Multi-Label Classifier Chains for Bird Sound

Bird sound data collected with unattended microphones for automatic surveys, or mobile devices for citizen science, typically contain multiple simultaneously vocalizing birds of different species. However, few works have considered the multi-label structure in birdsong. We propose to use an ensemble of classifier chains combined with a histogram-of-segments representation for multi-label classification of birdsong. The proposed method is compared with binary relevance and three multi-instance multi-label learning (MIML) algorithms from prior work (which focus more on structure in the sound, and less on structure in the label sets). Experiments are conducted on two real-world birdsong datasets, and show that the proposed method usually outperforms binary relevance (using the same features and base-classifier), and is better in some cases and worse in others compared to the MIML algorithms.Comment: 6 pages, 1 figure, submission to ICML 2013 workshop on bioacoustics. Note: this is a minor revision- the blind submission format has been replaced with one that shows author names, and a few corrections have been mad

Multi-instance multi-label learning : algorithms and applications to bird bioacoustics

We consider the problem of supervised classification of bird species from audio recordings in a real-world acoustic monitoring scenario (i.e. audio data is collected in the field with an omnidirectional microphone, without human supervision). Obtaining better data about bird activity can assist conservation efforts, and improve our understanding of their interactions with the environment and other organisms. However, traditional observation methods are labor- intensive. Most prior work on machine learning for bird song is not applicable to real-world acoustic monitoring, because it assumes recordings contain only a single species of bird, while recordings typically contain multiple simultaneously vocalizing birds. We propose to use the multi-instance multi-label (MIML) framework in machine learning for the species classification problem, where the dataset is viewed as a collection of bags of instances paired with sets of labels. Furthermore, we formalize MIML instance annotation, where the goal is to predict instance labels while learning only from bag label sets. We develop the first MIML representation for audio, and several new algorithms for MIML instance annotation based on support vector machines or classifier chains. The proposed methods classify either the set of species present in a recording, or individual calls, while learning only from recordings paired with a set of species. This form of training data requires less human effort to obtain than individually labeled calls. These methods are successfully applied to audio collected in the field which included multiple simultaneously vocalizing species. The proposed algorithms for MIML classification are general, and are also applied to object recognition in images

A two-species model of a two-dimensional sandpile surface: a case of asymptotic roughening

We present and analyze a model of an evolving sandpile surface in (2 + 1) dimensions where the dynamics of mobile grains ({\rho}(x, t)) and immobile clusters (h(x, t)) are coupled. Our coupling models the situation where the sandpile is flat on average, so that there is no bias due to gravity. We find anomalous scaling: the expected logarithmic smoothing at short length and time scales gives way to roughening in the asymptotic limit, where novel and non-trivial exponents are found.Comment: 7 Pages, 6 Figures; Granular Matter, 2012 (Online

Social networks in public and community housing: the impact on employment outcomes

Author version made available here in accordance with publisher copyright policy.This article seeks to examine some of the ways in which social networks may contribute to employment outcomes for community and public housing tenants. There is a body of literature that explores the relationship between social networks and employment outcomes, and a separate literature on the relationship between housing and social networks (which is largely concerned with homeowners). However, there has been little research that links all three aspects, especially in relation to social housing. This provides a starting point for this research, which involved interviews with housing organisation staff and focus groups with tenants in two case study areas in metropolitan Adelaide, South Australia. This article reports on the findings through examining the way in which housing tenure may affect social network formation, and considering the ways that these networks can impact on job attainment. It is concluded that, overall, those in community housing appeared to fare better, in terms of employment-conducive networks, than those in public housing. This finding is related not just to the management of the housing, but also to the broader issues of stigma, area-level deprivation and intergenerational unemployment

Acoustic classification of multiple simultaneous bird species: A multi-instance multi-label approach

Although field-collected recordings typically contain multiple simultaneously vocalizing birds of different species, acoustic species classification in this setting has received little study so far. This work formulates the problem of classifying the set of species present in an audio recording using the multi-instance multi-label (MIML) framework for machine learning, and proposes a MIML bag generator for audio, i.e., an algorithm which transforms an input audio signal into a bag-of-instances representation suitable for use with MIML classifiers. The proposed representation uses a 2D time-frequency segmentation of the audio signal, which can separate bird sounds that overlap in time. Experiments using audio data containing 13 species collected with unattended omnidirectional microphones in the H. J. Andrews Experimental Forest demonstrate that the proposed methods achieve high accuracy (96.1% true positives/negatives). Automated detection of bird species occurrence using MIML has many potential applications, particularly in long-term monitoring of remote sites, species distribution modeling, and conservation planning

A transient disruption of fibroblastic transcriptional regulatory network facilitates trans-differentiation

Transcriptional Regulatory Networks (TRNs) coordinate multiple transcription factors (TFs) in concert to maintain tissue homeostasis and cellular function. The re-establishment of target cell TRNs has been previously implicated in direct trans-differentiation studies where the newly introduced TFs switch on a set of key regulatory factors to induce de novo expression and function. However, the extent to which TRNs in starting cell types, such as dermal fibroblasts, protect cells from undergoing cellular reprogramming remains largely unexplored. In order to identify TFs specific to maintaining the fibroblast state, we performed systematic knockdown of 18 fibroblast-enriched TFs and analyzed differential mRNA expression against the same 18 genes, building a Matrix-RNAi. The resulting expression matrix revealed seven highly interconnected TFs. Interestingly, suppressing four out of seven TFs generated lipid droplets and induced PPARG and CEBPA expression in the presence of adipocyte-inducing medium only, while negative control knockdown cells maintained fibroblastic character in the same induction regime. Global gene expression analyses further revealed that the knockdown-induced adipocytes expressed genes associated with lipid metabolism and significantly suppressed fibroblast genes. Overall, this study reveals the critical role of the TRN in protecting cells against aberrant reprogramming, and demonstrates the vulnerability of donor cell's TRNs, offering a novel strategy to induce transgene-free trans-differentiations

Precipitation Constrains Amphibian Chytrid Fungus Infection Rates in a Terrestrial Frog Assemblage in Jamaica, West Indies

We model Batrachochytrium dendrobatidis (Bd) infection rates in Jamaican frogs—one of the most threatened amphibian fauna in the world. The majority of species we surveyed were terrestrial direct‐developing frogs or frogs that breed in tank bromeliads, rather than those that use permanent water bodies to breed. Thus, we were able to investigate the climatic correlates of Bd infection in a frog assemblage that does not rely on permanent water bodies. We sampled frogs for Bd across all of the major habitat types on the island, used machine learning algorithms to identify climatic variables that are correlated with infection rates, and extrapolated infection rates across the island. We compared the effectiveness of the machine learning algorithms for species distribution modeling in the context of our study, and found that infection rate rose quickly with precipitation in the driest month. Infection rates also increased with mean temperature in the warmest quarter until 22 °C, and remained relatively level thereafter. Both of these results are in accordance with previous studies of the physiology of Bd . Based on our environmental results, we suggest that frogs occupying high‐precipitation habitats with cool rainy‐season temperatures, though zcurrently experiencing low frequencies of infection, may experience an increase in infection rates as global warming increases temperatures in their habitat.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106115/1/btp12093.pd