The performance of land surface models (LSMs) strongly depends on their unknown parameter variables so that it is necessary to optimize them. Here I present a globally applicable and computationally efficient method for parameter optimization and uncertainty assessment of the LSM by combining Markov Chain Monte Carlo (MCMC) with machine learning. First, I performed the long-term ensemble simulation of the LSM, in which each ensemble member has different parameters' variables, and calculated the gap between simulation and observation, or the cost function, for each ensemble member. Second, I developed the statistical machine learning based surrogate model, which is computationally cheap but accurately mimics the relationship between parameters and the cost function, by applying the Gaussian process regression to learn the model simulation. Third, we applied MCMC by repeatedly driving the surrogate model to get the posterior probabilistic distribution of parameters. Using satellite passive microwave brightness temperature observations, both synthetic and real-data experiments were performed to optimize unknown soil and vegetation parameters of the LSM. The primary findings are (1) the proposed method is 50,000 times as fast as the direct application of MCMC to the full LSM; (2) the skill of the LSM to simulate both soil moisture and vegetation dynamics can be improved; (3) I successfully quantify the characteristics of equifinality by obtaining the full non-parametric probabilistic distribution of parameters.

Recognizing the pervasivetalent gapthat exists between data scientists and data workers in the line of business, Assisted Modeling helps teach data science with a guided walk-through and aims to help all data workers, regardless of technical acumen, advance their skill sets in the process of building machine learning models. Our approach in building Assisted Modeling is to advance the skills of the data worker, creating next-level citizen data scientists capable of building the machine learning models required to tackle the advanced analytic challenges of the future. Assisted Modeling provides users the transparency and control needed to build trustworthy machine learning models that drive business outcomes without writing a line of code. As an output of the application, users can access code-free machine learning tools directly within the Alteryx Designer interface. Assisted Modeling allows any data worker to construct machine learning models, understand how and why their models work, and capture modeling decisions, turning raw data into informed business decisions with unprecedented speed and confidence.

Government License: The submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory ("Argonne"). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan.

Change detection in heterogeneous multitemporal satellite images is an emerging and challenging topic in remote sensing. In particular, one of the main challenges is to tackle the problem in an unsupervised manner. In this paper we propose an unsupervised framework for bitemporal heterogeneous change detection based on the comparison of affinity matrices and image regression. First, our method quantifies the similarity of affinity matrices computed from co-located image patches in the two images. This is done to automatically identify pixels that are likely to be unchanged. With the identified pixels as pseudo-training data, we learn a transformation to map the first image to the domain of the other image, and vice versa. Four regression methods are selected to carry out the transformation: Gaussian process regression, support vector regression, random forest regression, and a recently proposed kernel regression method called homogeneous pixel transformation. To evaluate the potentials and limitations of our framework, and also the benefits and disadvantages of each regression method, we perform experiments on two real data sets. The results indicate that the comparison of the affinity matrices can already be considered a change detection method by itself. However, image regression is shown to improve the results obtained by the previous step alone and produces accurate change detection maps despite of the heterogeneity of the multitemporal input data. Notably, the random forest regression approach excels by achieving similar accuracy as the other methods, but with a significantly lower computational cost and with fast and robust tuning of hyperparameters.

The Department of Energy and the Weill Family Foundation on Monday signed a memorandum of understanding for a new public-private partnership which will use the agency's artificial intelligence capabilities for biomedical research. Former Citigroup CEO Sandy Weill and Energy Secretary Rick Perry told CNBC's "Closing Bell" in an exclusive interview that the partnership aims to improve the diagnosis of brain diseases and neurological disorders, which would result in more effective treatment. "As one looks at science and the development of new science, especially AI and computing, you really need to have partners to make things work." "It's too expensive for anybody to try to do it by themselves." The partnership between the federal government and the Weill Family Foundation, involves Lawrence Livermore, Lawrence Berkeley and Argonne National laboratories, and was announced during a roundtable on DOE-fueled artificial intelligence at Lawrence Livermore Lab in Livermore, California.

Homeowners often invest in energy-saving upgrades to make their homes more comfortable and lower their expenses, hoping to see reductions in their upcoming utility bills. Government-backed and utility-backed programs that provide energy-efficient home improvements share the same goal of reducing costs. But measuring the costs and effects of hundreds of different retrofits in thousands of households is a complex process, and the big picture -- which changes should be prioritized for the biggest benefit to the resident -- is difficult to put together. While energy efficiency programs have developed sophisticated models to improve decision making, documented disparities between predicted and realized savings demonstrate that there is still substantial progress that could be made. At the University of Illinois at Urbana-Champaign's National Center for Supercomputing Applications (NCSA), researchers are used to using compute power to dig for answers in piles of untamed data.

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In a recent manufacturing industry insights survey on artificial intelligence (AI), 44 percent of respondents from the automotive and manufacturing sectors classified AI as "highly important" to the manufacturing function in the next five years, while almost half--49 percent--said it was "absolutely critical to success." Yet, in many cases, AI is hard to comprehend for manufacturers, as the technology industry has painted it with such a wide brush that few actually understand how it becomes instantiated--beyond some omnipotent source delivering better business results. Manufacturers may actually view AI as highly complex and expensive, requiring end-to-end systems throughout their whole company to work properly, and this translates to a costly overhaul of their entire IT/OT operation. The reality is, AI is much more focused and achievable. AI can work on factory floors with minimal construction and get connected to machines via the Industrial Internet of Things (IIoT).

SOMEBODY LESS driven than Tom Siebel would have long since thrown in the towel. In 2006 the entrepreneur, then 53 years old, sold his first firm, Siebel Systems, which made computer programs to track customer relations, to Oracle, a giant of business software. That left him a billionaire--but a restless one. In 2009, a few months after Mr Siebel had launched a new startup, he was trampled by an elephant while on safari in Tanzania. When, a dozen surgeries later, he could work again, the enterprise almost went bankrupt.

If a picture is worth a thousand words, but still missing valuable location data, then why not use artificial intelligence (AI) and machine learning (ML) to fill in the gaps? This graphic below shows vast data sets containing buildings, green spaces, roads to travel, and parking lots. Drill down even further and see rooftops, solar panels, fire hydrants, gas lines, and many other objects. And all this data constantly changes over time. City residents move, purchase new developments for their homes, drive roads with new potholes, and build new construction.