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Bayesian Optimization on Large Graphs via a Graph Convolutional Generative Model: Application in Cardiac Model Personalization
Dhamala, Jwala, Ghimire, Sandesh, Sapp, John L., Horacek, B. Milan, Wang, Linwei
Personalization of cardiac models involves the optimization of organ tissue properties that vary spatially over the non-Euclidean geometry model of the heart. To represent the high-dimensional (HD) unknown of tissue properties, most existing works rely on a low-dimensional (LD) partitioning of the geometrical model. While this exploits the geometry of the heart, it is of limited expressiveness to allow partitioning that is small enough for effective optimization. Recently, a variational auto-encoder (VAE) was utilized as a more expressive generative model to embed the HD optimization into the LD latent space. Its Euclidean nature, however, neglects the rich geometrical information in the heart. In this paper, we present a novel graph convolutional VAE to allow generative modeling of non-Euclidean data, and utilize it to embed Bayesian optimization of large graphs into a small latent space. This approach bridges the gap of previous works by introducing an expressive generative model that is able to incorporate the knowledge of spatial proximity and hierarchical compositionality of the underlying geometry. It further allows transferring of the learned features across different geometries, which was not possible with a regular VAE. We demonstrate these benefits of the presented method in synthetic and real data experiments of estimating tissue excitability in a cardiac electrophysiological model.
Improving the Effectiveness of Traceability Link Recovery using Hierarchical Bayesian Networks
Moran, Kevin, Palacio, David N., Bernal-Cรกrdenas, Carlos, McCrystal, Daniel, Poshyvanyk, Denys, Shenefiel, Chris, Johnson, Jeff
Traceability is a fundamental component of the modern software development process that helps to ensure properly functioning, secure programs. Due to the high cost of manually establishing trace links, researchers have developed automated approaches that draw relationships between pairs of textual software artifacts using similarity measures. However, the effectiveness of such techniques are often limited as they only utilize a single measure of artifact similarity and cannot simultaneously model (implicit and explicit) relationships across groups of diverse development artifacts. In this paper, we illustrate how these limitations can be overcome through the use of a tailored probabilistic model. To this end, we design and implement a HierarchiCal PrObabilistic Model for SoftwarE Traceability (Comet) that is able to infer candidate trace links. Comet is capable of modeling relationships between artifacts by combining the complementary observational prowess of multiple measures of textual similarity. Additionally, our model can holistically incorporate information from a diverse set of sources, including developer feedback and transitive (often implicit) relationships among groups of software artifacts, to improve inference accuracy. We conduct a comprehensive empirical evaluation of Comet that illustrates an improvement over a set of optimally configured baselines of $\approx$14% in the best case and $\approx$5% across all subjects in terms of average precision. The comparative effectiveness of Comet in practice, where optimal configuration is typically not possible, is likely to be higher. Finally, we illustrate Comets potential for practical applicability in a survey with developers from Cisco Systems who used a prototype Comet Jenkins plugin.
USPTO Adds Company to $50M Artificial Intelligence and Machine Learning Contract โ IAM Network
The United States Patent and Trademark Office officially selected a new partner to support its increasing adoption of artificial intelligence and machine learning capabilities.General Dynamics Information Technology on Monday announced it was awarded a contract worth up to $50 million through its Intelligent Automation and Innovation Support Services blanket purchase agreement. GDIT is the latest of more than a dozen companies the agency tapped under the future-facing BPA. Other businesses who've made their own recent announcements detailing partnerships via the agreement include Octo and Steampunk.In the announcement, Vice President & General Manager Christopher Hegedus for GDIT's Diplomacy, Commerce and Government Operations business area noted the company's supported the agency for nearly two decades, and through this "new work, [aims to bring its] AI, ML and robotic process automation expertise to help USPTO develop solutions that accelerate the patent and trademark process to benefit American innovators." Charged with issuing patents for inventions and registering trademarks for product and intellectual property identification, USPTO is making deliberate moves to "propel" itself into the next decade technologically, the agency's chief information officer recently told Nextgov. And it appears the BPA is one avenue helping it to do exactly that.
AI/DC: Artificial Intelligence Writes an AC/DC Song MetalSucks
We still don't know when we'll get to hear actual new AC/DC, but in the meantime, this actually kinda-sorta suffices: a YouTuber known as Funk Turkey put all of the legendary Australian band's lyrics into the program lyrics.rip, The resulting song, "Great Balls," is about as accurate a parody of AC/DC as you're likely to ever hear. And even if you don't agree with that assessment, wellโฆ the lyrics are pretty goddamn funny.
Asia Leading in AI Business Deployment, Personalized Prediction to Combat COVID-19
Asia is leading the pack in AI business deployment compared to less than a third for US companies. The adoption rate in the rest of the world remains low, as firms do not understand the deployment of AIยน in their operations. The surveillance behavior of Chinese firms continues and contravenes privacy. MIT's decision to end its collaboration with iFlytekยนโฐ from China makes sense and will set the trend for other companies. Artificial intelligence does not have to hurt people but rather be ethical, responsible, and accountable.
Keywords Studios steaming ahead with growth plans despite crisis
Keywords Studios PLC (LON:KWS) supplies a range of technical services to computer games developers and publishers. Some of the services it provides include art services, software engineering, audio services, functionality quality assurance (QA), localisation (enabling games to be published in several languages), localisation QA, and player support. Among its clients are Sega, Nintendo, Google, Microsoft and Warner Bros. Established in 1998 it now has studios in more than 42 locations in 20 countries across four continents. Keywords employs a buy-and-build strategy and has been expanding rapidly since its first acquisition in 2014. The list of what Keywords owns is long and will most likely to continue to grow as the company aims to make six small bolt-on acquisitions each year and one or two larger purchases.
Humans and AI: Future Best Friends
It is not that hard to believe, how just two decades ago Deep Blue a computer beat a chess grandmaster Gary Kasparov. AI is enhancing itself and is becoming better at numerous "human" jobs -- diagnosing disease, translating languages, providing customer service -- and it's improving fast. This is raising reasonable fears amongst workers and upcoming students. According to The Guardian, 76% of Americans fear that their job will be lost to AI. While it's speculated AI will take over 1.8 million human jobs by the year 2020, however, the technology is also expected to create a 2.3 million new kinds of jobs, many of which will involve the collaboration between humans and AI.
Scientists are drowning in COVID-19 papers. Can new tools keep them afloat?
Science's COVID-19 reporting is supported by the Pulitzer Center. Timothy Sheahan, a virologist studying COVID-19, wishes he could keep pace with the growing torrent of new scientific papers about the disease and the novel coronavirus that causes it. But there are just too many--more than 4000 alone last week. "I'm not keeping up," says Sheahan, who works at the University of North Carolina, Chapel Hill. A loose-knit army of data scientists, software developers, and journal publishers is pressing hard to change that.
Coronavirus: Author Neil Gaiman's 11,000-mile lockdown trip to Scottish isle
Author Neil Gaiman has admitted breaking Scotland's lockdown rules by travelling 11,000 miles from New Zealand to his holiday home on Skye. The Good Omens and American Gods writer left his wife and son in Auckland so he could "isolate" at his island retreat. He wrote on his online bog: "Hullo from Scotland, where I am in rural lockdown on my own." The science fiction and fantasy author has since been criticised for "endangering" local people". The SNP's Westminster leader Ian Blackford, who is the MP for the island, told the Sunday Times the author's journey was unacceptable. He said: "What is it about people, when they know we are in the middle of lockdown that they think they can come here from the other side of the planet, in turn endangering local people from exposure to this infection that they could have picked up at any step of the way?" Mr Gaiman - whose main family home is in Woodstock in the USA - has owned the house on Skye for more than 10 years. The English-born author wrote on his blog that until two weeks ago he had been living in New Zealand with his wife, the singer Amanda Palmer, and their four-year-old son. He said the couple agreed "that we needed to give each other some space". The 59-year-old said he flew "masked and gloved, from empty Auckland airport" to Los Angeles. He then caught a British Airways flight to London before borrowing a friend's car and heading for Skye. "I drove north, on empty motorways and then on empty roads, and got in about midnight, and I've been here ever since," he said. "I needed to be somewhere I could talk to people in the UK while they and I were awake, not just before breakfast and after dinner.