Modelling how cleaner fish approach an ephemeral reward task demonstrates a role for ecologically tuned chunking in the evolution of advanced cognition

What makes cognition “advanced” is an open and not precisely defined question. One perspective involves increasing the complexity of associative learning, from conditioning to learning sequences of events (“chaining”) to representing various cue combinations as “chunks.” Here we develop a weighted graph model to study the mechanism enabling chunking ability and the conditions for its evolution and success, based on the ecology of the cleaner fish Labroides dimidiatus. In some environments, cleaners must learn to serve visitor clients before resident clients, because a visitor leaves if not attended while a resident waits for service. This challenge has been captured in various versions of the ephemeral reward task, which has been proven difficult for a range of cognitively capable species. We show that chaining is the minimal requirement for solving this task in its common simplified laboratory format that involves repeated simultaneous exposure to an ephemeral and permanent food source. Adding ephemeral–ephemeral and permanent–permanent combinations, as cleaners face in the wild, requires individuals to have chunking abilities to solve the task. Importantly, chunking parameters need to be calibrated to ecological conditions in order to produce adaptive decisions. Thus, it is the fine-tuning of this ability, which may be the major target of selection during the evolution of advanced associative learning.

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A role for ecologically-tuned chunking in the evolution of advanced cognition demonstrated by modelling the cleaner fish market problem

10.1101/2021.05.30.446324 ◽

2021 ◽

Author(s):

Yosef Prat ◽

Redouan Bshary ◽

Arnon Lotem

Keyword(s):

Associative Learning ◽

Food Source ◽

Graph Model ◽

Weighted Graph ◽

Fine Tuning ◽

Simultaneous Exposure ◽

Cleaner Fish ◽

Labroides Dimidiatus ◽

In The Wild ◽

Major Target

What makes cognition 'advanced' is an open and not precisely defined question. One perspective involves increasing the complexity of associative learning, from conditioning to learning sequences of events ('chaining') to representing various cue combinations as 'chunks'. Here we develop a weighted-graph model to study the conditions for the evolution of chunking ability, based on the ecology of the cleaner fish Labroides dimidiatus. Cleaners must learn to serve visitor clients before resident clients, because a visitor leaves if not attended while a resident waits for service. This challenge has been captured in various versions of the ephemeral-reward task, which has been proven difficult for a range of cognitively capable species. We show that chaining is the minimal requirement for solving the laboratory task, that involves repeated simultaneous exposure to an ephemeral and permanent food source. Adding ephemeral-ephemeral and permanent-permanent combinations, as cleaners face in the wild, requires individuals to have chunking abilities to solve the task. Importantly, chunking parameters need to be calibrated to ecological conditions in order to produce adaptive decisions. Thus, it is the fine tuning of this ability which may be the major target of selection during the evolution of advanced associative learning.

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Listen2Cough

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies ◽

10.1145/3448124 ◽

2021 ◽

Vol 5 (1) ◽

pp. 1-22

Author(s):

Xuhai Xu ◽

Ebrahim Nemati ◽

Korosh Vatanparvar ◽

Viswam Nathan ◽

Tousif Ahmed ◽

...

Keyword(s):

Health Assessment ◽

Ground Truth ◽

Health Condition ◽

Fine Tuning ◽

Detection Model ◽

Assessment Tasks ◽

Augmentation Techniques ◽

In The Wild ◽

The Rich ◽

Lung Health

The prevalence of ubiquitous computing enables new opportunities for lung health monitoring and assessment. In the past few years, there have been extensive studies on cough detection using passively sensed audio signals. However, the generalizability of a cough detection model when applied to external datasets, especially in real-world implementation, is questionable and not explored adequately. Beyond detecting coughs, researchers have looked into how cough sounds can be used in assessing lung health. However, due to the challenges in collecting both cough sounds and lung health condition ground truth, previous studies have been hindered by the limited datasets. In this paper, we propose Listen2Cough to address these gaps. We first build an end-to-end deep learning architecture using public cough sound datasets to detect coughs within raw audio recordings. We employ a pre-trained MobileNet and integrate a number of augmentation techniques to improve the generalizability of our model. Without additional fine-tuning, our model is able to achieve an F1 score of 0.948 when tested against a new clean dataset, and 0.884 on another in-the-wild noisy dataset, leading to an advantage of 5.8% and 8.4% on average over the best baseline model, respectively. Then, to mitigate the issue of limited lung health data, we propose to transform the cough detection task to lung health assessment tasks so that the rich cough data can be leveraged. Our hypothesis is that these tasks extract and utilize similar effective representation from cough sounds. We embed the cough detection model into a multi-instance learning framework with the attention mechanism and further tune the model for lung health assessment tasks. Our final model achieves an F1-score of 0.912 on healthy v.s. unhealthy, 0.870 on obstructive v.s. non-obstructive, and 0.813 on COPD v.s. asthma classification, outperforming the baseline by 10.7%, 6.3%, and 3.7%, respectively. Moreover, the weight value in the attention layer can be used to identify important coughs highly correlated with lung health, which can potentially provide interpretability for expert diagnosis in the future.

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Train Fast While Reducing False Positives: Improving Animal Classification Performance Using Convolutional Neural Networks

Geomatics ◽

10.3390/geomatics1010004 ◽

2021 ◽

Vol 1 (1) ◽

pp. 34-49

Author(s):

Mael Moreni ◽

Jerome Theau ◽

Samuel Foucher

Keyword(s):

False Positive ◽

Classification Performance ◽

Large Datasets ◽

False Positives ◽

Fine Tuning ◽

Training Time ◽

In The Wild ◽

Test Sets ◽

High Level ◽

Time Decrease

The combination of unmanned aerial vehicles (UAV) with deep learning models has the capacity to replace manned aircrafts for wildlife surveys. However, the scarcity of animals in the wild often leads to highly unbalanced, large datasets for which even a good detection method can return a large amount of false detections. Our objectives in this paper were to design a training method that would reduce training time, decrease the number of false positives and alleviate the fine-tuning effort of an image classifier in a context of animal surveys. We acquired two highly unbalanced datasets of deer images with a UAV and trained a Resnet-18 classifier using hard-negative mining and a series of recent techniques. Our method achieved sub-decimal false positive rates on two test sets (1 false positive per 19,162 and 213,312 negatives respectively), while training on small but relevant fractions of the data. The resulting training times were therefore significantly shorter than they would have been using the whole datasets. This high level of efficiency was achieved with little tuning effort and using simple techniques. We believe this parsimonious approach to dealing with highly unbalanced, large datasets could be particularly useful to projects with either limited resources or extremely large datasets.

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A Large Margin Learning Method for Matching Images of Natural Objects with Different Dimensions

Advances in Environmental Engineering and Green Technologies - Computer Vision and Pattern Recognition in Environmental Informatics ◽

10.4018/978-1-4666-9435-4.ch015 ◽

2015 ◽

pp. 323-341

Author(s):

Haoyi Zhou ◽

Jun Zhou ◽

Haichuan Yang ◽

Cheng Yan ◽

Xiao Bai ◽

...

Keyword(s):

Feature Matching ◽

Hyperspectral Image ◽

Graph Matching ◽

Graph Model ◽

Image Data ◽

Weighted Graph ◽

Hyperspectral Data ◽

Hyperspectral Images ◽

Natural Objects ◽

Different Dimensions

Imaging devices are of increasing use in environmental research requiring an urgent need to deal with such issues as image data, feature matching over different dimensions. Among them, matching hyperspectral image with other types of images is challenging due to the high dimensional nature of hyperspectral data. This chapter addresses this problem by investigating structured support vector machines to construct and learn a graph-based model for each type of image. The graph model incorporates both low-level features and stable correspondences within images. The inherent characteristics are depicted by using a graph matching algorithm on extracted weighted graph models. The effectiveness of this method is demonstrated through experiments on matching hyperspectral images to RGB images, and hyperspectral images with different dimensions on images of natural objects.

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Estimating Parameters of a Directed Weighted Graph Model with Beta-Distributed Edge-Weights

Journal of Mathematical Sciences ◽

10.1007/s10958-019-04186-0 ◽

2019 ◽

Vol 237 (5) ◽

pp. 611-620

Author(s):

M. Bolla ◽

J. Mala ◽

A. Elbanna

Keyword(s):

Graph Model ◽

Weighted Graph

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Replication Properties of Clade A/C Chimeric Feline Immunodeficiency Viruses and Evaluation of Infection Kinetics in the Domestic Cat

Journal of Virology ◽

10.1128/jvi.00337-08 ◽

2008 ◽

Vol 82 (16) ◽

pp. 7953-7963 ◽

Cited By ~ 22

Author(s):

Sohela de Rozìeres ◽

Jesse Thompson ◽

Magnus Sundstrom ◽

Julia Gruber ◽

Debora S. Stump ◽

...

Keyword(s):

Ex Vivo ◽

Domestic Cat ◽

Fine Tuning ◽

Surface Glycoprotein ◽

Wild Type ◽

Coding Region ◽

Mild Disease ◽

Clade C ◽

In The Wild

ABSTRACT Feline immunodeficiency virus (FIV) causes progressive immunodeficiency in domestic cats, with clinical course dependent on virus strain. For example, clade A FIV-PPR is predominantly neurotropic and causes a mild disease in the periphery, whereas clade C FIV-C36 causes fulminant disease with CD4+ T-cell depletion and neutropenia but no significant pathology in the central nervous system. In order to map pathogenic determinants, chimeric viruses were prepared between FIV-C36 and FIV-PPR, with reciprocal exchanges involving (i) the 3′ halves of the viruses, including the Vif, OrfA, and Env genes; (ii) the 5′ end extending from the 5′ long terminal repeat (LTR) to the beginning of the capsid (CA)-coding region; and (iii) the 3′ LTR and Rev2-coding regions. Ex vivo replication rates and in vivo replication and pathologies were then assessed and compared to those of the parental viruses. The results show that FIV-C36 replicates ex vivo and in vivo to levels approximately 20-fold greater than those of FIV-PPR. None of the chimeric FIVs recapitulated the replication rate of FIV-C36, although most replicated to levels similar to those of FIV-PPR. The rates of chloramphenicol acetyltransferase gene transcription driven by the FIV-C36 and FIV-PPR LTRs were identical. Furthermore, the ratios of surface glycoprotein (SU) to capsid protein (CA) in the released particles were essentially the same in the wild-type and chimeric FIVs. Tests were performed in vivo on the wild-type FIVs and chimeras carrying the 3′ half of FIV-C36 or the 3′ LTR and Rev2 regions of FIV-C36 on the PPR background. Both chimeras were infectious in vivo, although replication levels were lower than for the parental viruses. The chimera carrying the 3′ half of FIV-C36 demonstrated an intermediate disease course with a delayed peak viral load but ultimately resulted in significant reductions in neutrophil and CD4+ T cells, suggesting potential adaptation in vivo. Taken together, the findings suggest that the rapid-growth phenotype and pathogenicity of FIV-C36 are the result of evolutionary fine tuning throughout the viral genome, rather than being properties of any one constituent.

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