The bulk synchronous parallel (BSP) is a celebrated synchronization model for general-purpose parallel computing that has successfully been employed for distributed training of machine learning models. A prevalent shortcoming of the BSP is that it requires workers to wait for the straggler at every iteration. To ameliorate this shortcoming of classic BSP, we propose ELASTICBSP a model that aims to relax its strict synchronization requirement. The proposed model offers more flexibility and adaptability during the training phase, without sacrificing on the accuracy of the trained model. We also propose an efficient method that materializes the model, named ZIPLINE. The algorithm is tunable and can effectively balance the trade-off between quality of convergence and iteration throughput, in order to accommodate different environments or applications. A thorough experimental evaluation demonstrates that our proposed ELASTICBSP model converges faster and to a higher accuracy than the classic BSP. It also achieves comparable (if not higher) accuracy than the other sensible synchronization models.

Trajectory owner prediction is the basis for many applications such as personalized recommendation, urban planning. Although much effort has been put on this topic, the results archived are still not good enough. Existing methods mainly employ RNNs to model trajectories semantically due to the inherent sequential attribute of trajectories. However, these approaches are weak at Point of Interest (POI) representation learning and trajectory feature detection. Thus, the performance of existing solutions is far from the requirements of practical applications. In this paper, we propose a novel CNN-based Trajectory Owner Prediction (CNNTOP) method. Firstly, we connect all POI according to trajectories from all users. The result is a connected graph that can be used to generate more informative POI sequences than other approaches. Secondly, we employ the Node2Vec algorithm to encode each POI into a low-dimensional real value vector. Then, we transform each trajectory into a fixed-dimensional matrix, which is similar to an image. Finally, a CNN is designed to detect features and predict the owner of a given trajectory. The CNN can extract informative features from the matrix representations of trajectories by convolutional operations, Batch normalization, and $K$-max pooling operations. Extensive experiments on real datasets demonstrate that CNNTOP substantially outperforms existing solutions in terms of macro-Precision, macro-Recall, macro-F1, and accuracy.

AI - Deep Learning based platform is used for simulating the oil field, planning and predicting the oil produced in the Oil Industry supply chain using forecasting techniques. Machine Learning is used in predictive maintenance, forecasting, analysis, energy trading, buy/sell, trade, risk management, and optimization. The Oil & Gas industry is divided into divisions which are the upstream, downstream and midstream. Machine Learning Analytics is used in optimizations for upstream, downstream and midstream business process. The business processes are related to exploration, extraction, refining, transporting of oil and gas by oil tankers and pipelines, and marketing of petroleum products.

An artificial intelligence system developed by the NHS and Google could dramatically speed up diagnosis times for women with breast cancer, a study suggests. The "deep learning" system outperformed human experts in detecting breast cancer from x-rays. Integrating the system into Britain's breast cancer screening process -- in which a mammogram is read by two NHS radiologists -- could reduce the second reader's workload by nearly 90 per cent, according to the study. Women between the ages of 50 and 71 are invited to receive a mammogram on the NHS every three years. An x-ray of the breast tissue is used to look for abnormal growths or changes that may be cancerous.

"I am telling you, the world's first trillionaires are going to come from somebody who masters AI and all its derivatives, and applies it in ways we never thought of." Artificial Intelligence (AI), despite being a technology that is more than 50 years old, still most people wonder what this field is all about. Even though this technology is surrounding us in many forms and we are using AI technology such as AI-based mobile apps in our day-to-day lives but still many people find it hard to believe. According to reports by this HubSpot survey, around 63% people don't realize they are using AI technologies. As Artificial Intelligence is having many associated technologies such as Machine Learning, Deep Learning, Big Data, and Natural Language Processing, etc. it's totally understandable that people get confused. If you are one among them who gets confused with all these terminologies?

A deep neural network (DNN) with piecewise linear activatio ns can partition the input space into numerous small linear regions, where diffe rent linear functions are fitted. It is believed that the number of these regions rep resents the expressivity of the DNN. This paper provides a novel and meticulous perspe ctive to look into DNNs: Instead of just counting the number of the linear regio ns, we study their local properties, such as the inspheres, the directions of t he corresponding hyper-planes, the decision boundaries, and the relevance of the su rrounding regions. W e empirically observed that different optimization techniq ues lead to completely different linear regions, even though they result in similar cl assification accuracies. W e hope our study can inspire the design of novel optimizatio n techniques, and help discover and analyze the behaviors of DNNs. In the past few decades, deep neural networks (DNNs) have ach ieved remarkable success in various difficult tasks of machine learning (Krizhevsky et al., 2012; Graves et al., 2013; Goodfellow et al., 2014; He et al., 2016; Silver et al., 2017; Devlin et al., 2019). Albeit the great progress DNNs have made, there are still many problems which have not been thoro ughly studied, such as the expressivity and optimization of DNNs. High expressivity is believed to be one of the most important reasons for the success of DNNs. It is well known that a standard deep feedforward network with pie cewise linear activations can partition the input space into many linear regions, where different li near functions are fitted (Pascanu et al., 2014; Montufar et al., 2014). More specifically, the activat ion states are in one-to-one correspondence with the linear regions, i.e., all points in the same li near region activate the same nodes of the DNN, and hence the hidden layers serve as a series of affine transformations of these points.

The recent increase in the scale and complexity of software systems has introduced new challenges to the time series monitoring and anomaly detection process. A major drawback of existing anomaly detection methods is that they lack contextual information to help stakeholders identify the cause of anomalies. This problem, known as root cause detection, is particularly challenging to undertake in today's complex distributed software systems since the metrics under consideration generally have multiple internal and external dependencies. Significant manual analysis and strong domain expertise is required to isolate the correct cause of the problem. In this paper, we propose a method that isolates the root cause of an anomaly by analyzing the patterns in time series fluctuations. Our method considers the time series as observations from an underlying process passing through a sequence of discretized hidden states. The idea is to track the propagation of the effect when a given problem causes unaligned but homogeneous shifts of the underlying states. We evaluate our approach by finding the root cause of anomalies in Zillows clickstream data by identifying causal patterns among a set of observed fluctuations.

--Multivariate time series prediction has applications in a wide variety of domains and is considered to be a very challenging task, especially when the variables have correlations and exhibit complex temporal patterns, such as seasonality and trend. Many existing methods suffer from strong statistical assumptions, numerical issues with high dimensionality, manual feature engineering efforts, and scalability. In this work, we present a novel deep learning architecture, known as T emporal T ensor Transformation Network, which transforms the original multivariate time series into a higher order of tensor through the proposed T emporal-Slicing Stack Transformation. This yields a new representation of the original multivariate time series, which enables the convolution kernel to extract complex and nonlinear features as well as variable interactional signals from a relatively large temporal region. Experimental results show that T emporal T ensor Transformation Network outperforms several state-of-the-art methods on window-based predictions across various tasks. The proposed architecture also demonstrates robust prediction performance through an extensive sensitivity analysis. Index T erms--multivariate time series, prediction, convolution, deep learning, tensor transformation I. I NTRODUCTION Multivariate time series analysis has gained wide spread applications in many fields, e.g., financial market prediction, weather forecasting, and energy consumption prediction. It is used to model and explain the underlying temporal patterns among a group of time series variables in dynamical systems. V arious methods have been proposed to predict multivariate time series based on statistical modeling and deep neural networks. Classical statistical models assume that the time series is stationary, i.e., the summary statistics of data points are consistent over time. Preprocessing procedures are usually needed to remove trend, seasonality, and other time-dependent structures from the raw series in order to make the data stationary. In addition, these models also assume the independence condition in the underlying linear regression problem, i.e., the random errors in the model are not correlated over time.

Transfer in Reinforcement Learning (RL) refers to the idea of applying knowledge gained from previous tasks to solving related tasks. Learning a universal value function (Schaul et al., 2015), which generalizes over goals and states, has previously been shown to be useful for transfer. However, successor features are believed to be more suitable than values for transfer (Dayan, 1993; Barreto et al., 2017), even though they cannot directly generalize to new goals. In this paper, we propose (1) Universal Successor Features (USFs) to capture the underlying dynamics of the environment while allowing generalization to unseen goals and (2) a flexible end-to-end model of USFs that can be trained by interacting with the environment. We show that learning USFs is compatible with any RL algorithm that learns state values using a temporal difference method. Our experiments in a simple gridworld and with two MuJoCo environments show that USFs can greatly accelerate training when learning multiple tasks and can effectively transfer knowledge to new tasks.

At a very basic level, deep learning is a machine learning technique. It teaches a computer to filter inputs through layers to learn how to predict and classify information. Observations can be in the form of images, text, or sound. The inspiration for deep learning is the way that the human brain filters information. Its purpose is to mimic how the human brain works to create some real magic. In the human brain, there are about 100 billion neurons. Each neuron connects to about 100,000 of its neighbors.