Neural networks play an important role in crop-weed classification have high accuracy more than 95%. Manually choosing models and fine-tuning are laborious, yet it is indispensable in most traditional practices and researches. Moreover, classic training metric are not thoroughly compatible with farming tasks, that a model still have a noticeable chance of miss classifying crop to weed while it reach higher accuracy even more than 99%. In this paper we demonstrate a methodology of weed-crop classification based on autonomous models choosing and ensemble that could make models choosing and tunning automatically, and improve the prediction with high accuracy(>99% for both data set) in specific class with low risk in incorrect predicting.

We propose a new approach for learning contextualised cross-lingual word embeddings based only on a small parallel corpus (e.g. a few hundred sentence pairs). Our method obtains word embeddings via an LSTM-based encoder-decoder model that performs bidirectional translation and reconstruction of the input sentence. Through sharing model parameters among different languages, our model jointly trains the word embeddings in a common multilingual space. We also propose a simple method to combine word and subword embeddings to make use of orthographic similarities across different languages. We base our experiments on real-world data from endangered languages, namely Yongning Na, Shipibo-Konibo and Griko. Our experiments on bilingual lexicon induction and word alignment tasks show that our model outperforms existing methods by a large margin for most language pairs. These results demonstrate that, contrary to common belief, an encoder-decoder translation model is beneficial for learning cross-lingual representations, even in extremely low-resource scenarios.

In the realm of planning, this approach was pioneered by Howey, Long, and Fox As witnessed by the different planning competitions (Long who developed VAL (Howey, Long, and Fox 2004) that, 2000; Coles et al. 2012; Vallati et al. 2015), planning algorithms given a planning problem and potential solution, certifies and systems are becoming more and more scalable that the solution actually solves the given problem. Also, and efficient, which makes them suited for more realistic certifying unsolvability for planning was tackled by Eriksson, applications. Given that many applications of planning Röger, and Helmert (2017) who provided unsolvability are safety-critical, increasing the trustworthiness of certificates and checkers for state-space search algorithms planning algorithms and systems--i.e. the likelihood that and by Eriksson and Helmert (2020) for property they compute correct results--could be instrumental in their directed SATbased planning.

General value functions (GVFs) in the reinforcement learning (RL) literature are long-term predictive summaries of the outcomes of agents following specific policies in the environment. Affordances as perceived valences of action possibilities may be cast into predicted policy-relative goodness and modelled as GVFs. A systematic explication of this connection shows that GVFs and especially their deep learning embodiments (1) realize affordance prediction as a form of direct perception, (2) illuminate the fundamental connection between action and perception in affordance, and (3) offer a scalable way to learn affordances using RL methods. Through a comprehensive review of existing literature on recent successes of GVF applications in robotics, rehabilitation, industrial automation, and autonomous driving, we demonstrate that GVFs provide the right framework for learning affordances in real-world applications. In addition, we highlight a few new avenues of research opened up by the perspective of "affordance as GVF", including using GVFs for orchestrating complex behaviors.

The Earth is losing forests at an alarming rate. The United Nations Food and Agriculture Organization estimates that 420 million hectares of forest have been lost to agricultural use (largely cattle ranching, soya bean and oil palm farming) since 1990. Between 2015 and 2020, some 10 million hectares were destroyed each year. The Amazon rainforest, for example, lost an area the size of Yellowstone (3,769 square miles) in 2019, and saw deforestation rates spike 30 percent to their highest point in a decade. What's more, Climate change-induced wildfires, as we've seen recently in Australia and in California, have been especially destructive.

Salah Sukkarieh is Professor of Robotics and Intelligent Systems at the University of Sydney, and Director of Research and Innovation at the Australian Centre for Field Robotics. He has worked on autonomous systems for ports, mines, aerospace, and, most recently, agriculture. He recalls that when he started working on drone technology there were not many aerospace companies in Australia working on drones, and those that were were not interested in drones for agriculture or the environment as the business case didn't stack up financially. Australia's size and the remoteness of many rural areas have also been deterrents. There is strong interest from the agriculture industry in the use of robotics and automation to support farmers, and he is surprised by the number of students who are interested in working on these projects.

Mining is a traditionally analogue business. After all, the industry's symbol worldwide is a hammer and pick. Yet, despite the sector's antiquated reputation, some major mining companies are taking a progressive stance and proving digitisation and automation can achieve much better operational outcomes. Known as Mine 4.0, the industry is seeing digital transformation creep into everything from trucks, drills and trains to back-office processes, such as procurement and supply chain logistics. Miners have very little control over the revenue side of their business, as the global commodities crash of 2014 to 2015, when prices plunged by more than 30 per cent, and indeed the coronavirus epidemic demonstrate.

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Credit scoring is a rapidly expanding analytical technique used by banks and other financial institutions. Academic studies on credit scoring provide a range of classification techniques used to differentiate between good and bad borrowers. The main contribution of this paper is to introduce a new method for credit scoring based on Gaussian Mixture Models. Our algorithm classifies consumers into groups which are labeled as positive or negative. Labels are estimated according to the probability associated with each class. We apply our model with real world databases from Australia, Japan, and Germany. Numerical results show that not only our model's performance is comparable to others, but also its flexibility avoids over-fitting even in the absence of standard cross validation techniques. The framework developed by this paper can provide a computationally efficient and powerful tool for assessment of consumer default risk in related financial institutions.

Measure transport underpins several recent algorithms for posterior approximation in the Bayesian context, wherein a transport map is sought to minimise the Kullback--Leibler divergence (KLD) from the posterior to the approximation. The KLD is a strong mode of convergence, requiring absolute continuity of measures and placing restrictions on which transport maps can be permitted. Here we propose to minimise a kernel Stein discrepancy (KSD) instead, requiring only that the set of transport maps is dense in an $L^2$ sense and demonstrating how this condition can be validated. The consistency of the associated posterior approximation is established and empirical results suggest that KSD is competitive and more flexible alternative to KLD for measure transport.