Influences of agricultural landuse and seasonal changes in abiotic conditions on invertebrate colonisation of riparian leaf detritus in intermittent streams

The structure and function of agricultural stream reaches with sparse riparian and floodplain vegetation differ from those of forested reaches, but may be ‘reset’ as these streams flow through reaches with forested riparian zones. We investigated whether invertebrate colonisation of River Red Gum (Eucalyptus camaldulensis) leaf packs in lowland intermittent streams was influenced by the adjacent reach-scale landuse (cleared farmland or forested reserve) within an agricultural catchment in Victoria, Australia. Further, we examined the influence of seasonal changes in hydrology and associated changes in abiotic conditions on the colonisation of leaves by repeating experiments over two summers and one spring. Across these experiments, there were no consistent differences in the structure of communities that colonised leaves in farmland and reserve reaches. In both seasons, most leaf colonists were collectors and few were shredders in both farmland and reserve reaches. Relative abundances of gastropod grazers were much higher in summer than in spring. The structure of invertebrate communities colonising leaves in the different reaches converged over time when streams flowed in spring, but diverged over time as the streams dried and abiotic conditions within disconnected pools became increasingly harsh in summer. Thus, patterns of leaf pack colonisation were influenced by the regional climate causing large seasonal changes in hydrology, but not by reach-scale landuse. The large-scale disturbances of agricultural landuse across the catchment and a supra-seasonal drought probably contributed to low diversities of invertebrate communities in the streams.

Processing multiple image streams for real-time monitoring of parking lots

We present a system to detect parked vehicles in a typical parking complex using multiple streams of images captured through IP connected devices. Compared to traditional object detection techniques and machine learning methods, our approach is significantly faster in detection speed in the presence of multiple image streams. It is also capable of comparable accuracy when put to test against existing methods. And this is achieved without the need to train the system that machine learning methods require. Our approach uses a combination of psychological insights obtained from human detection and an algorithm replicating the outcomes of a SVM learner but without the noise that compromises accuracy in the normal learning process. Performance enhancements are made on the algorithm so that it operates well in the context of multiple image streams. The result is faster detection with comparable accuracy. Our experiments on images captured from a local test site shows very promising results for an implementation that is not only effective and low cost but also opens doors to new parking applications when combined with other technologies.

Generalized coupled photon transport equations for handling correlated photon streams with distinct frequencies

A generalized form of coupled photon transport equations that can handle correlated light beams with distinct frequencies is introduced. The derivation is based on the principle of energy conservation. For a single frequency, the current formulation reduces to a standard photon transport equation, and for fluorescence and phosphorescence, the diffusion models derived from the proposed photon transport model match for homogenous media. The generalized photon transport model is extended to handle wideband inputs in the frequency domain. © 2012 Optical Society of America.

Disturbance and the role of refuges in mediterranean climate streams

Refuges protect plant and animal populations from disturbance. Knowledge of refuges from disturbance in mediterranean climate rivers (med-rivers) has increased the last decade. We review disturbance processes and their relationship to refuges in streams in mediterranean climate regions (med-regions). Med-river fauna show high endemicity and their populations are often exposed to disturbance; hence the critical importance of refuges during (both seasonal and supraseasonal) disturbances. Disturbance pressures are increasing in med-regions, in particular from climatic change, salinisation, sedimentation, water extraction, hydropower generation, supraseasonal drought, and wildfire. Med-rivers show annual cycles of constrained precipitation and predictable seasonal drying, causing the biota to depend on seasonal refuges, in particular, those that are spatially predictable. This creates a spatial and temporal mosaic of inundation that determines habitat extent and refuge function. Refuges of sufficient size and duration to maintain populations, such as perennially flowing reaches, sustain biodiversity and may harbour relict populations, particularly during increasing aridification, where little other suitable habitat remains in landscapes. Therefore, disturbances that threaten perennial flows potentially cascade disproportionately to reduce regional scale biodiversity in med-regions. Conservation approaches for med-river systems need to conserve both refuges and refuge connectivity, reduce the impact of anthropogenic disturbances and sustain predictable, seasonal flow patterns.

Mining streams of short text for analysis of world-wide event evolutions

Streams of short text, such as news titles, enable us to effectively and efficiently learn the real world events that occur anywhere and anytime. Short text messages that are companied by timestamps and generally brief events using only a few words differ from other longer text documents, such as web pages, news stories, blogs, technical papers and books. For example, few words repeat in the same news titles, thus frequency of the term (i.e., TF) is not as important in short text corpus as in longer text corpus. Therefore, analysis of short text faces new challenges. Also, detecting and tracking events through short text analysis need to reliably identify events from constant topic clusters; however, existing methods, such as Latent Dirichlet Allocation (LDA), generates different topic results for a corpus at different executions. In this paper, we provide a Finding Topic Clusters using Co-occurring Terms (FTCCT) algorithm to automatically generate topics from a short text corpus, and develop an Event Evolution Mining (EEM) algorithm to discover hot events and their evolutions (i.e., the popularity degrees of events changing over time). In FTCCT, a term (i.e., a single word or a multiple-words phrase) belongs to only one topic in a corpus. Experiments on news titles of 157 countries within 4 months (from July to October, 2013) demonstrate that our FTCCT-based method (combining FTCCT and EEM) achieves far higher quality of the event's content and description words than LDA-based method (combining LDA and EEM) for analysis of streams of short text. Our method also visualizes the evolutions of the hot events. The discovered world-wide event evolutions have explored some interesting correlations of the world-wide events; for example, successive extreme weather phenomenon occur in different locations - typhoon in Hong Kong and Philippines followed hurricane and storm flood in Mexico in September 2013. © 2014 Springer Science+Business Media New York.

Responding to human full-body gestures embedded in motion data streams.

 This research created a neural-network enabled artificially intelligent performing agent that was able to learn to dance and recognise movement through a rehearsal and performance process with a human dancer. The agent exhibited emergent dance behaviour and successfully engaged in a live, semi-improvised dance performance with the human dancer.

The adaptable buffer algorithm for high quantile estimation in non-stationary data streams

The need to estimate a particular quantile of a distribution is an important problem which frequently arises in many computer vision and signal processing applications. For example, our work was motivated by the requirements of many semi-automatic surveillance analytics systems which detect abnormalities in close-circuit television (CCTV) footage using statistical models of low-level motion features. In this paper we specifically address the problem of estimating the running quantile of a data stream with non-stationary stochasticity when the memory for storing observations is limited. We make several major contributions: (i) we derive an important theoretical result which shows that the change in the quantile of a stream is constrained regardless of the stochastic properties of data, (ii) we describe a set of high-level design goals for an effective estimation algorithm that emerge as a consequence of our theoretical findings, (iii) we introduce a novel algorithm which implements the aforementioned design goals by retaining a sample of data values in a manner adaptive to changes in the distribution of data and progressively narrowing down its focus in the periods of quasi-stationary stochasticity, and (iv) we present a comprehensive evaluation of the proposed algorithm and compare it with the existing methods in the literature on both synthetic data sets and three large 'real-world' streams acquired in the course of operation of an existing commercial surveillance system. Our findings convincingly demonstrate that the proposed method is highly successful and vastly outperforms the existing alternatives, especially when the target quantile is high valued and the available buffer capacity severely limited.

Two maximum entropy-based algorithms for running quantile estimation in nonstationary data streams

The need to estimate a particular quantile of a distribution is an important problem that frequently arises in many computer vision and signal processing applications. For example, our work was motivated by the requirements of many semiautomatic surveillance analytics systems that detect abnormalities in close-circuit television footage using statistical models of low-level motion features. In this paper, we specifically address the problem of estimating the running quantile of a data stream when the memory for storing observations is limited. We make the following several major contributions: 1) we highlight the limitations of approaches previously described in the literature that make them unsuitable for nonstationary streams; 2) we describe a novel principle for the utilization of the available storage space; 3) we introduce two novel algorithms that exploit the proposed principle in different ways; and 4) we present a comprehensive evaluation and analysis of the proposed algorithms and the existing methods in the literature on both synthetic data sets and three large real-world streams acquired in the course of operation of an existing commercial surveillance system. Our findings convincingly demonstrate that both of the proposed methods are highly successful and vastly outperform the existing alternatives. We show that the better of the two algorithms (data-aligned histogram) exhibits far superior performance in comparison with the previously described methods, achieving more than 10 times lower estimate errors on real-world data, even when its available working memory is an order of magnitude smaller.

Ecologia da ictiofauna de um córrego de cabeceira da bacia do alto rio Paraná, Brasil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Ephemeroptera, Plecoptera and Trichoptera assemblages in two Atlantic Rainforest streams, Southeastern Brazil

Comunidades de Ephemeroptera, Plecoptera e Trichoptera (EPT) em substrato rochoso foram estudadas em dois riachos do Parque Estadual Intervales. Coletas com um amostrador de Surber (10 subamostras aleatórias, 1 m²) foram feitas mensalmente de setembro de 1999 a setembro de 2000 e trimestralmente de dezembro de 2000 a setembro de 2001 nos Ribeirões Bocaina e Água Comprida. A fauna de EPT do Ribeirão Bocaina foi mais diversificada e mais abundante do que a do Ribeirão Água Comprida. A fauna de EPT foi bastante diferente entre os dois riachos, tanto do ponto de vista da composição faunística quanto do ponto de vista funcional. Os resultados indicaram que não houve um padrão sazonal claro da variação temporal da densidade.

Ephemeroptera, Plecoptera and Trichoptera assemblages from riffles in mountain streams of Central Brazil: environmental factors influencing the distribution and abundance of immatures

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Allometric growth of two species of Ephemeroptera from Neotropical mountains streams

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)