Linking authors of short-text contents has important usages in many applications, including Named Entity Recognition (NER) and human community detection. However, certain challenges lie ahead. Firstly, the input short-text contents are noisy, ambiguous, and do not follow the grammatical rules. Secondly, traditional text mining methods fail to effectively extract concepts through words and phrases. Thirdly, the textual contents are temporally skewed, which can affect the semantic understanding by multiple time facets. Finally, using the complementary knowledge-bases makes the results biased to the content of the external database and deviates the understanding and interpretation away from the real nature of the given short text corpus. To overcome these challenges, we devise a neural network-based temporal-textual framework that generates the tightly connected author subgraphs from microblog short-text contents. Our approach, on the one hand, computes the relevance score (edge weight) between the authors through considering a portmanteau of contents and concepts, and on the other hand, employs a stack-wise graph cutting algorithm to extract the communities of the related authors. Experimental results show that compared to other knowledge-centered competitors, our multi-aspect vector space model can achieve a higher performance in linking short-text authors. Additionally, given the author linking task, the more comprehensive the dataset is, the higher the significance of the extracted concepts will be.

Continual lifelong learning is essential to many applications. In this paper, we propose a simple but effective approach to continual deep learning. Our approach leverages the principles of deep model compression, critical weights selection, and progressive networks expansion. By enforcing their integration in an iterative manner, we introduce an incremental learning method that is scalable to the number of sequential tasks in a continual learning process. Our approach is easy to implement and owns several favorable characteristics. First, it can avoid forgetting (i.e., learn new tasks while remembering all previous tasks). Second, it allows model expansion but can maintain the model compactness when handling sequential tasks. Besides, through our compaction and selection/expansion mechanism, we show that the knowledge accumulated through learning previous tasks is helpful to build a better model for the new tasks compared to training the models independently with tasks. Experimental results show that our approach can incrementally learn a deep model tackling multiple tasks without forgetting, while the model compactness is maintained with the performance more satisfiable than individual task training.

This paper explores the use of a novel form of Hierarchical Graph Neurons (HGN) for in-operation behaviour selection in a swarm of robotic agents. This new HGN is called Robotic-HGN (R-HGN), as it matches robot environment observations to environment labels via fusion of match probabilities from both temporal and intra-swarm collections. This approach is novel for HGN as it addresses robotic observations being pseudo-continuous numbers, rather than categorical values. Additionally, the proposed approach is memory and computation-power conservative and thus is acceptable for use in mobile devices such as single-board computers, which are often used in mobile robotic agents. This R-HGN approach is validated against individual behaviour implementation and random behaviour selection. This contrast is made in two sets of simulated environments: environments designed to challenge the held behaviours of the R-HGN, and randomly generated environments which are more challenging for the robotic swarm than R-HGN training conditions. R-HGN has been found to enable appropriate behaviour selection in both these sets, allowing significant swarm performance in pre-trained and unexpected environment conditions.

Recent surveys, studies, forecasts and other quantitative assessments of the progress of AI highlighted the growth in consumers' acceptance of chatbots, especially for help with routine tasks; questions about medical AI algorithms missing the worst patient outcomes; and new predictions for 2020 and beyond about the future of AI and work. Manpower France collects 1.3 million invoices from 80,000 companies annually. After nine months of testing Sidetrade's Aimie, a traditional machine learning-based tool, Manpower found that its collections increased 12% [Fortune] The first eight IT teams at Fannie Mae to receive Moogsoft's AIOps tool have seen a 35% reduction in IT incidents over the past 12 months; the teams using the AIOps tool have cut the time needed to resolve problems by between 25% and 75%, depending on the issue; Fannie expects that when it deploys the AI system to all business units and the system gets better at pinpointing root causes, monthly incidents will decline by 50% to 60% over the next year [WSJ] Familial hypercholesterolaemia or FH is a common genetic disorder that carries a 20-times higher risk for life-threatening cardiovascular disease, but today less than 10% percent of the 1.3 million Americans born with FH are diagnosed. The FIND FH screening algorithm was trained on data from 939 clinically diagnosed individuals and 83,136 individuals presumed free of FH. The model was then applied to a national health-care encounter database (170 million individuals) and an integrated health-care delivery system dataset (174,000 individuals).

Not every remix is an innovation: A network perspective on the 3d-printing community. Is the Maker Movement Contributing to Sustainability? Makers' ambitions to do socially valuable things. An empirically informed taxonomy for the Maker movement. DOI: 10.1007/978-3-319-41267-2_35 Misuraca G., Kucsera, C., Lipparini F., Voigt C., and Radescu R., (2015) ''Mapping and Analysis of ICT-enabled Social Innovation initiatives promoting social investment in integrated approaches to the provision of social services, European Commission's Joint Research Centre, IPTS (Technical Reports).

Throughout history, there have been moments when the progress of technology has taken great steps forward, when a combination of the right tools, a capacity for innovation, and sparks of ingenuity lead to breakthroughs that transform how we live our lives. How we produce and process information is critical to innovation – and our methods of recording and communicating information have themselves undergone great leaps. From the development of writing, to Gutenberg's printing press – which advanced the spread of knowledge to the masses and ushered in the enlightenment and scientific revolution – to the first programmable digital computer Colossus, the cost of reproducing and communicating information, or data, has fallen again and again. At the same time, tools for processing and making sense of large quantities of data have developed exponentially – with artificial intelligence (AI) representing the latest leap. In the same way that Gutenberg's press ushered in a new era of growth, data-driven technologies such as AI will underpin our future prosperity. There is no doubt that machine learning and AI is already improving peoples' lives, from intelligent personal assistants that can prepare us for changes in the weather, to systems that protect our money from criminals, or devices that offer medical advice from the comfort of our own home. And this is only the start; the potential of AI is undeniable. Our next challenge will be to harness this technology to transform how we diagnose diseases, manufacture goods and build our homes. Using advanced algorithmic techniques such as'deep learning', AI has the potential to solve complex problems fast, and in so doing, free up time and raise productivity. But we also need to make sure AI benefits everyone in the UK, which is why – in addition to this Sector Deal – the government is establishing a Centre for Data Ethics and Innovation to advise on the ethical use of data, including for AI. The huge global opportunity AI presents is why the Industrial Strategy white paper identified AI and data as 1 of 4 Grand Challenges – in which the UK can lead the world for years to come.

We're looking for a Senior Data Analyst to join the Decision Science team at Zapier, with a focus on either Marketing data or Product & Revenue data. Decision Science & Analytics is responsible for driving data insights, experimentation and quantitative research at Zapier. We work across Product & Revenue, Marketing, Finance and Customer Support, steering our business stakeholders to take data-informed decisions and deepening business understanding of opportunities and weaknesses. Data Analysts in Decision Science are semi-embedded into different business zones, developing tight-knit thought partnerships with key stakeholders. If you are a creative Data Analyst interested in helping to grow a product that helps the world automate their work so they can get back to living, this may be the right challenge for you!

Human brain development is rapid during infancy and early childhood. Many disease processes impair this development. Therefore, brain developmental age estimation (BDAE) is essential for all diseases affecting cognitive development. Brain magnetic resonance imaging (MRI) of infants shows brain growth and morphologic patterns during childhood. Therefore, we can estimate the developmental age from brain images. However, MRI analysis is time-consuming because each scan contains millions of data points (voxels). We investigated the three-dimensional convolutional neural network (3D CNN), a deep learning algorithm, to rapidly classify neurodevelopmental age with high accuracy based on MRIs. MRIs from normal newborns were obtained from the National Institute of Mental Health (NIMH) Data Archive. Age categories of pediatric MRIs were 3 wks + 1 wk, 1 yr + 2 wks, and 3 yrs + 4 wks. We trained a BDAE method using T1, T2, and proton density (PD) images from MRI scans of 112 individuals using 3D CNN. Compared with the known age, our method has a sensitivity of 99% and specificity of 98.3%. Moreover, our 3D CNN model has better performance in neurodevelopmental age estimation than does 2D CNN.

Multidimensional data have become ubiquitous and are frequently involved in situations where the information is aggregated over multiple data atoms. The aggregation can be over time or other features, such as geographical location or group affiliation. We often have access to multiple aggregated views of the same data, each aggregated in one or more dimensions, especially when data are collected or measured by different agencies. However, data mining and machine learning models require detailed data for personalized analysis and prediction. Thus, data disaggregation algorithms are becoming increasingly important in various domains. The goal of this paper is to reconstruct finer-scale data from multiple coarse views, aggregated over different (subsets of) dimensions. The proposed method, called PREMA, leverages low-rank tensor factorization tools to provide recovery guarantees under certain conditions. PREMA is flexible in the sense that it can perform disaggregation on data that have missing entries, i.e., partially observed. The proposed method considers challenging scenarios: i) the available views of the data are aggregated in two dimensions, i.e., double aggregation, and ii) the aggregation patterns are unknown. Experiments on real data from different domains, i.e., sales data from retail companies, crime counts, and weather observations, are presented to showcase the effectiveness of PREMA.

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