Local network community detection is the task of finding a single community of nodes concentrated around few given seed nodes in a localized way. Conductance is a popular objective function used in many algorithms for local community detection. This paper studies a continuous relaxation of conductance. We show that continuous optimization of this objective still leads to discrete communities. We investigate the relation of conductance with weighted kernel k-means for a single community, which leads to the introduction of a new objective function, $\sigma$-conductance. Conductance is obtained by setting $\sigma$ to $0$. Two algorithms, EMc and PGDc, are proposed to locally optimize $\sigma$-conductance and automatically tune the parameter $\sigma$. They are based on expectation maximization and projected gradient descent, respectively. We prove locality and give performance guarantees for EMc and PGDc for a class of dense and well separated communities centered around the seeds. Experiments are conducted on networks with ground-truth communities, comparing to state-of-the-art graph diffusion algorithms for conductance optimization. On large graphs, results indicate that EMc and PGDc stay localized and produce communities most similar to the ground, while graph diffusion algorithms generate large communities of lower quality.

The amount of data in pathology images has previously been too vast for researchers to process easily, but that's changing thanks to advanced machine learning. A group of researchers from Stanford University were able to more accurately predict lung cancer prognoses by grabbing images from the Cancer Genome Atlas from patients with the disease, and then through those train a computer software program to pinpoint characteristics in the images previously unable to be seen by the human eye, according to an announcement. Their research was published in Nature Communications. Once the researchers could home in on those specific characteristics, they were able to figure out the cancer subtype, as well as how long a patient would live with that diagnosis. "Ultimately this technique will give us insight into the molecular mechanisms of cancer by connecting important pathological features with outcome data," Michael Snyder, Ph.D., a professor and chair of genetics at Stanford, said in the announcement.

It's been called the'sexiest job of the 21st century', the'hottest job of the decade', and is the fastest-growing field in tech at the moment – the impact of Data Science in today's world cannot be overstated. As a discipline, data science involves the collection and study of data – both structured and unstructured – to gain insights and information that can be used by organizations to devise effective strategies. By collating data over a period of time, patterns can be identified that enable companies to find new market opportunities, enhance efficiency, reduce costs, and place themselves at a competitive advantage in their industry. Due to rapid technological advances, especially in areas like mobile advertising, social media, and website personalization, a massive amount of data is being generated on a daily basis. These data volumes have resulted in industries having to become data-savvy & adapt to the new landscape – or risk falling behind the competition.

This is the first in Blue Hill Research's occasional blog series "Questioning Authority with Toph Whitmore." As co-founder (with friend Michael Stonebraker) of Vertica, Andy Palmer ambitiously sought nothing less than to reinvent the database. In 2013, he and Stonebraker moved up the data value chain and founded Tamr, the Cambridge, MA-based software company aiming to provide a unified view of data in the modern enterprise. Palmer joined me for a discussion in which he talked Tamr, predicted the future of enterprise data management, and introduced a rather colorful (yet apt) analogy of which, he admits, his marketing team is less than fond. TOPH WHITMORE: Tell me about the genesis of Tamr.

Forecasting the future isn't easy but some things are predictable. It is predictable, for example, that every now and then everyone will get excited about the imminent arrival of machines that think like people and that will therefore destroy people's jobs. This triggers a mix of enthusiasm and paranoia. But after the humanoid machines fail to materialize as predicted, everyone calms down and gets back to work. In 2016, we are approaching the end of one of those periodic bouts of excitement.

The U.K.'s Brexit vote was something few prognosticators saw coming prior to the June 23 referendum. But once the results were in, it was clear the vote to leave the European Union would have a major impact on financial markets. The pound sterling fell in value by 11% two days after the vote, and both the Dow Jones Industrial Average and the London Stock Exchange's FTSE 100 index lost more than 2% of their total value. This left millions of traders all over the world scrambling to find safer investment positions. But at least one group of investors was relatively calm, according to Omer Cedar, CEO of Omega Point Research Inc., a New York-based software company that sells analytics tools to help investment managers review their portfolios for risks.

At an artificial intelligence conference in New York City last week, Professor Alexi Samsonovich from the Moscow-based National Research Nuclear University (MEPhl) Cybernetics Department told Sputnik News, "We are on the verge of a major breakthrough" in AI. In the past six months, we've seen AI master the board game Go, write a short film script, and infiltrate Snapchat filters. Each of these achievements is impressive in its own right. Together, they show just how quickly AI is advancing. But what was this breakthrough Samsonovich hinted at in NYC? Digital Trends reached out to him to find out.

It doesn't take an expert to confirm that people are spending more time on the internet. For those interested in exact quantitative measures, consider this: a Pew Research Center survey found one-fifth of Americans self-report going online "almost constantly" and 73 percent admitting they go online on a daily basis. Now, researchers from the University of California, Santa Cruz and University of Illinois, Urbana Champaign have found how an increased dependency on the internet impacts our problem solving abilities, recall and learning. The study, published in the journal Memory, looked at the odds of a person reaching out to use a device as an aide tool when answering questions. The researchers divided participants into two groups: one that used their memory and another that used Google.

Ford today announces its intent to have a high-volume, fully autonomous SAE level 4-capable vehicle in commercial operation in 2021 in a ride-hailing or ride-sharing service. This Smart News Release features multimedia. Ford has been researching autonomous vehicles for more than a decade, and intends to have a high-volume, fully autonomous SAE level-4-capable vehicle in commercial operation for ride sharing services in 2021. Ford currently tests fully autonomous vehicles in Michigan, Arizona and California, and will triple its autonomous test fleet this year to have the largest test fleet of any automaker. To get there, the company is investing in or collaborating with four startups to enhance its autonomous vehicle development, doubling its Silicon Valley team and more than doubling its Palo Alto campus.

Google is pulling another lever on its influential search engine in an effort to boost voter turnout in November's U.S. presidential election. Beginning Tuesday, Google will provide a summary box detailing state voting laws at the top of the search results whenever a user appears to be looking for that information. The breakdown will focus on the rules particular to the state where the search request originates unless a user asks for another location. Google is introducing the how-to-vote instructions a month after it unveiled a similar feature that explains how to register to vote in states across the U.S. The search giant said its campaign is driven by rabid public interest in the presidential race between Hillary Clinton and Donald Trump. As of last week, it said, the volume of search requests tied to the election, the candidates and key campaign issues had more than quadrupled compared to a similar point in the 2012 presidential race.