Researchers at the U.S. Department of Energy's (DOE) Argonne National Laboratory have developed a new AI-based framework that can produce X-ray images in 3D. The team, which includes members from three divisions at Argonne, has developed a method to create 3D visualizations from X-ray data. Their efforts were meant to allow them to better use the Advanced Photon Source (APS) at their lab, but potential applications of this technology range from astronomy to electron microscopy. Lab tests showed that the new approach, called 3D-CDI-NN, can create 3D visualizations from data hundreds of times faster than existing technology. "In order to make full use of what the upgraded APS will be capable of, we have to reinvent data analytics. Our current methods are not enough to keep up. Machine learning can make full use and go beyond what is currently possible," says Mathew Cherukara of the Argonne National Laboratory, corresponding author of the paper.

Computers have been able to quickly process 2D images for some time. Your cell phone can snap digital photographs and manipulate them in a number of ways. Much more difficult, however, is processing an image in three dimensions, and doing it in a timely manner. The mathematics are more complex, and crunching those numbers, even on a supercomputer, takes time. That's the challenge a group of scientists from the U.S. Department of Energy's (DOE) Argonne National Laboratory is working to overcome.

Shanghai Electric ("Shanghai Electric" or "the Company") (601727.SS and 02727.HK) shared the same stage with global companies for the Industry Intelligence Conference at the World Artificial Intelligence Conference 2021 (WAIC 2021) in Shanghai recently to discuss the benefits of industry intelligence and digital transformation. The annual Shanghai-based tech fest invites tech gurus and industry insiders to unlock insights, analysis and trends to shed light on topics such as staying ahead of change, supporting industries in AI integration, helping global elites and leaders to make better-informed decisions in an era defined by ever-evolving digital technologies. At WAIC 2021, Shanghai Electric also reached a strategic partnership agreement with companies involved in the AI Investment Fund, which was launched in 2019 as part of an initiative of the Shanghai municipal government to support the city's pledge to build a "first-class AI innovation ecosystem." The cooperation will see Shanghai Electric providing digital solutions to facilitate the upgrade and transformation of smart factories, industrial parks and medical institutions in Shanghai. During an Industry Intelligence Conference panel discussion, Ms. Yang Hong, Vice President of Shanghai Electric, said that the meaning of digital transformation rests on its tremendous potential in boosting efficiency and product quality and reducing operational expense.

It is a well-known fact that the power sector is one of the most diversified sectors in the world. It is on the verge of a massive challenge or change in the nearby future. It all depends on how the power sector companies decide on the area of operations, protection of the environment, and many more other factors. The power sector needs to focus on environmental protection as well as updated cutting-edge technologies to modify traditional business policies. There are some international power sector trends that are going on amidst the COVID-19 pandemic such as a massive drop in electricity usage in industrial sectors, faster adoption of solar panels, flexibility for electricity security, and so on.

We conducted experiments to verify the robustness of our calibration procedure based on polynomial fitting. We replicated the process by taking 250 randomly picked times from the learning set. Finally, we explore the frequencies of mean and SD error as shown in Figure 15. Overall, the highest frequencies of both SBP and DBP mean error falls between 4 and 5 mmHg, which satisfies AAMI standards. Similarly, the highest frequency of SD errors is less than 8 mmHg, which also qualifies the AAMI protocol. In addition, 9 out of 35 candidates proceed 10 times of data collection to calculate the intraclass correlation coefficient (ICC). Figure 16 shows the ICC result of each candidate. The average ICC of SBP and DBP are 0.8 and 0.76, respectively.

Each subfield has its own culture and design goals. They both contribute to features that matter to users, but often to different sets of features. The PL community has deep expertise in developing modular, reusable abstractions. The HCI community has deep expertise in developing abstractions that are easy to learn or match the existing mental models of their target users. With rich histories of abstraction design across both fields, a union of these forms of expertise holds the promise of delivering useful, usable, and powerful abstractions.

In Proc. of the 2016 Conf. on Embedded Real Time Softw. and Systems.

Message-passing algorithms such as belief propagation (BP) are parallel computing schemes that try to estimate the marginals of a high dimensional probability distribution. They are used in various areas involving the statistics of a large number of interacting random variables, such as computational thermodynamics [5, 10], artificial intelligence [11, 21, 15], computer vision [18] and communications processing [3, 4]. We have shown the existence of a non-linear correspondence between BP algorithms and discrete integrators of a new form of continuous-time diffusion equations on belief networks [13, 14].

Knowledge Graph (KG) and attention mechanism have been demonstrated effective in introducing and selecting useful information for weakly supervised methods. However, only qualitative analysis and ablation study are provided as evidence. In this paper, we contribute a dataset and propose a paradigm to quantitatively evaluate the effect of attention and KG on bag-level relation extraction (RE). We find that (1) higher attention accuracy may lead to worse performance as it may harm the model's ability to extract entity mention features; (2) the performance of attention is largely influenced by various noise distribution patterns, which is closely related to real-world datasets; (3) KG-enhanced attention indeed improves RE performance, while not through enhanced attention but by incorporating entity prior; and (4) attention mechanism may exacerbate the issue of insufficient training data. Based on these findings, we show that a straightforward variant of RE model can achieve significant improvements (6% AUC on average) on two real-world datasets as compared with three state-of-the-art baselines. Our codes and datasets are available at https://github.com/zig-kwin-hu/how-KG-ATT-help.

"AI is an instrument just like anything else. You can do harm and you can do wonderful things. ESG is the embodiment of all the good things you can do with AI. Squeeze all the juice out of AI but at the same time we need to understand the consequences so we can do things responsibly!" The wise words from Aiko Yamashita, Senior Data Scientist at the Advanced Analytics Centre of Excellence in DNB Bank, during our conversation on Altair's'Future Says'.