Semantic segmentation of plant roots from RGB (mini-) rhizotron images-generalisation potential and false positives of established methods and advanced deep-learning models

Background Manual analysis of (mini-)rhizotron (MR) images is tedious. Several methods have been proposed for semantic root segmentation based on homogeneous, single-source MR datasets. Recent advances in deep learning (DL) have enabled automated feature extraction, but comparisons of segmentation accuracy, false positives and transferability are virtually lacking. Here we compare six state-of-the-art methods and propose two improved DL models for semantic root segmentation using a large MR dataset with and without augmented data. We determine the performance of the methods on a homogeneous maize dataset, and a mixed dataset of > 8 species (mixtures), 6 soil types and 4 imaging systems. The generalisation potential of the derived DL models is determined on a distinct, unseen dataset. Results The best performance was achieved by the U-Net models; the more complex the encoder the better the accuracy and generalisation of the model. The heterogeneous mixed MR dataset was a particularly challenging for the non-U-Net techniques. Data augmentation enhanced model performance. We demonstrated the improved performance of deep meta-architectures and feature extractors, and a reduction in the number of false positives. Conclusions Although correction factors are still required to match human labelled root lengths, neural network architectures greatly reduce the time required to compute the root length. The more complex architectures illustrate how future improvements in root segmentation within MR images can be achieved, particularly reaching higher segmentation accuracies and model generalisation when analysing real-world datasets with artefacts-limiting the need for model retraining.O

Towards empathic deep q-learning

As reinforcement learning (RL) scales to solve increasingly complex tasks, interest continues to grow in the fields of AI safety and machine ethics. As a contribution to these fields, this paper introduces an extension to Deep Q-Networks (DQNs), called Empathic DQN, that is loosely inspired both by empathy and the golden rule ("Do unto others as you would have them do unto you"). Empathic DQN aims to help mitigate negative side effects to other agents resulting from myopic goal-directed behavior. We assume a setting where a learning agent coexists with other independent agents (who receive unknown rewards), where some types of reward (e.g. negative rewards from physical harm) may generalize across agents. Empathic DQN combines the typical (self-centered) value with the estimated value of other agents, by imagining (by its own standards) the value of it being in the other's situation (by considering constructed states where both agents are swapped). Proof-of-concept results in two gridworld environments highlight the approach's potential to decrease collateral harms. While extending Empathic DQN to complex environments is non-trivial, we believe that this first step highlights the potential of bridge-work between machine ethics and RL to contribute useful priors for norm-abiding RL agents.Comment: To be presented as a poster at the IJCAI-19 AI Safety Worksho

Neural additive vector autoregression models for causal discovery in time series

Causal structure discovery in complex dynamical systems is an important challenge for many scientific domains. Although data from (interventional) experiments is usually limited, large amounts of observational time series data sets are usually available. Current methods that learn causal structure from time series often assume linear relationships. Hence, they may fail in realistic settings that contain nonlinear relations between the variables. We propose Neural Additive Vector Autoregression (NAVAR) models, a neural approach to causal structure learning that can discover nonlinear relationships. We train deep neural networks that extract the (additive) Granger causal influences from the time evolution in multi-variate time series. The method achieves state-of-the-art results on various benchmark data sets for causal discovery, while providing clear interpretations of the mapped causal relations.Comment: 11 pages, 5 figure

The joint mission (Universität zu Köln and University of Pisa) at Zawyet Sultan: report of the archaeological surveys at the site (2015, 2017, 2019) and the current related research projects

In 2015 the joint archaeological mission led by Richard Bussmann of the University of Cologne, and by Gianluca Miniaci of the University of Pisa, with the cooperation of the Egyptian Ministry of Tourism and Antiquities, began an archaeological project at the site of Zawyet Sultan (ancient Hebenu), located in Middle Egypt, about 8 kilometres south of the modern city of el-Minya. The core of the archaeological area includes the remains of a small early Old Kingdom step pyramid, extensive debris of a Greco-Roman settlement, an enclosure wall, a fragmentary stone ramp dating back to the New Kingdom/Roman Period, and rows of Old and New Kingdom rock-cut tombs belonging to the local and provincial elite. The main aims of the project are to outline the ancient topography of the site in order to better understand its spatial organisation and the interaction between the pyramid, the settlement, and cemeteries throughout a long period of time (ca. 3500 BC-900 AD). This article presents the preliminary results of the fieldwork carried out at the site in 2015, 2017, 2019 and the still ongoing archive and museum investigation related to the material coming from the past excavations at the site of Zawyet Sultan

Zawiyet Sultan, Middle Egypt. A Pottery Survey

The article describes briefly the pottery collected during the 2015 survey mission to Zawyet Sultan in Middle Egypt. It concerns pottery from the early Old Kingdom, the late Old Kingdom, the New Kingdom, the Roman period and the early Islamic period.status: publishe

Zawyet Sultan, Middle Egypt. A Pottery Survey

The article describes briefly the pottery collected during the 2015 survey mission to Zawyet Sultan in Middle Egypt. It concerns pottery from the early Old Kingdom, the late Old Kingdom, the New Kingdom, the Roman period and the early Islamic period

Semantic segmentation of plant roots from RGB (mini-) rhizotron images-generalisation potential and false positives of established methods and advanced deep-learning models

Abstract: Background Manual analysis of (mini-)rhizotron (MR) images is tedious. Several methods have been proposed for semantic root segmentation based on homogeneous, single-source MR datasets. Recent advances in deep learning (DL) have enabled automated feature extraction, but comparisons of segmentation accuracy, false positives and transferability are virtually lacking. Here we compare six state-of-the-art methods and propose two improved DL models for semantic root segmentation using a large MR dataset with and without augmented data. We determine the performance of the methods on a homogeneous maize dataset, and a mixed dataset of > 8 species (mixtures), 6 soil types and 4 imaging systems. The generalisation potential of the derived DL models is determined on a distinct, unseen dataset.Results The best performance was achieved by the U-Net models; the more complex the encoder the better the accuracy and generalisation of the model. The heterogeneous mixed MR dataset was a particularly challenging for the non-U-Net techniques. Data augmentation enhanced model performance. We demonstrated the improved performance of deep meta-architectures and feature extractors, and a reduction in the number of false positives.Conclusions Although correction factors are still required to match human labelled root lengths, neural network architectures greatly reduce the time required to compute the root length. The more complex architectures illustrate how future improvements in root segmentation within MR images can be achieved, particularly reaching higher segmentation accuracies and model generalisation when analysing real-world datasets with artefacts-limiting the need for model retraining

Benefits of Social Learning in Physical Robots

Robot-to-robot learning, a specific case of social learning in robotics, enables the ability to transfer robot controllers directly from one robot to another. Previous studies showed that the exchange of controller information can increase learning speed and performance. However, most of these studies have been performed in simulation, where robots are identical. Therefore, the results do not necessarily transfer to a real environment, where each robot is unique per definition due to the random differences in hardware. In this paper, we investigate the effect of exchanging controller information, on top of individual learning, in a group of Thymio II robots for two tasks: obstacle avoidance and foraging. The controllers of the robots are neural networks that evolve using a modified version of the state-of-the-art NEAT algorithm, called cNEAT, which allows the conversion of innovations numbers from other robots. This paper shows that robot-to-robot learning seems to at least parallelise the search, reducing wall clock time. Additionally, controllers are less complex, resulting in a smaller search space