Landscape-wide pulse events predict trait-based responses among wetland birds in perennial channels of a dryland wetland.

Wetlands in arid or semiarid zones are vital for maintaining biodiversity but face growing threats. Flooding regime variability is a key driver of ecological dynamism in these systems, dictating primary productivity on a large spatial scale. The functional composition or diversity of wetland-dependent bird species has been found to be sensitive to fluctuations in hydrological regimes and can thus be indicative of cascading ecosystem responses associated with climate change. In this paper, we investigate whether large-scale changes in inundation and fire-a significant additional biodiversity determinant in (semi-)arid landscapes-are reliable predictors of functional group responses of wetland-dependent birds along a perennial channel of the Okavango Delta, Botswana. We fit generalized additive models (GAMs) to 6 years of bird survey data collected along ~190-km-long annual transects and use remotely sensed landscape-level inundation estimates, as well as spatiotemporal distance to fire, to predict the responsiveness of seven trait-based functional group abundances. During the surveys, a total of 89 different wetland-dependent bird species were recorded, including 76 residents, across all years, with below-surface feeding waders consistently the most abundant functional group. Including estimated spatiotemporal variability in flooding and fire, as well as their interactions, improved model fit for all seven functional groups, explaining between 46.8% and 68.3% of variability in functional group abundances. Covariates representing longer-term variability in inundation generally performed better than shorter-term ones. For example, variability in inundation over the 5 months preceding a survey best predicted the responses of all functional groups, which also all exhibited responsiveness to the interaction between flooding and fire. We were able to interpret the responses of individual functional groups, based on the resource exploitation assumption. Overall, our results suggest that perennial waters in dryland wetlands offer functional refugia to wetland-dependent birds and highlight the indicative power of large-scale trait-based bird monitoring. Our findings demonstrate the potential utility of such a monitoring regime for dryland wetland ecosystems vulnerable to industrial-scale anthropogenic pressure and associated climate change.

Score-mediated mutual consent and indirect reciprocity.

Helping strangers at a cost to oneself is a hallmark of many human interactions, but difficult to justify from the viewpoint of natural selection, particularly in anonymous one-shot interactions. Reputational scoring can provide the necessary motivation via "indirect reciprocity," but maintaining reliable scores requires close oversight to prevent cheating. We show that in the absence of such supervision, it is possible that scores might be managed by mutual consent between the agents themselves instead of by third parties. The space of possible strategies for such "consented" score changes is very large but, using a simple cooperation game, we search it, asking what kinds of agreement can i) invade a population from rare and ii) resist invasion once common. We prove mathematically and demonstrate computationally that score mediation by mutual consent does enable cooperation without oversight. Moreover, the most invasive and stable strategies belong to one family and ground the concept of value by incrementing one score at the cost of the other, thus closely resembling the token exchange that underlies money in everyday human transactions. The most successful strategy has the flavor of money except that agents without money can generate new score if they meet. This strategy is evolutionarily stable, and has higher fitness, but is not physically realizable in a decentralized way; when conservation of score is enforced more money-like strategies dominate. The equilibrium distribution of scores under any of this family of strategies is geometric, meaning that agents with score 0 are inherent to money-like strategies.

Epidemic changepoint detection in the presence of nuisance changes.

Many time series problems feature epidemic changes-segments where a parameter deviates from a background baseline. Detection of such changepoints can be improved by accounting for the epidemic structure, but this is currently difficult if the background level is unknown. Furthermore, in practical data the background often undergoes nuisance changes, which interfere with standard estimation techniques and appear as false alarms. To solve these issues, we develop a new, efficient approach to simultaneously detect epidemic changes and estimate unknown, but fixed, background level, based on a penalised cost. Using it, we build a two-level detector that models and separates nuisance and signal changes. The analytic and computational properties of the proposed methods are established, including consistency and convergence. We demonstrate via simulations that our two-level detector provides accurate estimation of changepoints under a nuisance process, while other state-of-the-art detectors fail. In real-world genomic and demographic datasets, the proposed method identified and localised target events while separating out seasonal variations and experimental artefacts. Supplementary Information: The online version contains supplementary material available at 10.1007/s00362-022-01307-x.

Incremental Learning of Human Activities in Smart Homes.

Sensor-based human activity recognition has been extensively studied. Systems learn from a set of training samples to classify actions into a pre-defined set of ground truth activities. However, human behaviours vary over time, and so a recognition system should ideally be able to continuously learn and adapt, while retaining the knowledge of previously learned activities, and without failing to highlight novel, and therefore potentially risky, behaviours. In this paper, we propose a method based on compression that can incrementally learn new behaviours, while retaining prior knowledge. Evaluation was conducted on three publicly available smart home datasets.

Holds enable one-shot reciprocal exchange.

Strangers routinely cooperate and exchange goods without any knowledge of one another in one-off encounters without recourse to a third party, an interaction that is fundamental to most human societies. However, this act of reciprocal exchange entails the risk of the other agent defecting with both goods. We examine the choreography for safe exchange between strangers, and identify the minimum requirement, which is a shared hold, either of an object, or the other party; we show that competing agents will settle on exchange as a local optimum in the space of payoffs. Truly safe exchanges are rarely seen in practice, even though unsafe exchange could mean that risk-averse agents might avoid such interactions. We show that an 'implicit' hold, whereby an actor believes that they could establish a hold if the other agent looked to be defecting, is sufficient to enable the simple swaps that are the hallmark of human interactions and presumably provide an acceptable trade-off between risk and convenience. We explicitly consider the particular case of purchasing, where money is one of the goods.

A changepoint prefilter for sound event detection in long-term bioacoustic recordings.

Long-term soundscape recordings are useful for a variety of applications, most notably in bioacoustics. However, the processing of such data is currently limited by the ability to efficiently and reliably detect the target sounds, which are often sparse and overshadowed by environmental noise. This paper proposes a sound detector based on changepoint theory applied to a wavelet representation of the sound. In contrast to existing methods, in this framework, theoretical analysis of the detector's performance and optimality for downstream applications can be made. The relevant statistical and algorithmic developments to support these claims are presented. The method is then tested on a real task of detecting two bird species in acoustic surveys. Compared to commonly used alternatives, the proposed method consistently produced a lower false alarm rate and improved the survey efficiency as measured by the precision of the inferred population size. Finally, it is demonstrated how the method can be combined with a simple classifier to detect cat sounds in domestic recordings, which is an example from the Detection and Classification of Acoustic Scenes and Events (DCASE) 2018 workshop. The resulting performance is comparable to the state-of-the-art deep learning models and requires much less training data.

Warfare induces post-marital residence change.

Post-marital residence is a sex-biased dispersal defined by the place where a newly-wed couple lives after marriage. Common choices for this practice include patrilocal residence, where the couple lives with the man's family, and matrilocal residence, where they live with the woman's family. Deviations from accepted practice typically invoke strong sanctions, but despite this pressure to conform to post-marital residence norms, residence states are unexpectedly dynamic over time. Theories have been proposed to explain the pressures, both internal and external, that drive these changes in post-marital residence state. Two of the most popular emphasize the importance of warfare, but are largely restricted to qualitative statements. Here, we develop an agent-based model that captures key features of these theories, with a particular focus on warfare. We show that warfare can change post-marital residence practices, but such change only propagates through a wider network of communities under a narrow set of conditions. Additional factors, potentially including a strong sex-bias in the division of labor, are required to induce change more widely. While warfare thus serves as an important trigger for residence change, multiple interacting forces appear to be necessary to shift communities between different post-marital residence states under most conditions.

Experimental test of birdcall detection by autonomous recorder units and by human observers using broadcast.

Autonomous recording units are now routinely used to monitor birdsong, starting to supplement and potentially replace human listening methods. However, to date there has been very little systematic comparison of human and machine detection ability. We present an experiment based on broadcast calls of nocturnal New Zealand birds in an area of natural forest. The soundscape was monitored by both novice and experienced humans performing a call count, and autonomous recording units. We match records of when calls were broadcast with detections by both humans and machines, and construct a hierarchical generalized linear model of the binary variable of correct detection or not, with a set of covariates about the call (distance, sound direction, relative altitude, and line of sight) and about the listener (age, experience, and gender). The results show that machines and humans have similar listening ability. Humans are more homogeneous in their recording of sounds, and this was not affected by their individual experience or characteristics. Humans were affected by trial and location, in particular one of the stations located in a small but deep valley. Despite recorders being affected significantly more than people by distance, altitude, and line of sight, their overall detection probability was higher. The specific location of recorders seems to be the most important factor determining what they record, and we suggest that for best results more than one recorder (or at least, microphone) is needed at each station to ensure all bird sounds of interest are captured.

The impact of environmental factors in birdsong acquisition using automated recorders.

The use of automatic acoustic recorders is becoming a principal method to survey birds in their natural habitats, as it is relatively noninvasive while still being informative. As with any other sound, birdsong degrades in amplitude, frequency, and temporal structure as it propagates to the recorder through the environment. Knowing how different birdsongs attenuate under different conditions is useful to, for example, develop protocols for deploying acoustic recorders and improve automated detection methods, an essential part of the research field that is becoming known as ecoacoustics. This article presents playback and recapture (record) experiments carried out under different environmental conditions using twenty bird calls from eleven New Zealand bird species in a native forest and an open area, answering five research questions: (1) How does birdsong attenuation differ between forest and open space? (2) What is the relationship between transmission height and birdsong attenuation? (3) How does frequency of birdsong impact the degradation of sound with distance? (4) Is birdsong attenuation different during the night compared to the day? and (5) what is the impact of wind on attenuation? Bird calls are complex sounds; therefore, we have chosen to use them rather than simple tones to ensure that this complexity is not missed in the analysis. The results demonstrate that birdsong transmission was significantly better in the forest than in the open site. During the night, the attenuation was at a minimum in both experimental sites. Transmission height affected the propagation of the songs of many species, particularly the flightless ones. The effect of wind was severe in the open site and attenuated lower frequencies. The reverberations due to reflective surfaces masked higher frequencies (8 kHz) in the forest even at moderate distances. The findings presented here can be applied to develop protocols for passive acoustic monitoring. Even though the attenuation can be generalized to frequency bands, the structure of the birdsong is also important. Selecting a reasonable sampling frequency avoids unnecessary data accumulation because higher frequencies attenuate more in the forest. Even at moderate distances, recorders capture significantly attenuated birdsong, and hence, automated analysis methods for field recordings need to be able to detect and recognize faint birdsong.

Birdsong Denoising Using Wavelets.

Automatic recording of birdsong is becoming the preferred way to monitor and quantify bird populations worldwide. Programmable recorders allow recordings to be obtained at all times of day and year for extended periods of time. Consequently, there is a critical need for robust automated birdsong recognition. One prominent obstacle to achieving this is low signal to noise ratio in unattended recordings. Field recordings are often very noisy: birdsong is only one component in a recording, which also includes noise from the environment (such as wind and rain), other animals (including insects), and human-related activities, as well as noise from the recorder itself. We describe a method of denoising using a combination of the wavelet packet decomposition and band-pass or low-pass filtering, and present experiments that demonstrate an order of magnitude improvement in noise reduction over natural noisy bird recordings.

Discrete differential geometry: the nonplanar quadrilateral mesh.

We consider the problem of constructing a discrete differential geometry defined on nonplanar quadrilateral meshes. Physical models on discrete nonflat spaces are of inherent interest, as well as being used in applications such as computation for electromagnetism, fluid mechanics, and image analysis. However, the majority of analysis has focused on triangulated meshes. We consider two approaches: discretizing the tensor calculus, and a discrete mesh version of differential forms. While these two approaches are equivalent in the continuum, we show that this is not true in the discrete case. Nevertheless, we show that it is possible to construct mesh versions of the Levi-Civita connection (and hence the tensorial covariant derivative and the associated covariant exterior derivative), the torsion, and the curvature. We show how discrete analogs of the usual vector integral theorems are constructed in such a way that the appropriate conservation laws hold exactly on the mesh, rather than only as approximations to the continuum limit. We demonstrate the success of our method by constructing a mesh version of classical electromagnetism and discuss how our formalism could be used to deal with other physical models, such as fluids.

Habituation revisited: an updated and revised description of the behavioral characteristics of habituation.

The most commonly cited descriptions of the behavioral characteristics of habituation come from two papers published almost 40 years ago [Groves, P. M., & Thompson, R. F. (1970). Habituation: A dual-process theory. Psychological Review, 77, 419-450; Thompson, R. F., & Spencer, W. A. (1966). Habituation: A model phenomenon for the study of neuronal substrates of behavior. Psychological Review, 73, 16-43]. In August 2007, the authors of this review, who study habituation in a wide range of species and paradigms, met to discuss their work on habituation and to revisit and refine the characteristics of habituation. This review offers a re-evaluation of the characteristics of habituation in light of these discussions. We made substantial changes to only a few of the characteristics, usually to add new information and expand upon the description rather than to substantially alter the original point. One additional characteristic, relating to long-term habituation, was added. This article thus provides a modern summary of the characteristics defining habituation, and can serve as a convenient primer for those whose research involves stimulus repetition.

Using habituation in machine learning.

Habituation, a decrement in response to a stimulus that is presented repeatedly without ill effect, can be identified in almost all animals. It can also be used in machine learning to provide a variety of different applications, such as novelty detection, recency encoding, and temporal signal pre-processing. This paper examines how habituation can be mathematically modelled, and discusses how well these models fit the revised characteristics of habituation. It then demonstrates how the models can be combined with neural networks in order to realise the various applications. Finally, some simple experimental results are presented that demonstrate the effectiveness of the methods.

A hamiltonian particle method for diffeomorphic image registration.

Diffeomorphic image registration, where images are aligned using diffeomorphic warps, is a popular subject for research in medical image analysis. We introduce a novel algorithm for computing diffeomorphic warps that solves the Euler equations on the diffeomorphism group explicitly, based on a discretisation of the Hamiltonian, rather than using an optimiser. The result is an algorithm that is many times faster than those considered previously.

A unified information-theoretic approach to groupwise non-rigid registration and model building.

The non-rigid registration of a group of images shares a common feature with building a model of a group of images: a dense, consistent correspondence across the group. Image registration aims to find the correspondence, while modelling requires it. This paper presents the theoretical framework required to unify these two areas, providing a groupwise registration algorithm, where the inherently groupwise model of the image data becomes an integral part of the registration process. The performance of this algorithm is evaluated by extending the concepts of generalisability and specificity from shape models to image models. This provides an independent metric for comparing registration algorithms of groups of images. Experimental results on MR data of brains for various pairwise and groupwise registration algorithms is presented, and demonstrates the feasibility of the combined registration/modelling framework, as well as providing quantitative evidence for the superiority of groupwise approaches to registration.

Clinical quality needs complex adaptive systems and machine learning.

The vast increase in clinical data has the potential to bring about large improvements in clinical quality and other aspects of healthcare delivery. However, such benefits do not come without cost. The analysis of such large datasets, particularly where the data may have to be merged from several sources and may be noisy and incomplete, is a challenging task. Furthermore, the introduction of clinical changes is a cyclical task, meaning that the processes under examination operate in an environment that is not static. We suggest that traditional methods of analysis are unsuitable for the task, and identify complexity theory and machine learning as areas that have the potential to facilitate the examination of clinical quality. By its nature the field of complex adaptive systems deals with environments that change because of the interactions that have occurred in the past. We draw parallels between health informatics and bioinformatics, which has already started to successfully use machine learning methods.

Constructing diffeomorphic representations for the groupwise analysis of nonrigid registrations of medical images.

Groupwise nonrigid registrations of medical images define dense correspondences across a set of images, defined by a continuous deformation field that relates each target image in the group to some reference image. These registrations can be automatic, or based on the interpolation of a set of user-defined landmarks, but in both cases, quantifying the normal and abnormal structural variation across the group of imaged structures implies analysis of the set of deformation fields. We contend that the choice of representation of the deformation fields is an integral part of this analysis. This paper presents methods for constructing a general class of multi-dimensional diffeomorphic representations of deformations. We demonstrate, for the particular case of the polyharmonic clamped-plate splines, that these representations are suitable for the description of deformations of medical images in both two and three dimensions, using a set of two-dimensional annotated MRI brain slices and a set of three-dimensional segmented hippocampi with optimized correspondences. The class of diffeomorphic representations also defines a non-Euclidean metric on the space of patterns, and, for the case of compactly supported deformations, on the corresponding diffeomorphism group. In an experimental study, we show that this non-Euclidean metric is superior to the usual ad hoc Euclidean metrics in that it enables more accurate classification of legal and illegal variations.

Constructing diffeomorphic representations of non-rigid registrations of medical images.

The analysis of deformation fields, such as those generated by non-rigid registration algorithms, is central to the quantification of normal and abnormal variation of structures in the registered images. The correct choice of representation is an integral part of this analysis. This paper presents methods for constructing a general class of multi-dimensional diffeomorphic representations of deformations. We demonstrate that these representations are suitable for the description of deformations of medical images in 2 and 3 dimensions. Furthermore, we show that the non-Euclidean metric inherent in this representation is superior to the usual ad hoc Euclidean metrics in that it enables more accurate classification of legal and illegal variations.

A self-organising network that grows when required.

The ability to grow extra nodes is a potentially useful facility for a self-organising neural network. A network that can add nodes into its map space can approximate the input space more accurately, and often more parsimoniously, than a network with predefined structure and size, such as the Self-Organising Map. In addition, a growing network can deal with dynamic input distributions. Most of the growing networks that have been proposed in the literature add new nodes to support the node that has accumulated the highest error during previous iterations or to support topological structures. This usually means that new nodes are added only when the number of iterations is an integer multiple of some pre-defined constant, A. This paper suggests a way in which the learning algorithm can add nodes whenever the network in its current state does not sufficiently match the input. In this way the network grows very quickly when new data is presented, but stops growing once the network has matched the data. This is particularly important when we consider dynamic data sets, where the distribution of inputs can change to a new regime after some time. We also demonstrate the preservation of neighbourhood relations in the data by the network. The new network is compared to an existing growing network, the Growing Neural Gas (GNG), on a artificial dataset, showing how the network deals with a change in input distribution after some time. Finally, the new network is applied to several novelty detection tasks and is compared with both the GNG and an unsupervised form of the Reduced Coulomb Energy network on a robotic inspection task and with a Support Vector Machine on two benchmark novelty detection tasks.