Parallel exploration is a key to a successful search. The recently proposed Negatively Correlated Search (NCS) achieved this ability by constructing a set of negatively correlated search processes and has been applied to many real-world problems. In NCS, the key technique is to explicitly model and maximize the diversity among search processes in parallel. However, the original diversity model was mostly devised by intuition, which introduced several drawbacks to NCS. In this paper, a mathematically principled diversity model is proposed to solve the existing drawbacks of NCS, resulting a new NCS framework. A new instantiation of NCS is also derived and its effectiveness is verified on a set of multi-modal continuous optimization problems.

Palo Alto, CA, 94306, USA ABSTRACT We propose an architecture to jointly learn word and label embeddings for slot filling in spoken language understanding. The proposed approach encodes labels using a combination of word embeddings and straightforward word-label association from the training data. Compared to the state-of- the-art methods, our approach does not require label embed-dings as part of the input and therefore lends itself nicely to a wide range of model architectures. In addition, our architecture computes contextual distances between words and labels to avoid adding contextual windows, thus reducing memory footprint. We validate the approach on established spoken dialogue datasets and show that it can achieve state-of-the-art performance with much fewer trainable parameters. Index T erms-- Slot-filling, recurrent neural network, distributional semantics, sequence labelling 1. INTRODUCTION In spoken language understanding (SLU), an essential step is to associate each word in an utterance with one semantic class label. These annotated utterances can then serve as a basis for higher level SLU tasks, such as topic identification and dialogue response generation. This process of semantic label tagging in SLU, dubbed slot filling, labels utterance sequences with tags under a specific scheme. As an example, the BIO scheme prefixes tags with one of the characters { B, I, O } to indicate the continuity of a tag: Begin, Inside, or Outside, e.g., B-price indicates this position is the beginning of the tag price. Researchers also developed deep learning architecture for slot filling, e.g., [1, 2, 3].

All around the world, from small-town Illinois in the US to Rochdale in England, from Perth, Australia, to Dumka in northern India, a revolution is under way in how governments treat the poor. You can't see it happening, and may have heard nothing about it. It's being planned by engineers and coders behind closed doors, in secure government locations far from public view. Only mathematicians and computer scientists fully understand the sea change, powered as it is by artificial intelligence (AI), predictive algorithms, risk modeling and biometrics. But if you are one of the millions of vulnerable people at the receiving end of the radical reshaping of welfare benefits, you know it is real and that its consequences can be serious – even deadly.

The artificial intelligence in manufacturing market is poised to hike from USD 1 billion in 2018 to over USD 16 billion by 2025, according to a 2019 Global Market Insights, Inc. report. The AI in manufacturing market is driven by the rapid adoption of industry 4.0 technologies. The growing need among the manufacturers to reduce the cost of operation and enhance operational efficiency is the primary factor driving the adoption of Industry 4.0. The new technology solutions are enhancing operational efficiency and reducing the time to market the products. It allows enterprises to analyze the customer demand, align their operations to meet the customer's requirement, and analyze the process in real-time.

All around the world, from small-town Illinois in the US to Rochdale in England, from the Pacific shore of Perth, Australia, to Dumka in northern India, a revolution is under way in how governments treat the poor. You can't see it happening, and may have heard nothing about it. It's being planned by engineers and coders behind closed doors, in secure government locations far from public view. Only mathematicians and computer scientists fully understand the sea change, powered as it is by artificial intelligence (AI), predictive algorithms, risk modeling and biometrics. But if you are one of the millions of vulnerable people at the receiving end of the radical reshaping of welfare benefits, you know it is real and that its consequences can be serious – even deadly.

Before Insurance Australia Group (IAG) can begin selling more than insurance products to consumers, the company realised it needed to shift what is currently a very analogue business into something that is more digitally orientated. "We really want to change the mindset, get some records and customer value, and build new businesses as well, including beyond insurance," IAG Digital Architecture director Ian Jamieson explained at New Relic Future Stack 2019 last week. "An insurance company not selling insurance is quite disruptive because a lot of the core systems is under insurance, so if we want to sell a solution that provides emergency assistance to your home that is not an insurance product, how would you bill someone for that so that it's not under insurance … there are a range of things we need to transform and add new capabilities to." Jamieson said some areas that IAG is looking to expand its business into include motor and home repair services, spinning up brand new businesses such as in mobility services, and through acquisitions of startups, such as its most recent purchase of Carbar, a subscription-based car ownership platform. To make sure these business plans become a reality, Jamieson said the company has moved away from taking traditional waterfall approaches to projects and using a cross-functional method.

PROFET: Construction and Inference of DBNs Based on Mathematical Models Hamda Ajmal, Michael Madden and Catherine Enright School of Computer Science, National University of Ireland Galway h.ajmal1@nuigalway.ie, Abstract This paper presents, evaluates, and discusses a new software tool to automatically build Dynamic Bayesian Networks (DBNs) from ordinary differential equations (ODEs) entered by the user. The DBNs generated from ODE models can handle both data uncertainty and model uncertainty in a principled manner. The application, named PROFET, can be used for temporal data mining with noisy or missing variables. It enables automatic re-estimation of model parameters using temporal evidence in the form of data streams. For temporal inference, PROFET includes both standard fixed time step particle filtering and its extension, adaptive-time particle filtering algorithms. Adaptive-time particle filtering enables the DBN to automatically adapt its time step length to match the dynamics of the model. We demonstrate PROFET's functionality by using it to infer the model variables by estimating the model parameters of four benchmark ODE systems. From the generation of the DBN model to temporal inference, the entire process is automated and is delivered as an open-source platform-independent software application with a comprehensive user interface. PROFET is released under the Apache License 2.0. Its source code, executable and documentation are available at http:://profet.

Colour vision has long fascinated scientists, who have sought to understand both the physiology of the mechanics of colour vision and the psychophysics of colour perception. We consider representations of colour in anatomically constrained convolutional deep neural networks. Following ideas from neuroscience, we classify cells in early layers into groups relating to their spectral and spatial functionality. We show the emergence of single and double opponent cells in our networks and characterise how the distribution of these cells changes under the constraint of a retinal bottleneck. Our experiments not only open up a new understanding of how deep networks process spatial and colour information, but also provide new tools to help understand the black box of deep learning. The code for all experiments is avaialable at \url{https://github.com/ecs-vlc/opponency}.

Reanalysis datasets combining numerical physics models and limited observations to generate a synthesised estimate of variables in an Earth system, are prone to biases against ground truth. Biases identified with the NASA Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) aerosol optical depth (AOD) dataset, against the Aerosol Robotic Network (AERONET) ground measurements in previous studies, motivated the development of a deep learning based AOD prediction model globally. This study combines a convolutional neural network (CNN) with MERRA-2, tested against all AERONET sites. The new hybrid CNN-based model provides better estimates validated versus AERONET ground truth, than only using MERRA-2 reanalysis.

Data samples collected for training machine learning models are typically assumed to be independent and identically distributed (iid). Recent research has demonstrated that this assumption can be problematic as it simplifies the manifold of structured data. This has motivated different research areas such as data poisoning, model improvement, and explanation of machine learning models. In this work, we study the influence of a sample on determining the intrinsic topological features of its underlying manifold. We propose the Shapley Homology framework, which provides a quantitative metric for the influence of a sample of the homology of a simplicial complex. By interpreting the influence as a probability measure, we further define an entropy which reflects the complexity of the data manifold. Our empirical studies show that when using the 0-dimensional homology, on neighboring graphs, samples with higher influence scores have more impact on the accuracy of neural networks for determining the graph connectivity and on several regular grammars whose higher entropy values imply more difficulty in being learned.