This paper is a step-by-step tutorial for fitting a mixture distribution to data. It merely assumes the reader has the background of calculus and linear algebra. Other required background is briefly reviewed before explaining the main algorithm. In explaining the main algorithm, first, fitting a mixture of two distributions is detailed and examples of fitting two Gaussians and Poissons, respectively for continuous and discrete cases, are introduced. Thereafter, fitting several distributions in general case is explained and examples of several Gaussians (Gaussian Mixture Model) and Poissons are again provided. Model-based clustering, as one of the applications of mixture distributions, is also introduced. Numerical simulations are also provided for both Gaussian and Poisson examples for the sake of better clarification.

Electroencephalography (EEG) is a complex signal and can require several years of training to be correctly interpreted. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question. In this work, we review 156 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, brain-computer interfacing, and cognitive and affective monitoring. We extract trends and highlight interesting approaches in order to inform future research and formulate recommendations. Various data items were extracted for each study pertaining to 1) the data, 2) the preprocessing methodology, 3) the DL design choices, 4) the results, and 5) the reproducibility of the experiments. Our analysis reveals that the amount of EEG data used across studies varies from less than ten minutes to thousands of hours. As for the model, 40% of the studies used convolutional neural networks (CNNs), while 14% used recurrent neural networks (RNNs), most often with a total of 3 to 10 layers. Moreover, almost one-half of the studies trained their models on raw or preprocessed EEG time series. Finally, the median gain in accuracy of DL approaches over traditional baselines was 5.4% across all relevant studies. More importantly, however, we noticed studies often suffer from poor reproducibility: a majority of papers would be hard or impossible to reproduce given the unavailability of their data and code. To help the field progress, we provide a list of recommendations for future studies and we make our summary table of DL and EEG papers available and invite the community to contribute.

Talking to voice assistants or chatbots are more like asking something to do with no emotions attached which is far different from talking to a human. With the advancements in artificial intelligence and machine learning, the tech giants are always looking to develop something out of the box. In an effort to make these assistants more human-like in nature, Microsoft has come up a chatbot that not only assists you whenever you want but also conveys it emotionally. AI-based chatbot, XiaoIce, translated to "little Ice" in Chinese is one of the ambitious projects of Microsoft, that was released by researchers in May 2014 at China. It's uniquely designed as an AI companion with an emotional connection to satisfy the need for human communication and is catering to over 660 million users.

Similarly, an Indian travel start-up, RailYatri, has created an Estimated Arrival Time prediction algorithm using Machine Learning and statistical modelling techniques to predict the arrival time of trains. The system, trained on historical data, can provide customers with realistic estimated times for the arrival of their trains. According to Kapil Raizada, Cofounder of RailYatri, the method to predict the arrival time of trains in India had not changed over decades and was typically based on a distance by speed ratio for trains with some buffer time. RailYatri's Machine Learning algorithm takes into considerations other parameters ("ground realities") such as increasing traffic, rush, seasonality, etc, and adapts as it learns from subsequent inputs, making the predictions better with time. It uses clustering techniques to organise historical train runs into thousands of patterns where time series data attributes are similar.

In the world of science and technology, it is being said that we are at the beginning of the Fourth Industrial Revolution. The first that lasted from 1760 to 1840 brought in the age of mechanised production. It was the result of new materials like iron and steel, which combined with new energy resources of coal and steam, led to'mass production', and a factory system with division of labour. The second industrial revolution from 1870 to the early part of 20th century was the result of electricity, and the internal combustion engine. Both powered industrial machines and made transport possible.

If you want to see what the future of iron to support machine learning looks like, then perhaps the best place to look at what the hyperscalers and cloud builders who account for the vast majority of processing and applications in this field are deploying. Or, more precisely, look at the iron that their ODM partners are trying to peddle to other companies that is inspired by what the hyperscalers and cloud builders are using. Inspur, one of the upstart makers of infrastructure that is located in China but which is expanding outwards to North America and Europe, is a good case in point. The company has very good insight into what the Big Four in China – Alibaba, Baidu, Tencent, and either China Mobile or JD.com, depending on how you want to rank numbers four and five – are doing with their vast infrastructure, and it dominates some of these accounts. As we reported back in October 2018, when Inspur was making a push into Open Compute, Inspur has about half of the plain vanilla server shipments and about 80 percent of the GPU accelerated machine learning shipments to the hyperscalers and cloud builders in China. Inspur also works with Microsoft, one of the Big Four in the United States, on its current generation "Project Olympus" servers, the designs of which have been open sourced through the Open Compute Project championed by Facebook alongside some other hyperscale iron that was inspired by Inspur's manufacturing deals with Alibaba and Tencent.

A new business intelligence report released by HTF MI with title "Asia-Pacific Drone Analytics Market Report 2018 Market" has abilities to raise as the most significant market worldwide as it has remained playing a remarkable role in establishing progressive impacts on the universal economy. The Asia-Pacific Drone Analytics Market Report offers energetic visions to conclude and study market size, market hopes, and competitive surroundings. The research is derived through primary and secondary statistics sources and it comprises both qualitative and quantitative detailing. Market Overview of Asia-Pacific Drone Analytics If you are involved in the Asia-Pacific Drone Analytics industry or aim to be, then this study will provide you inclusive point of view. It's vital you keep your market knowledge up to date segmented by Applications [Agriculture & Forestry, Construction, Insurance, Mining & Quarrying, Utility, Telecommunication, Oil & Gas, Transportation & Others], Product Types [, Seismic, Acoustic, Magnetic & Infrared] and major players.

Applications can be submitted online at http://www.ai.iitkgp.ac.in/outreach, latest by 15th February, 2019 IIT Hyderabad to launch B.Tech. in Artificial Intelligence Indian Institute of Technology- Kharagpur, Kharagpur-7211302, has invited applications for admission to the Certificate program in'Foundations of Artificial Intelligence and Machine Learning'. The six month course is aimed at working professionals and students. The classes will be conducted in the weekends in Kolkata and Bengaluru from March 2019. Eligibility: Applicant should hold a BE/ B.Tech. or MSc with Mathematics and should have learned Programming at college level. Final year students are also eligible to apply.

The new research comes from Institute of Evolutionary Biology (IBE), the Centro Nacional de Análisis Genómico (CNAG-CRG) of the Centre for Genomic Regulation (CRG) and the Institute of Genomics at the University of Tartu. In studies researchers have applied deep learning algorithms and statistical methods to establish the footprint of a new hominid. The application of human DNA computational analysis indicates that the extinct species was a hybrid of Neanderthals and Denisovans. At some stage this hominid cross bred with'Out of Africa' modern humans within the region of the world that is now Asia. The scientific theory of recent African origin of modern humans is the most widely accepted model of the geographic origin and early migration of anatomically modern humans (Homo sapiens).

A new machine-learning model developed by a West Virginia University student has the potential for energy, environmental and even healthcare applications. The model, which can be used to predict the adsorption energies, i.e. adhesive capabilities in gold nanoparticles, was developed by Gihan Panapitiya, a doctoral physics student from Sri Lanka. Gold nanoparticles have historically been used by artists to bring out vibrant colors via their interaction with light. Now they are increasingly used in high technology applications, electronic conductors and others. "Machine learning recently came into the spotlight, and we wanted to do something linking machine learning with gold nanoparticles as catalysts," he said.