"The problem of giving rules for producing true scientific statements has been replaced by the problem of finding efficient heuristic rules for culling the reasonable candidates for an explanation from an appropriate set of possible candidates [and finding methods for constructing the candidates]."
– B. Buchanan, quoted in Lindley Darden. Recent Work in Computational Scientific Discovery.
Researches have made a breakthrough discovery about the impulsive electron loss that happens in the Earth's upper atmosphere. A paper on the research was published in the Geophysical Review Letters on Wednesday and details the scientific discoveries two spacecraft made about the loss and its cause, according to NASA. The Cubesat FIREBIRD II was one of those craft that recorded the electron microburst when it happened. The craft observed the microbursts from its place orbiting 310 miles above Earth while one of the Van Allen Probes that orbits a bit higher up was able to capture a rising-tone lower band chorus. That chorus of waves had the duration and cadence highly similar to those of the microburst that the FIREBIRD had captured.
Sometimes it seems surprising that science functions at all. In 2005, medical science was shaken by a paper with the provocative title "Why most published research findings are false."1 Written by John Ioannidis, a professor of medicine at Stanford University, it didn't actually show that any particular result was wrong. Instead, it showed that the statistics of reported positive findings was not consistent with how often one should expect to find them. As Ioannidis concluded more recently, "many published research findings are false or exaggerated, and an estimated 85 percent of research resources are wasted."2
Missing data present significant challenges to trend analysis of time series. Straightforward approaches consisting of supplementing missing data with constant or zero values or with linear trends can severely degrade the quality of the trend analysis, which significantly reduces the reliability of the trend analysis. We present a robust adaptive approach to discover the trends from fragmented time series. The approach proposed in this paper is based on the HASF (Hypothesis-testing-based Adaptive Spline Filtering) trend analysis algorithm, which can accommodate non-uniform sampling and is therefore inherently robust to missing data. HASF adapts the nodes of the spline based on hypothesis testing and variance minimization, which adds to its robustness.
Formulating the Null and the alternate hypothesis for normality test; Choice of null hypothesis based on absence of action and the vice versa for alternate hypothesis; checking for normality in Minitab; interpreting the Q–Q plot; Comparing the computed'p' value with α (alpha) for taking the decision on whether or not to take the action; Step to performing the 1 sample Z test, selection of appropriate hypothesis in minitab.
Google is fully aware of artificial intelligence's (AI) potential -- DeepMind's AlphaGo AI is one of today's most well-known examples of its capabilities -- and in an earnings call this week, the company made it clear they believe the future of technology lies with AI. During the call, Sundar Pichai, CEO of Alphabet (Google's parent company), praised the company's decision to invest in AI early, highlighting the concept's trajectory from "a research project to something that can solve new problems for a billion people a day," according to an Inverse report. Pichai went on to note how Google's AI research is already producing products that utilize machine learning, such as the Google Clips camera that debuted earlier this month. "Even though we are in the early days of AI, we are already rethinking how to build products around machine learning," said Pichai. "It's a new paradigm compared to mobile-first software, and I'm thrilled how Google is leading the way."
I gave a talk about p-values and hypothesis testing at BIDS. Please check out my slides! People take for granted that the tests they use work without justifying the leap from data to model. I gave three examples of hypothesis tests I've developed where standard methods of analysis have failed: testing the adequacy of pseudo-random number generators for statistical simulations, gender bias in student evaluations of teaching, and risk-limiting election auditing.
Back in 2014, CERN released the data from its Large Hadron Collider (LHC) experiments onto an online portal called the Open Data portal. This was the first time results of any particle collider experiment have been released to the public, and now it's produced results. The MIT team was able to show, using CMS data, that the same equation can predict both the pattern of these jets and the energy of the particles produced from a proton collision. Perhaps it will encourage other particle colliders to make their data available as well.
The defense industry is the latest sector to utilize AI. With AI at helm, a central command could launch a multi-pronged attack from land, air, and water simultaneously without any humans on the warfront. The gun autonomously takes it own decision to fire on a target. Another potential drawback is ease of taking decisions to launch an attack when no human combatants are involved.
"It's definitely been a paradigm shift in where you might find life," says Cassini project scientist Linda Spilker. Icy geysers fueled by Enceladus's ocean shoot out from cracks in the moon's surface, allowing the Cassini spacecraft to sample them directly during flybys. But that doesn't mean some exotic form of life couldn't be swimming through Titan's methane lakes. With its own watery geysers, Jupiter's moon Europa is another exciting ocean world outside the Goldilocks zone, and is the subject of a coming NASA mission, called Europa Clipper, planned to launch in the 2020s.
The speed at which any given scientific discipline advances will depend on how well its researchers collaborate with one another, and with technologists, in areas of eScience such as databases, workflow management, visualization, and cloud computing technologies. In The Fourth Paradigm: Data-Intensive Scientific Discovery, the collection of essays expands on the vision of pioneering computer scientist Jim Gray for a new, fourth paradigm of discovery based on data-intensive science and offers insights into how it can be fully realized. "The impact of Jim Gray's thinking is continuing to get people to think in a new way about how data and software are redefining what it means to do science." "I often tell people working in eScience that they aren't in this field because they are visionaries or super-intelligent--it's because they care about science and they are alive now.