In this paper, we propose a one-pass algorithm on MapReduce for penalized linear regression \[f_\lambda(\alpha, \beta) = \|Y - \alpha\mathbf{1} - X\beta\|_2^2 + p_{\lambda}(\beta)\] where $\alpha$ is the intercept which can be omitted depending on application; $\beta$ is the coefficients and $p_{\lambda}$ is the penalized function with penalizing parameter $\lambda$. $f_\lambda(\alpha, \beta)$ includes interesting classes such as Lasso, Ridge regression and Elastic-net. Compared to latest iterative distributed algorithms requiring multiple MapReduce jobs, our algorithm achieves huge performance improvement; moreover, our algorithm is exact compared to the approximate algorithms such as parallel stochastic gradient decent. Moreover, what our algorithm distinguishes with others is that it trains the model with cross validation to choose optimal $\lambda$ instead of user specified one. Key words: penalized linear regression, lasso, elastic-net, ridge, MapReduce

Noname manuscript No. (will be inserted by the editor) Abstract Hierarchical models are versatile tools for joint modeling of data sets arising from different, but related, sources. Fully Bayesian inference may, however, become computationally prohibitive if the sourcespecific data models are complex, or if the number of sources is very large. To facilitate computation, we propose an approach, where inference is first made independently for the parameters of each data set, whereupon the obtained posterior samples are used as observed data in a substitute hierarchical model, based on a scaled likelihood function. Compared to direct inference in a full hierarchical model, the approach has the advantage of being able to speed up convergenceby breaking down the initial large inference problem into smaller individual subproblems with better convergence properties. Moreover it enables parallel processing of the possibly complex inferences of the source-specific parameters, which may otherwise create a computational bottleneck if processed jointly as part of a hierarchical model.

WIRE)--DataRPM, the award-winning Cognitive Data Science company that automates Machine Learning to deliver Recommendation & Prediction data products, today announced a partnership with big data analytics company, Tamr, Inc. "We are truly excited about our partnership with Tamr to expand the portfolio of innovative solutions to provide end-to-end data products," said Sundeep Sanghavi, DataRPM Co-Founder and Chief Executive Officer. "Data integration quality and prep is a crucial requirement for every big data initiative. Our partnership further meets the expanding needs of our customers and allows them to truly leverage machine learning all the way from data ingestion to strategic business insights." "DataRPM's Cognitive Data Science Platform automates machine learning for Recommendations & Predictions to deliver continuous insights that propel enterprises' growth dramatically, thus redefining data science with scale, speed and repeatability," further explained Sundeep. "With the dramatic explosion in data, compounded by the growing shortage of data scientists, and the need for faster sprint cycles to launch strategic initiatives, the call of the hour is Cognitive Data Science. By automating Machine Learning, greater value from productized data can now be derived through operationalizing its usage within companies' process flows in a continuous manner. "Tamr's machine-driven, human-guided approach to data preparation aligns closely with DataRPM's machine learning automation," said Andy Palmer, Tamr Co-Founder and Chief Executive Officer. "DataRPM's Prediction and Recommendation models need a continuous flow of clean, unified data that Tamr provides.

The initial collection on Megapixl.com is curated based on editor's feedback and customer behaviors that rate images and place them within certain ranks in the collection. Once the site is launched, the machine learning platform will take gathered user data into account and offer refinements to the Dreamstime.com Content on the site includes photos, vector art, and video content representing several categories including abstract, business, people, editorial, 2D & 3D Animation, Video Production Elements, Technology, and Travel. "By launching MegaPixl we give users access to an incredible collection that is curated by humans and then further improved and customized by machine learning tools that learn past behaviors to make dynamic recommendations," said Serban Enache, CEO and co-founder, Dreamstime. "The initial collection on Megapixl is already curated because we've accounted for user's actions so we know how images will be rated and placed within the collection. Megapixl is designed as a first stop for designers because we are effectively using the designer community to help itself by basing the collection on designer behaviors. Users will come to the site to find the right image quickly – giving them more time to focus on their designs."

In theory, chatbots on Messenger would allow businesses to provide customer service without involving human workers. If you wanted to reach out to a business, you could do so via your Messenger app, rather than by looking up a phone number and calling. For example, Facebook showed off a chatbot for 1-800-Flowers.com that automatically takes orders via Messenger. It says things like: "White is a great choice! What is the recipient's name?"

Description: The ATLAS conference is an interdisciplinary workshop on mathematical and algorithmimcal approaches for high dimensional problems in data sciences. This year's event is particularly dedicated to signal processing and applications in different fields as medical imaging, neurosciences, astrophysics.... with a particular emphasis on the use of innovative optimization methods. The workshop's program will feature plenary talks given by experts in the field, as well as short talk.

This week, in Geneva, Indian diplomats are closely monitoring an international expert review of the legal implications of the so-called "lethal autonomous weapons". These weapons will have the capability of selecting and engaging targets on their own. Although fully autonomous weapons are yet to register significant presence in the arsenal of any nation, many consider their development and deployment inevitable in the coming years. Rapid advances in robotics, machine-learning and big-data analytics are at once driving the so-called "fourth industrial revolution" and the transformation of modern warfare. How the leading powers mobilise and deploy these technologies will shape the balance of economic and military power among them in the coming decades.

Emerald Medical Applications Corp. (OTCQB: MRLA), an Israeli-based company engaged in the development and sale of its proprietary DermaCompare cloud-based, artificial intelligence technology for the early diagnosis of Melanoma/skin cancer, today announced entry into a cooperation agreement with Terem, one of Israel's largest community-based, emergency healthcare providers with 17 medical facilities, serving over 700,000 patients throughout Israel. Starting in April 2016, Emerald will begin to offer its DermaCompare technology at each of Terem's clinics throughout Israel, offering advanced dermatological examinations, diagnosisand treatment led by a leading professional Dermatologists. DermaCompare is Emerald's cloud-based, artificial, intelligence technology using Total Body Photography imaging which is capable of being automatically compared to a patient's previous images to diagnose and detect the presence of Melanoma in its earliest stages. Lior Wayn, Emerald's CEO, stated that "DermaCompare, Emerald's FDA approved, HIPPA compliant software technology, which can be downloaded from any Mac or Android based App store, enables physicians andtheir patients, using virtually any digital camera, including cell phones, iPads, tablets and other similar devices, to take Total Body Photography images and, in real-time, transmit these images for dermatological evaluation and identification of suspicious moles, lesions and other skin conditions. These images are then compared using Emerald's cloud database, as well as the patients previous Total Body Photography images, which will dramatically enhance a physician's ability to detect Melanoma earlier, more accurately and more efficient than other means of diagnosis."

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Tree based learning algorithms are considered to be one of the best and mostly used supervised learning methods. Tree based methods empower predictive models with high accuracy, stability and ease of interpretation. Unlike linear models, they map non-linear relationships quite well. They are adaptable at solving any kind of problem at hand (classification or regression). Methods like decision trees, random forest, gradient boosting are being popularly used in all kinds of data science problems. Hence, for every analyst (fresher also), it's important to learn these algorithms and use them for modeling. This tutorial is meant to help beginners learn tree based modeling from scratch. After the successful completion of this tutorial, one is expected to become proficient at using tree based algorithms and build predictive models. Note: This tutorial requires no prior knowledge of machine learning.