited. Stands on northerly and southerly aspects did differ consistently from one another, but other aspects were not intermediate in character. The overall structure of the forest stands we inventoried was typical of old secondary forests. Even the oldest forests of central Iowa are still relatively young, and their structure and composition is likely to change in the coming decades. The permanent plots we established will allow for documentation and analysis of this change

First Results from Betatron Matching Monitors Installed in the CERN PSB and SPS

In order to satisfy the tight emittance requirements of LHC, betatron matching monitors, based on multiturn beam profile measurements, have been proposed and installed in the CERN SPS and PSB. The SPS monitor is based on a OTR beam profile acquisition system and was installed two years ago and has since been tested. It helped to uncover a mismatch between PS and SPS. Experience and more results wil l be presented. The PSB monitor is based on a wire SEM and has been installed at the beginning of 1998. The first results presented here are very promising

Diminished soil functions occur under simulated climate change in a sup-alpine pasture, but heterotrophic temperature sensitivity indicates microbial resilience

Peer reviewedPublisher PD

Variation in Soil Respiration across Soil and Vegetation Types in an Alpine Valley.

BACKGROUND AND AIMS: Soils of mountain regions and their associated plant communities are highly diverse over short spatial scales due to the heterogeneity of geological substrates and highly dynamic geomorphic processes. The consequences of this heterogeneity for biogeochemical transfers, however, remain poorly documented. The objective of this study was to quantify the variability of soil-surface carbon dioxide efflux, known as soil respiration (Rs), across soil and vegetation types in an Alpine valley. To this aim, we measured Rs rates during the peak and late growing season (July-October) in 48 plots located in pastoral areas of a small valley of the Swiss Alps. FINDINGS: Four herbaceous vegetation types were identified, three corresponding to different stages of primary succession (Petasition paradoxi in pioneer conditions, Seslerion in more advanced stages and Poion alpinae replacing the climactic forests), as well as one (Rumicion alpinae) corresponding to eutrophic grasslands in intensively grazed areas. Soils were developed on calcareous alluvial and colluvial fan deposits and were classified into six types including three Fluvisols grades and three Cambisols grades. Plant and soil types had a high level of co-occurrence. The strongest predictor of Rs was soil temperature, yet we detected additional explanatory power of sampling month, showing that temporal variation was not entirely reducible to variations in temperature. Vegetation and soil types were also major determinants of Rs. During the warmest month (August), Rs rates varied by over a factor three between soil and vegetation types, ranging from 2.5 μmol m-2 s-1 in pioneer environments (Petasition on Very Young Fluvisols) to 8.5 μmol m-2 s-1 in differentiated soils supporting nitrophilous species (Rumicion on Calcaric Cambisols). CONCLUSIONS: Overall, this study provides quantitative estimates of spatial and temporal variability in Rs in the mountain environment, and demonstrates that estimations of soil carbon efflux at the watershed scale in complex geomorphic terrain have to account for soil and vegetation heterogeneity

Seasonal patterns and controls on net ecosystem CO2 exchange in a boreal peatland complex

We measured seasonal patterns of net ecosystem exchange (NEE) of CO2 in a diverse peatland complex underlain by discontinuous permafrost in northern Manitoba, Canada, as part of the Boreal Ecosystems Atmosphere Study (BOREAS). Study sites spanned the full range of peatland trophic and moisture gradients found in boreal environments from bog (pH 3.9) to rich fen (pH 7.2). During midseason (July‐August, 1996), highest rates of NEE and respiration followed the trophic sequence of bog (5.4 to −3.9 μmol CO2 m−2 s−1) \u3c poor fen (6.3 to −6.5 μmol CO2 m−2 s−1) \u3c intermediate fen (10.5 to −7.8 μmol CO2 m−2 s−1) \u3c rich fen (14.9 to −8.7 μmol CO2m−2 s−1). The sequence changed during spring (May‐June) and fall (September‐October) when ericaceous shrub (e.g., Chamaedaphne calyculata) bogs and sedge (Carex spp.) communities in poor to intermediate fens had higher maximum CO2 fixation rates than deciduous shrub‐dominated (Salix spp. and Betula spp.) rich fens. Timing of snowmelt and differential rates of peat surface thaw in microtopographic hummocks and hollows controlled the onset of carbon uptake in spring. Maximum photosynthesis and respiration were closely correlated throughout the growing season with a ratio of approximately 1/3 ecosystem respiration to maximum carbon uptake at all sites across the trophic gradient. Soil temperatures above the water table and timing of surface thaw and freeze‐up in the spring and fall were more important to net CO2 exchange than deep soil warming. This close coupling of maximum CO2 uptake and respiration to easily measurable variables, such as trophic status, peat temperature, and water table, will improve models of wetland carbon exchange. Although trophic status, aboveground net primary productivity, and surface temperatures were more important than water level in predicting respiration on a daily basis, the mean position of the water table was a good predictor (r2 = 0.63) of mean respiration rates across the range of plant community and moisture gradients. Q10 values ranged from 3.0 to 4.1 from bog to rich fen, but when normalized by above ground vascular plant biomass, the Q10 for all sites was 3.3