Separating spatially correlated signals using first-order precoders

This paper deals with blind separation of spatially correlated signals mixed by an instantaneous system. Taking advantage of the fact that the source signals are accessible in some man-made systems such as wireless communication systems, we preprocess the source signals in transmitters by a set of properly designed first-order precoders and then the coded signals are transmitted. At the receiving side, information about the precoders are utilized to perform signal separation. Compared with the existing precoder-based methods, the new method only employs the simplest first-order precoders, which reduces the delay in data transmission and is easier to implement in practical applications.

Visual effects in great bowerbird sexual displays and their implications for signal design

It is often assumed that the primary purpose of a male's sexual display is to provide information about quality, or to strongly stimulate prospective mates, but other functions of courtship displays have been relatively neglected. Male great bowerbirds (Ptilonorhynchus nuchalis) construct bowers that exploit the female's predictable field of view (FOV) during courtship displays by creating forced perspective illusions, and the quality of illusion is a good predictor of mating success. Here, we present and discuss two additional components of male courtship displays that use the female's predetermined viewpoint: (i) the rapid and diverse flashing of coloured objects within her FOV and (ii) chromatic adaptation of the female's eyes that alters her perception of the colour of the displayed objects. Neither is directly related to mating success, but both are likely to increase signal efficacy, and may also be associated with attracting and holding the female's attention. Signal efficacy is constrained by trade-offs between the signal components; there are both positive and negative interactions within multicomponent signals. Important signal components may have a threshold effect on fitness rather than the often assumed linear relationship.

Bayesian nonparametric extraction of hidden contexts from pervasive honest signals

Hidden patterns and contexts play an important part in intelligent pervasive systems. Most of the existing works have focused on simple forms of contexts derived directly from raw signals. High-level constructs and patterns have been largely neglected or remained under-explored in pervasive computing, mainly due to the growing complexity over time and the lack of efficient principal methods to extract them. Traditional parametric modeling approaches from machine learning find it difficult to discover new, unseen patterns and contexts arising from continuous growth of data streams due to its practice of training-then-prediction paradigm. In this work, we propose to apply Bayesian nonparametric models as a systematic and rigorous paradigm to continuously learn hidden patterns and contexts from raw social signals to provide basic building blocks for context-aware applications. Bayesian nonparametric models allow the model complexity to grow with data, fitting naturally to several problems encountered in pervasive computing. Under this framework, we use nonparametric prior distributions to model the data generative process, which helps towards learning the number of latent patterns automatically, adapting to changes in data and discovering never-seen-before patterns, contexts and activities. The proposed methods are agnostic to data types, however our work shall demonstrate to two types of signals: accelerometer activity data and Bluetooth proximal data. © 2014 IEEE.

Individualized arrhythmia detection with ECG signals from wearable devices

Low cost pervasive electrocardiogram (ECG) monitors is changing how sinus arrhythmia are diagnosed among patients with mild symptoms. With the large amount of data generated from long-term monitoring, come new data science and analytical challenges. Although traditional rule-based detection algorithms still work on relatively short clinical quality ECG, they are not optimal for pervasive signals collected from wearable devices - they don't adapt to individual difference and assume accurate identification of ECG fiducial points. To overcome these short-comings of the rule-based methods, this paper introduces an arrhythmia detection approach for low quality pervasive ECG signals. To achieve the robustness needed, two techniques were applied. First, a set of ECG features with minimal reliance on fiducial point identification were selected. Next, the features were normalized using robust statistics to factors out baseline individual differences and clinically irrelevant temporal drift that is common in pervasive ECG. The proposed method was evaluated using pervasive ECG signals we collected, in combination with clinician validated ECG signals from Physiobank. Empirical evaluation confirms accuracy improvements of the proposed approach over the traditional clinical rules.

Classification of electrocardiogram and auscultatory blood pressure signals using machine learning models

In this paper, two real-world medical classification problems using electrocardiogram (ECG) and auscultatory blood pressure (Korotkoff) signals are examined. A total of nine machine learning models are applied to perform classification of the medical data sets. A number of useful performance metrics which include accuracy, sensitivity, specificity, as well as the area under the receiver operating characteristic curve are computed. In addition to the original data sets, noisy data sets are generated to evaluate the robustness of the classifiers against noise. The 10-fold cross validation method is used to compute the performance statistics, in order to ensure statistically reliable results pertaining to classification of the ECG and Korotkoff signals are produced. The outcomes indicate that while logistic regression models perform the best with the original data set, ensemble machine learning models achieve good accuracy rates with noisy data sets.

Illusions vary because of the types of decorations at bowers, not male skill at arranging them, in great bowerbirds

Many animals use extended phenotypes to attract mates, but the availability of suitable resources in the environment can affect the size and form of these signals, with unknown consequences for honest signalling. In some populations of the great bowerbird, Ptilonorhynchus nuchalis, males arrange decorations by size, with smaller decorations placed closer to the bower entrance than larger decorations. This may create a more even background pattern from the female's viewpoint within the bower than if decorations were arranged randomly. Males show consistent, individual variation in the size-distance gradient, which could reflect variation among males in the cognitive skills needed to arrange decorations. We examined whether individual consistency in gradient characteristics is related to a male's skill at decoration arrangement or the types of decorations at bowers. We paired 18 males and switched bower decorations between pairs. We measured gradient characteristics before switching and 4 and 8 days after switching. Gradient characteristics after switching were related to those of the bower from which decorations were received, not to those of the male's own bower before switching. Gradient characteristics were also related to the types of decorations received, including bones and snail shells. These results suggest that variation among males in the size-distance gradient is explained by differences in the availability of decorations at bowers, not the cognitive skills required to arrange decorations. Although variation in gradient characteristics could indicate the male's ability to locate and transport particular decorations, it could also reflect local availability of objects, with no relationship to male quality.

Investment in sensory structures, testis size, and wing coloration in males of a diurnal moth species: trade-offs or correlated growth?

For dioecious animals, reproductive success typically involves an exchange between the sexes of signals that provide information about mate location and quality. Typically, the elaborate, secondary sexual ornaments of males signal their quality, while females may signal their location and receptivity. In theory, the receptor structures that receive the latter signals may also become elaborate or enlarged in a way that ultimately functions to enhance mating success through improved mate location. The large, elaborate antennae of many male moths are one such sensory structure, and eye size may also be important in diurnal moths. Investment in these traits may be costly, resulting in trade-offs among different traits associated with mate location. For polyandrous species, such trade-offs may also include traits associated with paternity success, such as larger testes. Conversely, we would not expect this to be the case for monandrous species, where sperm competition is unlikely. We investigated these ideas by evaluating the relationship between investment in sensory structures (antennae, eye), testis, and a putative warning signal (orange hindwing patch) in field-caught males of the monandrous diurnal painted apple moth Teia anartoides (Lepidoptera: Lymantriidae) in southeastern Australia. As predicted for a monandrous species, we found no evidence that male moths with larger sensory structures had reduced investment in testis size. However, contrary to expectation, investment in sensory structures was correlated: males with relatively larger antennae also had relatively larger eyes. Intriguingly, also, the size of male orange hindwing patches was positively correlated with testis size.

Mixed signals among tests for panel cointegration

In this paper, we study the effect that different serial correlation adjustment methods can have on panel cointegration testing. As an example, we consider the very popular tests developed by Pedroni [Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics 61, 653670., Pedroni, P. (2004). Panel cointegration: asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econometric Theory 20, 597-625.]. Results based on both simulated and real data suggest that different adjustment methods can lead to significant variations in test outcome, and thus also in the conclusions. © 2007 Elsevier B.V. All rights reserved.

The relative importance of spatial proximity, kin selection and potential ‘greenbeard’ signals on provisioning behaviour among helpers in a cooperative bird

© 2015 Springer-Verlag Berlin Heidelberg Many hypotheses have been proposed to account for cooperative behaviour, with those favouring kin selection receiving the greatest support to date. However, the importance of relatedness becomes less clear in complex societies where interactions can involve both kin and non-kin. To help clarify this, we examined the relative effect of indirect versus key direct benefit hypotheses in shaping cooperative decisions. We assessed the relative importance of likely reciprocal aid (as measured by spatial proximity between participants), kin selection (using molecular-based relatedness indices) and putative signals of relatedness (vocal similarity) on helper/helper cooperative provisioning dynamics in bell miners (Manorina melanophrys), a species living in large, complex societies. Using network analysis, we quantified the extent of shared provisioning (helping at the same nests) among individual helpers (excluding breeding pairs) over three seasons and 4290 provisioning visits, and compared these with the location of individuals within a colony and networks built using either genetic molecular relatedness or call similarity indices. Significant levels of clustering were observed in networks; individuals within a cluster were more closely related to each other than other colony members, and cluster membership was stable across years. The probability of a miner helping at another’s nest was not simply a product of spatial proximity and thus the potential for reciprocal aid. Networks constructed using helping data were significantly correlated to those built using molecular data in 5 of 10 comparisons, compared to 8 of 10 comparisons for networks constructed using call similarity. This suggests an important role of kinship in shaping helping dynamics in a complex cooperative society, apparently determined via an acoustic ‘greenbeard’ signal in this system.

Spread spectrum-based high embedding capacity watermarking method for audio signals

Abstract— Audio watermarking is a promising technology for copyright protection of audio data. Built upon the concept of spread spectrum (SS), many SS-based audio watermarking method shave been developed, where a pseudonoise (PN) sequence is usually used to introduce security. A major drawback of the existing SS-based audio watermarking methods is their low embedding capacity. In this paper, we propose a new SS-based audio watermarking method which possesses much higher embedding capacity while ensuring satisfactory imperceptibility and robustness. The high embedding capacity is achieved through a set of mechanisms: embedding multiple watermark bits in one audio segment, reducing host signal interference on watermark extraction, and adaptively adjusting PN sequence amplitude in watermark embedding based on the property of audio segments. The effectiveness of the proposed audio watermarking method is demonstrated by simulation examples.

Assortative mating among animals of captive and wild origin following experimental conservation releases

Captive breeding is a high profile management tool used for conserving threatened species. However, the inevitable consequence of generations in captivity is broad scale and often-rapid phenotypic divergence between captive and wild individuals, through environmental differences and genetic processes. Although poorly understood, mate choice preference is one of the changes that may occur in captivity that could have important implications for the reintroduction success of captive-bred animals. We bred wild-caught house mice for three generations to examine mating patterns and reproductive outcomes when these animals were simultaneously released into multiple outdoor enclosures with wild conspecifics. At release, there were significant differences in phenotypic (e.g. body mass) and genetic measures (e.g. Gst and F) between captive-bred and wild adult mice. Furthermore, 83% of offspring produced post-release were of same source parentage, inferring pronounced assortative mating. Our findings suggest that captive breeding may affect mating preferences, with potentially adverse implications for the success of threatened species reintroduction programmes.

Nonparametric discovery of movement patterns from accelerometer signals

Monitoring daily physical activity plays an important role in disease prevention and intervention. This paper proposes an approach to monitor the body movement intensity levels from accelerometer data. We collect the data using the accelerometer in a realistic setting without any supervision. The ground-truth of activities is provided by the participants themselves using an experience sampling application running on their mobile phones. We compute a novel feature that has a strong correlation with the movement intensity. We use the hierarchical Dirichlet process (HDP) model to detect the activity levels from this feature. Consisting of Bayesian nonparametric priors over the parameters the model can infer the number of levels automatically. By demonstrating the approach on the publicly available USC-HAD dataset that includes ground-truth activity labels, we show a strong correlation between the discovered activity levels and the movement intensity of the activities. This correlation is further confirmed using our newly collected dataset. We further use the extracted patterns as features for clustering and classifying the activity sequences to improve performance.

SECC: simultaneous extraction of context and community from pervasive signals

Understanding user contexts and group structures plays a central role in pervasive computing. These contexts and community structures are complex to mine from data collected in the wild due to the unprecedented growth of data, noise, uncertainties and complexities. Typical existing approaches would first extract the latent patterns to explain the human dynamics or behaviors and then use them as the way to consistently formulate numerical representations for community detection, often via a clustering method. While being able to capture high-order and complex representations, these two steps are performed separately. More importantly, they face a fundamental difficulty in determining the correct number of latent patterns and communities. This paper presents an approach that seamlessly addresses these challenges to simultaneously discover latent patterns and communities in a unified Bayesian nonparametric framework. Our Simultaneous Extraction of Context and Community (SECC) model roots in the nested Dirichlet process theory which allows nested structure to be built to explain data at multiple levels. We demonstrate our framework on three public datasets where the advantages of the proposed approach are validated.