Graph generative models have been extensively studied in the data mining literature. While traditional techniques are based on generating structures that adhere to a pre-decided distribution, recent techniques have shifted towards learning this distribution directly from the data. While learning-based approaches have imparted significant improvement in quality, some limitations remain to be addressed. First, learning graph distributions introduces additional computational overhead, which limits their scalability to large graph databases. Second, many techniques only learn the structure and do not address the need to also learn node and edge labels, which encode important semantic information and influence the structure itself. Third, existing techniques often incorporate domain-specific rules and lack generalizability. Fourth, the experimentation of existing techniques is not comprehensive enough due to either using weak evaluation metrics or focusing primarily on synthetic or small datasets. In this work, we develop a domain-agnostic technique called GraphGen to overcome all of these limitations. GraphGen converts graphs to sequences using minimum DFS codes. Minimum DFS codes are canonical labels and capture the graph structure precisely along with the label information. The complex joint distributions between structure and semantic labels are learned through a novel LSTM architecture. Extensive experiments on million-sized, real graph datasets show GraphGen to be 4 times faster on average than state-of-the-art techniques while being significantly better in quality across a comprehensive set of 11 different metrics. Our code is released at https://github.com/idea-iitd/graphgen.

Diabetes is a global public health disease projected to affect 642 million adults by 2040, with about 75% residing in low- and middle-income countries. Diabetic retinopathy (DR) affects 1 in 3 people with diabetes and remains the leading cause of blindness in working-aged adults. There are 3 broad strategic imperatives to prevent blindness caused by DR. Primary prevention requires preventing or delaying the onset of DR in those with diabetes by systems-level lifestyle modifications such as increasing physical activity or dietary modifications, pharmacological interventions for glycaemic and blood pressure control, and systematic screening for the onset of DR. Secondary prevention requires preventing the progression of DR in patients with DR by continuing systemic risk factor control, regular screening to monitor for the progression of mild DR to vision-threatening stages, and the development and implementation of evidence-based guidelines for managing DR. In this aspect, telemedicine-based DR screening incorporating artificial intelligence technology has the potential to facilitate more widespread and cost-effective screening, particularly in low- and middle-income countries. Tertiary prevention of DR blindness has been the main focus of the clinical ophthalmology community, classically based on laser photocoagulation treatment and ocular surgery but with an increasing use of anti-vascular endothelial growth factor (anti-VEGF) for vision-threatening DR. Evidence from serial epidemiological studies shows blindness due to DR has declined in high-income countries (e.g., the USA and UK) due to coordinated public health education efforts, increased awareness, early detection by DR screening, sustained systemic risk factor control, and the availability of effective tertiary level treatment. However, the progress made in reducing DR blindness in high-income countries may be overwhelmed by the increasing numbers of patients with diabetes and DR in low- and middle-income countries (e.g., China, India, Indonesia, etc.).

The Machine Learning Artificial intelligence market has been changing all over the world and we have been seeing a great growth In the Machine Learning Artificial intelligence market and this growth is expected to be huge by 2024. The market has been lucrative and the growth of the market is driven by key factors such as manufacturing activity, risks of the market, acquisitions, new trends, assessment of the new technologies and their implementation. This report covers all of the aspects required to gain a complete understanding of the pre-market conditions, current conditions as well as a well-measured forecast. The report has been segmented as per the examined essential aspects such as sales, revenue, market size, and other aspects involved to post good growth numbers in the market. Top Companies are covering This Report:- AIBrain, Amazon, Anki, CloudMinds, Deepmind, Google, Facebook, IBM, Iris AI, Apple, Luminoso, Qualcomm.

LONDON--(BUSINESS WIRE)--The global artificial intelligence (AI) market in manufacturing industry is expected to post a CAGR of around 31% during the period 2019-2023, according to the latest market research report by Technavio. Manufacturing companies are moving toward the implementation of Industry 4.0 standard to intensify automation to achieve higher operational efficiencies. This is increasing the adoption of a greater number of connected devices and technologies such as big data, ML, and IoT, which is resulting in the generation of high volumes of data. This, in turn, is compelling manufacturing firms to adopt AI-based solutions to extract insights from the data to improve the management of operations. Hence, the integration of industrial IoT and big data is crucial in driving the growth of the market.

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Chile is making its mark in the world, and specifically in the Latin America, with its focus on technology. But as the the country moves closer to its goal, it sees the need for more coordinated policies. That and the desire to build a more "informed and knowledge-based" society prompted the creation of its own Ministry of Science, Technology, Knowledge and Innovation in 2018. Andres Couve has spent his entire career working on research and development. A biologist with a PhD degree in Cell Biology from the prestigious Mount Sinai School of Medicine in New York, he also holds a post-doctorate in Neuroscience from University College London (UCL).

In this paper we develop a new model for deep image clustering, using convolutional neural networks and tensor kernels. The proposed Deep Tensor Kernel Clustering (DTKC) consists of a convolutional neural network (CNN), which is trained to reflect a common cluster structure at the output of its intermediate layers. Encouraging a consistent cluster structure throughout the network has the potential to guide it towards meaningful clusters, even though these clusters might appear to be nonlinear in the input space. The cluster structure is enforced through the idea of unsupervised companion objectives, where separate loss functions are attached to layers in the network. These unsupervised companion objectives are constructed based on a proposed generalization of the Cauchy-Schwarz (CS) divergence, from vectors to tensors of arbitrary rank. Generalizing the CS divergence to tensor-valued data is a crucial step, due to the tensorial nature of the intermediate representations in the CNN. Several experiments are conducted to thoroughly assess the performance of the proposed DTKC model. The results indicate that the model outperforms, or performs comparable to, a wide range of baseline algorithms. We also empirically demonstrate that our model does not suffer from objective function mismatch, which can be a problematic artifact in autoencoder-based clustering models.

Grid path planning is an important problem in AI. Its understanding has been key for the development of autonomous navigation systems. An interesting and rather surprising fact about the vast literature on this problem is that only a few neighborhoods have been used when evaluating these algorithms. Indeed, only the 4- and 8-neighborhoods are usually considered, and rarely the 16-neighborhood. This paper describes three contributions that enable the construction of effective grid path planners for extended 2k-neighborhoods; that is, neighborhoods that admit 2k neighbors per state, where k is a parameter. First, we provide a simple recursive definition of the 2k-neighborhood in terms of the 2k-1-neighborhood. Second, we derive distance functions, for any k ≥ 2, which allow us to propose admissible heuristics that are perfect for obstacle-free grids, which generalize the well-known Manhattan and Octile distances. Third, we define the notion of canonical path for the 2k-neighborhood; this allows us to incorporate our neighborhoods into two versions of A*, namely Canonical A* and Jump Point Search (JPS), whose performance, we show, scales well when increasing k. Our empirical evaluation shows that, when increasing k, the cost of the solution found improves substantially.  Used with the 2k-neighborhood, Canonical A* and JPS, in many configurations, are also superior to the any-angle path planner Theta* both in terms of solution quality and runtime. Our planner is competitive with one implementation of the any-angle path planner, ANYA in some configurations. Our main practical conclusion is that standard, well-understood grid path planning technology may provide an effective approach to any-angle grid path planning.

In problem solving, the paths towards solutions can be viewed as a sequence of decisions. The decisions, made by humans or computers, describe a trajectory through a high-dimensional representation space of the problem. Using dimensionality reduction, these trajectories can be visualized in lower dimensional space. Such embedded trajectories have previously been applied to a wide variety of data, but so far, almost exclusively the self-similarity of single trajectories has been analyzed. In contrast, we describe patterns emerging from drawing many trajectories---for different initial conditions, end states, or solution strategies---in the same embedding space. We argue that general statements about the problem solving tasks and solving strategies can be made by interpreting these patterns. We explore and characterize such patterns in trajectories resulting from human and machine-made decisions in a variety of application domains: logic puzzles (Rubik's cube), strategy games (chess), and optimization problems (neural network training). In the context of Rubik's cube, we present a physical interactive demonstrator that uses trajectory visualization to provide immediate feedback to users regarding the consequences of their decisions. We also discuss the importance of suitably chosen representation spaces and similarity metrics for the embedding.

Code-mixing is the practice of alternating between two or more languages. Mostly observed in multilingual societies, its occurrence is increasing and therefore its importance. A major part of sentiment analysis research has been monolingual, and most of them perform poorly on code-mixed text. In this work, we introduce methods that use different kinds of multilingual and cross-lingual embeddings to efficiently transfer knowledge from monolingual text to code-mixed text for sentiment analysis of code-mixed text. Our methods can handle code-mixed text through a zero-shot learning. Our methods beat state-of-the-art on English-Spanish code-mixed sentiment analysis by absolute 3\% F1-score. We are able to achieve 0.58 F1-score (without parallel corpus) and 0.62 F1-score (with parallel corpus) on the same benchmark in a zero-shot way as compared to 0.68 F1-score in supervised settings. Our code is publicly available.