Search results for "Learning"
showing 10 items of 6669 documents
Predators' consumption of unpalatable prey does not vary as a function of bitter taste perception
2020
Many prey species contain defensive chemicals that are described as tasting bitter. Bitter taste perception is, therefore, assumed to be important when predators are learning about prey defenses. However, it is not known how individuals differ in their response to bitter taste, and how this influences their foraging decisions. We conducted taste perception assays in which wild-caught great tits (Parus major) were given water with increasing concentrations of bitter-tasting chloroquine diphosphate until they showed an aversive response to bitter taste. This response threshold was found to vary considerably among individuals, ranging from chloroquine concentrations of 0.01 mmol/L to 8 mmol/L.…
Novel simple templates for reproducible positioning of skin applicators in brachytherapy.
2016
Purpose : Esteya and Valencia surface applicators are designed to treat skin tumors using brachytherapy. In clinical practice, in order to avoid errors that may affect the treatment outcome, there are two issues that need to be carefully addressed. First, the selected applicator for the treatment should provide adequate margin for the target, and second, the applicator has to be precisely positioned before each treatment fraction. In this work, we describe the development and use of a new acrylic templates named Template La Fe-ITIC. They have been designed specifically to help the clinical user in the selection of the correct applicator, and to assist the medical staff in reproducing the po…
Simple learning rules to cope with changing environments
2008
10 pages; International audience; We consider an agent that must choose repeatedly among several actions. Each action has a certain probability of giving the agent an energy reward, and costs may be associated with switching between actions. The agent does not know which action has the highest reward probability, and the probabilities change randomly over time. We study two learning rules that have been widely used to model decision-making processes in animals-one deterministic and the other stochastic. In particular, we examine the influence of the rules' 'learning rate' on the agent's energy gain. We compare the performance of each rule with the best performance attainable when the agent …
Thompson Sampling Based Active Learning in Probabilistic Programs with Application to Travel Time Estimation
2019
The pertinent problem of Traveling Time Estimation (TTE) is to estimate the travel time, given a start location and a destination, solely based on the coordinates of the points under consideration. This is typically solved by fitting a function based on a sequence of observations. However, it can be expensive or slow to obtain labeled data or measurements to calibrate the estimation function. Active Learning tries to alleviate this problem by actively selecting samples that minimize the total number of samples needed to do accurate inference. Probabilistic Programming Languages (PPL) give us the opportunities to apply powerful Bayesian inference to model problems that involve uncertainties.…
A Methodology to Derive Global Maps of Leaf Traits Using Remote Sensing and Climate Data
2018
This paper introduces a modular processing chain to derive global high-resolution maps of leaf traits. In particular, we present global maps at 500 m resolution of specific leaf area, leaf dry matter content, leaf nitrogen and phosphorus content per dry mass, and leaf nitrogen/phosphorus ratio. The processing chain exploits machine learning techniques along with optical remote sensing data (MODIS/Landsat) and climate data for gap filling and up-scaling of in-situ measured leaf traits. The chain first uses random forests regression with surrogates to fill gaps in the database (> 45% of missing entries) and maximizes the global representativeness of the trait dataset. Plant species are then a…
Temperate Fish Detection and Classification: a Deep Learning based Approach
2021
A wide range of applications in marine ecology extensively uses underwater cameras. Still, to efficiently process the vast amount of data generated, we need to develop tools that can automatically detect and recognize species captured on film. Classifying fish species from videos and images in natural environments can be challenging because of noise and variation in illumination and the surrounding habitat. In this paper, we propose a two-step deep learning approach for the detection and classification of temperate fishes without pre-filtering. The first step is to detect each single fish in an image, independent of species and sex. For this purpose, we employ the You Only Look Once (YOLO) …
Foraging Bumblebees Selectively Attend to Other Types of Bees Based on Their Reward-Predictive Value.
2020
Using social information can be an efficient strategy for learning in a new environment while reducing the risks associated with trial-and-error learning. Whereas social information from conspecifics has long been assumed to be preferentially attended by animals, heterospecifics can also provide relevant information. Because different species may vary in their informative value, using heterospecific social information indiscriminately can be ineffective and even detrimental. Here, we evaluated how selective use of social information might arise at a proximate level in bumblebees (Bombus terrestris) as a result of experience with demonstrators differing in their visual appearance and in thei…
Machine learning predictions of trophic status indicators and plankton dynamic in coastal lagoons
2018
Abstract Multivariate trophic indices provide an efficient way to assess and classify the eutrophication level and ecological status of a given water body, but their computation requires the availability of experimental information on many parameters, including biological data, that might not always be available. Here we show that machine learning techniques – once trained against a full data set – can be used to infer plankton biomass information from chemical and physical parameter only, so that trophic index can then be computed without using additional biological data. More specifically, we reconstruct plankton information from chemical and physical data, and this information together w…
Unsupervised Classification of Acoustic Echoes from Two Krill Species in the Southern Ocean (Ross Sea)
2021
This work presents a computational methodology able to automatically classify the echoes of two krill species recorded in the Ross sea employing scientific echo-sounder at three different frequencies (38, 120 and 200 kHz). The goal of classifying the gregarious species represents a time-consuming task and is accomplished by using differences and/or thresholds estimated on the energy features of the insonified targets. Conversely, our methodology takes into account energy, morphological and depth features of echo data, acquired at different frequencies. Internal validation indices of clustering were used to verify the ability of the clustering in recognizing the correct number of species. Th…
LTSER platforms as a place-based transdisciplinary research infrastructure: learning landscape approach through evaluation
2019
Context: Place-based transdisciplinary research involves multiple academic disciplines and non-academic actors. Long-Term Socio-Ecological Research (LTSER) platform is one concept with ~ 80 initiatives globally. Objectives: As an exercise in learning through evaluation we audited (1) the siting, construction and maintenance of individual LTSER platforms, and (2) them as a distributed infrastructure for place-based transdisciplinary research with focus on the European continent. Methods: First, we defined a normative model for ideal performance at both platform and network levels. Second, four surveys were sent out to the 67 self-reported LTSER platforms officially listed at the end of 2016.…