The digital economy has transformed the way we communicate with each other; the way we consume information, products, and services; the way we entertain ourselves. It's revolutionized seemingly non-digital industries--think of how different financial services, for instance, are today from what they were two decades ago--and investors expect it to soon transform others, which is why Tesla Motors is worth more than General Motors despite making a tiny fraction as many cars as GM makes and earning a tiny fraction of the revenue. This phenomenon explains why the so-called Big Five of the digital economy--Apple, Alphabet, Microsoft, Amazon, and Facebook--have, at various points over the last year, been the five most valuable companies in the world. So you might say that the digital economy has lived up to the expectations people had for it 20 years ago, in the early days of the Web. In other important ways, however, its consequences have been smaller than you might think.

Recitations from Tel-Aviv University introductory course to computer science, assembled as IPython notebooks by Yoav Ram. Exploratory Computing with Python, a set of 15 Notebooks that cover exploratory computing, data analysis, and visualization. No prior programming knowledge required. Each Notebook includes a number of exercises (with answers) that should take less than 4 hours to complete. Developed by Mark Bakker for undergraduate engineering students at the Delft University of Technology.

Before the internet arrived, I used to be an equity analyst. I'd spend my days finding and arranging metaphorical jigsaw pieces to come up with a complete picture and an investment recommendation, which the sales guys would disseminate by telephoning their favorite clients first, and the ones they didn't like so much a bit later. Today, at asset management companies and other financial institutions, there are still large teams of analysts and portfolio managers, sifting through data, developing investment theses and making asset allocation decisions. The difference is that we are deluged in data. Not only is the amount of data available to us accelerating, the nature of it is changing, with new sources of information being seen as potentially relevant for analysis.

A committee of NHK came up with a proposal on Tuesday calling for charging fees on households without television sets for watching TV programs distributed through the Internet simultaneously. The committee also proposed that NHK not charge additional fees to households that have already signed viewing contracts with the public broadcaster when members of the households watch programs online. The broadcast law requires residents in Japan to conclude the contracts if they own TV program receivers. Currently, the law prohibits NHK from distributing TV programs at the same time through the Internet on a regular basis. If the law is amended to allow such simultaneous program distribution, fees should be imposed as soon as non-TV devices such as smartphones and personal computers become ready for receiving programs online upon downloading of apps, the committee said.

The marketing industry is rapidly evolving. It's at a new phase where Big Data and AI (Artificial Intelligence) are driving strategy development and decision making. Because conventional analytics scale at a gradual pace, marketers are beginning to utilise AI to analyse the large volume of unstructured data to improve marketing research, forecasting accuracy and campaign experiences. A research of CMOs from the US, UK, and China from companies with more than $500 million in revenue revealed that around two thirds of these think Artificial Intelligence will play a significant role in their future marketing operations. But only a third claimed to have good knowledge of how it is actually implemented.

China's not just a copycat economy anymore; it has turned into a high-speed innovator. So says Ben Joffe, general partner of HAX, a five-year-old hardware accelerator and investor. And the rest of the world had better watch out. We call what China is doing "Xiaomization," because smart-phone giant Xiaomi's high-speed commoditization of product categories is driving a lot of people out of business. Right now, he says, Xiaomi has invested in 77 companies--and four are unicorns, meaning they have a market capitalization of over $1 billion.

Artificial Intelligence gets a bad rap in pop culture. Movies like Terminator (with its rebellious Skynet) and 2001 (with its murderous HAL 9000) portray a future where the robots get smart, and conclude that it is in their interests to destroy mankind. But the truth about AI is a lot more mundane. Most of us use AI every day when we talk and interact with Siri or Google on our phones, and AI is why Netflix knows what movies you'll like and what other products you'll want to buy on Amazon. AI is poised to become a big deal in banking.

The max-product {belief propagation} (BP) is a popular message-passing heuristic for approximating a maximum-a-posteriori (MAP) assignment in a joint distribution represented by a graphical model (GM). In the past years, it has been shown that BP can solve a few classes of linear programming (LP) formulations to combinatorial optimization problems including maximum weight matching, shortest path and network flow, i.e., BP can be used as a message-passing solver for certain combinatorial optimizations. However, those LPs and corresponding BP analysis are very sensitive to underlying problem setups, and it has been not clear what extent these results can be generalized to. In this paper, we obtain a generic criteria that BP converges to the optimal solution of given LP, and show that it is satisfied in LP formulations associated to many classical combinatorial optimization problems including maximum weight perfect matching, shortest path, traveling salesman, cycle packing, vertex/edge cover and network flow.

In this paper, we study the missing sample recovery problem using methods based on sparse approximation. In this regard, we investigate the algorithms used for solving the inverse problem associated with the restoration of missed samples of image signal. This problem is also known as inpainting in the context of image processing and for this purpose, we suggest an iterative sparse recovery algorithm based on constrained $l_1$-norm minimization with a new fidelity metric. The proposed metric called Convex SIMilarity (CSIM) index, is a simplified version of the Structural SIMilarity (SSIM) index, which is convex and error-sensitive. The optimization problem incorporating this criterion, is then solved via Alternating Direction Method of Multipliers (ADMM). Simulation results show the efficiency of the proposed method for missing sample recovery of 1D patch vectors and inpainting of 2D image signals.

Microsoft's biggest hurdle with Crackdown 3 isn't its rumored troubled development cycle, it's that Agents of Mayhem exists and is coming out first. Both share a similar premise: You're a superpowered human given free reign over a cartoony open world. There are plenty of physics-based shenanigans that result from shooting harpoon rifles at snipers and black hole guns at gang members in both, and each has a familiar structure of taking out a crime syndicate from the bottom up. The difference lies in the execution: There are a lot of cooks working on Crackdown 3 -- some brand new to the franchise -- while Mayhem's team is a group of seasoned open-world veterans. Crackdown 3 is being developed by two studios.