We use persistent homology along with the eigenfunctions of the Laplacian to study similarity amongst triangulated 2-manifolds. Our method relies on studying the lower-star filtration induced by the eigenfunctions of the Laplacian. This gives us a shape descriptor that inherits the rich information encoded in the eigenfunctions of the Laplacian. Moreover, the similarity between these descriptors can be easily computed using tools that are readily available in Topological Data Analysis. We provide experiments to illustrate the effectiveness of the proposed method.

Humans approximately spend a third of their life sleeping, which makes monitoring sleep an integral part of well-being. In this paper, a 34-layer deep residual ConvNet architecture for end-to-end sleep staging is proposed. The network takes raw single channel electroencephalogram (Fpz-Cz) signal as input and yields hypnogram annotations for each 30s segments as output. Experiments are carried out for two different scoring standards (5 and 6 stage classification) on the expanded PhysioNet Sleep-EDF dataset, which contains multi-source data from hospital and household polysomnography setups. The performance of the proposed network is compared with that of the state-of-the-art algorithms in patient independent validation tasks. The experimental results demonstrate the superiority of the proposed network compared to the best existing method, providing a relative improvement in epoch-wise average accuracy of 6.8% and 6.3% on the household data and multi-source data, respectively. Codes are made publicly available on Github.

This paper proposes a new model, called condition-transforming variational autoencoder (CTVAE), to improve the performance of conversation response generation using conditional variational autoencoders (CVAEs). In conventional CVAEs , the prior distribution of latent variable z follows a multivariate Gaussian distribution with mean and variance modulated by the input conditions. Previous work found that this distribution tends to become condition independent in practical application. In our proposed CTVAE model, the latent variable z is sampled by performing a non-lineartransformation on the combination of the input conditions and the samples from a condition-independent prior distribution N (0; I). In our objective evaluations, the CTVAE model outperforms the CVAE model on fluency metrics and surpasses a sequence-to-sequence (Seq2Seq) model on diversity metrics. In subjective preference tests, our proposed CTVAE model performs significantly better than CVAE and Seq2Seq models on generating fluency, informative and topic relevant responses.

Fine-grained Named Entity Recognition is a task whereby we detect and classify entity mentions to a large set of types. These types can span diverse domains such as finance, healthcare, and politics. We observe that when the type set spans several domains the accuracy of the entity detection becomes a limitation for supervised learning models. The primary reason being the lack of datasets where entity boundaries are properly annotated, whilst covering a large spectrum of entity types. Furthermore, many named entity systems suffer when considering the categorization of fine grained entity types. Our work attempts to address these issues, in part, by combining state-of-the-art deep learning models (ELMo) with an expansive knowledge base (Wikidata). Using our framework, we cross-validate our model on the 112 fine-grained entity types based on the hierarchy given from the Wiki(gold) dataset.

Verv, the Google-mentored energy tech startup behind the smart energy hub and green electricity sharing platform, recently announced that it has raised over £6.5 million (€7.5 million) in its Series A round led by environmental fund Earthworm. Earthworm has invested £5 million in Verv's pioneering IoT and renewable energy trading technology that could drive down household electricity bills and carbon emissions by over 20%. Other investors in the round include European innovation engine for sustainable energy, InnoEnergy, Crowdcube and international energy and services company, Centrica. Earthworm's investment is an important backing of Verv's vision to make millions of homes more green with a global network of smart hubs that offer a real-time breakdown of key appliance use and spend, as well as enable the trading of domestic renewable energy between communities. At Earthworm we are driven by sustainability and Verv represents a brilliant example of'enabling' technology.

Artificial Intelligence (AI) has been omnipresent and the latest in the block is Military. In recent times, AI has become a critical part of modern warfare. Compared with the conventional systems, military establishments churning enormous volumes of data are capable to integrate AI on a more unified process. Ensuring operational efficiency, AI improves self-regulation, self-control and self-actuation of combat systems, credit to its inherent computing coupled with accurate decision-making capabilities. Taking into account the enormous capability Artificial intelligence (AI) holds in the modern-day warfare, many of the world's most powerful countries have increased their investments into military and self-security.

Just a week after it was announced, Google's new AI ethics board is already in trouble. The board, founded to guide "responsible development of AI" at Google, would have had eight members and met four times over the course of 2019 to consider concerns about Google's AI program. Those concerns include how AI can enable authoritarian states, how AI algorithms produce disparate outcomes, whether to work on military applications of AI, and more. Of the eight people listed in Google's initial announcement, one (privacy researcher Alessandro Acquisti) has announced on Twitter that he won't serve, and two others are the subject of petitions calling for their removal -- Kay Coles James, president of the conservative Heritage Foundation think tank, and Dyan Gibbens, CEO of drone company Trumbull Unmanned. Thousands of Google employees have signed onto the petition calling for James's removal.

The inevitable march toward automation continues, analysts from the McKinsey Global Institute and from Tata Communications wrote in separate reports this week. Artificial intelligence's growth comes as no surprise -- a survey from Narrative Science and the National Business Research Institute conducted earlier this year found that 61 percent of businesses implemented AI in 2017, up from 38 percent in 2016 -- but this week's findings lay out in detail the likely socioeconomic impacts in the coming decade. The McKinsey models predict that 70 percent of companies will adopt at least one form of AI -- whether computer vision, natural language, virtual assistants, robotic process automation, or advanced machine learning -- by 2020. And Tata found unbridled enthusiasm among business leaders for an AI-dominated future; in a survey of 120 of them, 90 percent said they expect AI to enhance decision-making. McKinsey and Tata both contend that's a good thing.

The inevitable march toward automation continues, analysts from the McKinsey Global Institute and from Tata Communications wrote in separate reports this week. Artificial intelligence's growth comes as no surprise -- a survey from Narrative Science and the National Business Research Institute conducted earlier this year found that 61 percent of businesses implemented AI in 2017, up from 38 percent in 2016 -- but this week's findings lay out in detail the likely socioeconomic impacts in the coming decade. The McKinsey models predict that 70 percent of companies will adopt at least one form of AI -- whether computer vision, natural language, virtual assistants, robotic process automation, or advanced machine learning -- by 2020. And Tata found unbridled enthusiasm among business leaders for an AI-dominated future; in a survey of 120 of them, 90 percent said they expect AI to enhance decision-making. McKinsey and Tata both contend that's a good thing.

Few biometric technologies are sparking the imagination quite like facial recognition. Equally, its arrival has prompted profound concerns and reactions. With artificial intelligence and the blockchain, face recognition certainly represents a significant digital challenge for all companies and organizations - and especially governments. In this dossier, you'll discover the 7 face recognition facts and trends that are set to shape the landscape in 2019. Let's jump right in .