Reconstructing the causal network in a complex dynamical system plays a crucial role in many applications, from sub-cellular biology to economic systems. Here we focus on inferring gene regulation networks (GRNs) from perturbation or gene deletion experiments. Despite their scientific merit, such perturbation experiments are not often used for such inference due to their costly experimental procedure, requiring significant resources to complete the measurement of every single experiment. To overcome this challenge, we develop the Robust IDentification of Sparse networks (RIDS) method that reconstructs the GRN from a small number of perturbation experiments. Our method uses the gene expression data observed in each experiment and translates that into a steady state condition of the system's nonlinear interaction dynamics. Applying a sparse optimization criterion, we are able to extract the parameters of the underlying weighted network, even from very few experiments. In fact, we demonstrate analytically that, under certain conditions, the GRN can be perfectly reconstructed using $K = \Omega (d_{max})$ perturbation experiments, where $d_{max}$ is the maximum in-degree of the GRN, a small value for realistic sparse networks, indicating that RIDS can achieve high performance with a scalable number of experiments. We test our method on both synthetic and experimental data extracted from the DREAM5 network inference challenge. We show that the RIDS achieves superior performance compared to the state-of-the-art methods, while requiring as few as ~60% less experimental data. Moreover, as opposed to almost all competing methods, RIDS allows us to infer the directionality of the GRN links, allowing us to infer empirical GRNs, without relying on the commonly provided list of transcription factors.

And while the word is out that law enforcement agencies from California to New York have used the devices to monitor citizens for years, a new report (PDF) from the bipartisan House Oversight and Government Reform Committee shows that the rules governing their usage can vary greatly from state to state or even department to department. As a result, committee chairman Jason Chaffetz (R-UT) and member Elijah Cummings (D-MD) are calling on Congress to establish "a clear, nationwide framework that ensures the privacy of all Americans are adequately protected." The committee has already pushed the Department of Justice, the Department of Homeland Security and the IRS to require a warrant before deploying a Stingray or other similar cell network-spoofing device, but the report's findings showed that in many states law enforcement agencies don't even need probable cause in order to justify their usage. To remedy that situation, the report recommends that Congress pass clear rules about "when and how geolocation information can be accessed and used." The DOJ and DHS will then be responsible for requiring local law enforcement agencies to adopt the framework before they can receive federal funding for Stingray devices like the ones that violated FCC regulations in Baltimore.

This is the first installment in a three-part review of 2016 in machine learning and deep learning. In Part Two, we cover developments in each of the leading open source machine learning and deep learning projects. Part Three will review the machine learning and deep learning moves of commercial software vendors. As organizations expand the use of machine learning for profiling and automated decisions, there is growing concern about the potential for bias. In 2016, reports in the media documented racial bias in predictive models used for criminal sentencing, discriminatory pricing in automated auto insurance quotes, an image classifier that learned "whiteness" as an attribute of beauty, and hidden stereotypes in Google's word2vec algorithm.

To ramp up its contribution to global fights against money laundering and terrorism financing, South Korea will introduce an artificial intelligence-based system to better filter out financial crimes, said the country's financial intelligence chief. Yoo Kwang-yeol, commissioner of the Korea Financial Intelligence Unit, said his agency is currently working to upgrade the main system that stores and analyzes information regarding hundreds of millions of financial transactions in order to increase accuracy of capturing suspicious transactions out of normal ones. Yoo Kwang-yeol, commissioner of Korea Financial Intelligence Unit speaks during an interview at his office in Gwanghwamun, central Seoul, Dec. 6. For this, a group of KOFIU experts paid a trip to Australia earlier this month to learn from the Australian financial intelligence system. "AI can help improve efficiency of sorting out suspicious financial transactions and accuracy of analyzing related account information," Yoo said.

The Canadian firm is set to invest C$100 million ($75 million) in a new autonomous vehicle-testing hub over several years, the company's chief executive said on Monday, marking a change of direction for the smartphone pioneer. Apple is said to have taken the unusual step of working on its car software far away from its California HQ. QNX, which was bought out by Blackberry in 2010, is known for producing car software. What is REALLY going on in North Korea? 'Explosion' thought... From reacting to a bad gift to buying the perfect present:... Facebook Messenger FINALLY unveils group video calls that... Look up! FOUR asteroids are set to make a'close approach'... What is REALLY going on in North Korea?

Professor Viren Swami, a social psychology expert explains the five things you need to know about giving and receiving gifts He says the best presents are'giver-centric' rather than'recipient-centric' If you're given a present you don't like, the best way to diffuse the situation is just to say'thank you' even if you don't mean it He says the best presents are'giver-centric' rather than'recipient-centric' If you're given a present you don't like, the best way to diffuse the situation is just to say'thank you' even if you don't mean it We've all been there - you excitedly tear into a present, only to be met with yet another pair of socks. But what is the best way to react to a gift you really don't want? Police trial'Big Brother' AI system that is so powerful it... Virtual dating simulator that teaches men how to pick up... Terrifying 13ft Avatar robot takes its first steps:... Santa ISN'T sexist if he gives your daughter a doll:... Police trial'Big Brother' AI system that is so powerful it... Virtual dating simulator that teaches men how to pick up... Terrifying 13ft Avatar robot takes its first steps:... Santa ISN'T sexist if he gives your daughter a doll:... In a series of studies, researchers found that most people think recipient-centric gifts are preferred. Surprise gifts aren't always a good idea, especially if its not on their wish list But something less physical, such as tickets to a show, could bring more enjoyment in the long term Socially responsible gifts, such as charity donations in someone's name, may not leave someone feeling'warm' towards you Practical gifts can be great because the receiver will get use out of them Put yourself in the person's shoes and think about what they might get out of a gift in the long term Surprise gifts aren't always a good idea, especially if its not on their wish list Socially responsible gifts, such as charity donations in someone's name, may not leave someone feeling'warm' towards you Put yourself in the person's shoes and think about what they might get out of a gift in the long term If you do receive a poor gift, one way of defusing the situation is simply to say'thank you' (stock image) Christmas is culturally perceived as a time of giving and spending more of one's income on others, which is associated with greater levels of happiness than spending money on oneself At over seven feet tall, is Freddy Britain's biggest dog?

As growing legions of graying Wall Streeters worry that technology is about to make them obsolete, the industry's most junior staffers are almost unanimously expecting brighter days, according to an Options Group survey. The recruiting firm asked more than 3,200 traders, salespeople and other finance professionals how advances over the next five years will impact them. In every bracket age 26 and up, at least 8 percent of respondents predicted they will lose their livelihood. But virtually nobody younger checked that box. Instead, their most popular answer was "better work-life balance."

The rules of the game called psychological science.

Recently, I have been working on the Neural Networks for Machine Learning course offered by Coursera and taught by Geoffrey Hinton. Overall, it is a nice course and provides an introduction to some of the modern topics in deep learning. However, there are instances where the student has to do lots of extra work in order to understand the topics covered in full detail. One of the assignments in the course is to study the Neural Probabilistic Language Model (The related article can be downloaded from here). An example dataset, as well as a code written in Octave (equivalently Matlab) are provided for the assignment.

Lossy compression and clustering fundamentally involve a decision about what features are relevant and which are not. The information bottleneck method (IB) by Tishby, Pereira, and Bialek formalized this notion as an information-theoretic optimization problem and proposed an optimal tradeoff between throwing away as many bits as possible, and selectively keeping those that are most important. In the IB, compression is measure my mutual information. Here, we introduce an alternative formulation that replaces mutual information with entropy, which we call the deterministic information bottleneck (DIB), that we argue better captures this notion of compression. As suggested by its name, the solution to the DIB problem turns out to be a deterministic encoder, or hard clustering, as opposed to the stochastic encoder, or soft clustering, that is optimal under the IB. We compare the IB and DIB on synthetic data, showing that the IB and DIB perform similarly in terms of the IB cost function, but that the DIB significantly outperforms the IB in terms of the DIB cost function. We also empirically find that the DIB offers a considerable gain in computational efficiency over the IB, over a range of convergence parameters. Our derivation of the DIB also suggests a method for continuously interpolating between the soft clustering of the IB and the hard clustering of the DIB.