In 10 years, the circular economy will be the only economy, replacing wasteful linear economies, predicts Gartner. According to Gartner, circular economic business models encourage continuous reuse of materials to minimise waste and the demand for additional natural resource consumption. "The circular economy creates an ecosystem of materials," notes Sarah Watt, senior director analyst at Gartner. "What was previously viewed as waste now has value. However those ecosystems are complex, and include many interdependencies and feedback loops."

Moritz Lipp is a Ph.D. candidate at Graz University of Technology, Flanders, Austria. Michael Schwarz is a postdoctoral researcher at Graz University of Technology, Flanders, Austria. Daniel Gruss is an assistant professor at Graz University of Technology, Flanders, Austria. Thomas Prescher is a chief architect at Cyberus Technology GmbH, Dresden, Germany. Werner Haas is the Chief Technology Officer at Cyberus Technology GmbH, Dresden, Germany.

In October 2019, an Amazon employee in Melbourne, Australia, bumped into another person while cycling on the road. As she was assuring that person that she would help, she realised he was deaf and mute and had no idea what she was saying. That awkward situation could have been avoided if assistive technology was on hand to facilitate communication between the two parties. Following the incident, a team led by Santanu Dutt, head of technology for Southeast Asia at Amazon Web Services, got down to work. Within 10 days or so, Dutt's team had built a machine learning model that was trained on sign languages.

Entity alignment (EA) is to discover equivalent entities in knowledge graphs (KGs), which bridges heterogeneous sources of information and facilitates the integration of knowledge. Existing EA solutions mainly rely on structural information to align entities, typically through KG embedding. Nonetheless, in real-life KGs, only a few entities are densely connected to others, and the rest majority possess rather sparse neighborhood structure. We refer to the latter as long-tail entities, and observe that such phenomenon arguably limits the use of structural information for EA. To mitigate the issue, we revisit and investigate into the conventional EA pipeline in pursuit of elegant performance. For pre-alignment, we propose to amplify long-tail entities, which are of relatively weak structural information, with entity name information that is generally available (but overlooked) in the form of concatenated power mean word embeddings. For alignment, under a novel complementary framework of consolidating structural and name signals, we identify entity's degree as important guidance to effectively fuse two different sources of information. To this end, a degree-aware co-attention network is conceived, which dynamically adjusts the significance of features in a degree-aware manner. For post-alignment, we propose to complement original KGs with facts from their counterparts by using confident EA results as anchors via iterative training. Comprehensive experimental evaluations validate the superiority of our proposed techniques.

Training deep reinforcement learning agents on environments with multiple levels / scenes / conditions from the same task, has become essential for many applications aiming to achieve generalization and domain transfer from simulation to the real world [1,2]. While such a strategy is helpful with generalization, the use of multiple scenes significantly increases the variance of samples collected for policy gradient computations. Current methods continue to view this collection of scenes as a single Markov Decision Process (MDP) with a common value function; however, we argue that it is better to treat the collection as a single environment with multiple underlying MDPs. To this end, we propose a dynamic value estimation (DVE) technique for these multiple-MDP environments, motivated by the clustering effect observed in the value function distribution across different scenes. The resulting agent is able to learn a more accurate and scene-specific value function estimate (and hence the advantage function), leading to a lower sample variance. Our proposed approach is simple to accommodate with several existing implementations (like PPO, A3C) and results in consistent improvements for a range of ProcGen environments and the AI2-THOR framework based visual navigation task.

Land cover maps are a vital input variable to many types of environmental research and management. While they can be produced automatically by machine learning techniques, these techniques require substantial training data to achieve high levels of accuracy, which are not always available. One technique researchers use when labelled training data are scarce is domain adaptation (DA) -- where data from an alternate region, known as the source domain, are used to train a classifier and this model is adapted to map the study region, or target domain. The scenario we address in this paper is known as semi-supervised DA, where some labelled samples are available in the target domain. In this paper we present Sourcerer, a Bayesian-inspired, deep learning-based, semi-supervised DA technique for producing land cover maps from SITS data. The technique takes a convolutional neural network trained on a source domain and then trains further on the available target domain with a novel regularizer applied to the model weights. The regularizer adjusts the degree to which the model is modified to fit the target data, limiting the degree of change when the target data are few in number and increasing it as target data quantity increases. Our experiments on Sentinel-2 time series images compare Sourcerer with two state-of-the-art semi-supervised domain adaptation techniques and four baseline models. We show that on two different source-target domain pairings Sourcerer outperforms all other methods for any quantity of labelled target data available. In fact, the results on the more difficult target domain show that the starting accuracy of Sourcerer (when no labelled target data are available), 74.2%, is greater than the next-best state-of-the-art method trained on 20,000 labelled target instances.

Will self-driving cars be able to cope with highly dangerous roads? Let's talk about dangerous roads. In a moment, I'll provide you with a recently published list of the presumed Top Ten most dangerous roads in the world. For some of you, the odds are that you'll be happy that you've never had a cause to try and traverse these bad-to-the-bone roads, while others of you are probably going to put these alarming roads on your bucket list of places you have to go and give a whirl someday. Do you prefer roads that are calm, easy to navigate, and present little or no qualms?

Peter Souza'a employer trains adults on the autism spectrum for tasks and roles in IT. Years ago, Michael Field-house had a dinner party and friends attended with their young son Andrew, who is autistic, non-verbal, and low-functioning. At one point, Fieldhouse noticed Andrew, who was five- or six-years-old at the time, outside dropping pebbles into an urn in a Japanese garden. "I was curious about that and I started timing him," recalls Fieldhouse. "I noted there were perfect intervals between every stone. He did that for at least an hour."

Multi-view alignment, achieving one-to-one correspondence of multi-view inputs, is critical in many real-world multi-view applications, especially for cross-view data analysis problems. Recently, an increasing number of works study this alignment problem with Canonical Correlation Analysis (CCA). However, existing CCA models are prone to misalign the multiple views due to either the neglect of uncertainty or the inconsistent encoding of the multiple views. To tackle these two issues, this paper studies multi-view alignment from the Bayesian perspective. Delving into the impairments of inconsistent encodings, we propose to recover correspondence of the multi-view inputs by matching the marginalization of the joint distribution of multi-view random variables under different forms of factorization. To realize our design, we present Adversarial CCA (ACCA) which achieves consistent latent encodings by matching the marginalized latent encodings through the adversarial training paradigm. Our analysis based on conditional mutual information reveals that ACCA is flexible for handling implicit distributions. Extensive experiments on correlation analysis and cross-view generation under noisy input settings demonstrate the superiority of our model.

Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic. A basic assumption behind multivariate time series forecasting is that its variables depend on one another but, upon looking closely, it is fair to say that existing methods fail to fully exploit latent spatial dependencies between pairs of variables. In recent years, meanwhile, graph neural networks (GNNs) have shown high capability in handling relational dependencies. GNNs require well-defined graph structures for information propagation which means they cannot be applied directly for multivariate time series where the dependencies are not known in advance. In this paper, we propose a general graph neural network framework designed specifically for multivariate time series data. Our approach automatically extracts the uni-directed relations among variables through a graph learning module, into which external knowledge like variable attributes can be easily integrated. A novel mix-hop propagation layer and a dilated inception layer are further proposed to capture the spatial and temporal dependencies within the time series. The graph learning, graph convolution, and temporal convolution modules are jointly learned in an end-to-end framework. Experimental results show that our proposed model outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets and achieves on-par performance with other approaches on two traffic datasets which provide extra structural information.