Lifeboat Foundation

Scientists have long studied the work of Subrahmanyan Chandrasekhar, the Indian-born American astrophysicist who won the Nobel Prize in 1983, but few know that his research on stellar and planetary dynamics owes a deep debt of gratitude to an almost forgotten woman: Donna DeEtte Elbert.

From 1948 to 1979, Elbert worked as a “computer” for Chandrasekhar, tirelessly devising and solving mathematical equations by hand. Though she shared authorship with the Nobel laureate on 18 papers and Chandrasekhar enthusiastically acknowledged her seminal contributions, her greatest achievement went unrecognized until a postdoctoral scholar at UCLA connected threads in Chandrasekhar’s work that all led back to Elbert.

Elbert’s achievement? Before anyone else, she predicted the conditions argued to be optimal for a planet or star to generate its own magnetic field, said the scholar, Susanne Horn, who has spent half a decade building on Elbert’s work.

Our Machine Learning models show that there are periods where there are earthquakes magnitude ≥7 and periods without earthquakes with magnitude ≥7 in the analyzed seismic zones. In addition, our Machine Learning models predict a new seismically active phase for earthquakes magnitude ≥7 between 2040± 5and 2057 ± 5, 2024 ± 1 and 2026 ± 1, 2026 ± 2 and 2031 ± 2, 2024 ± 2 and 2029 ± 2, and 2022 ± 1 and 2028 ± 2 for the five seismic zones in United States, Mexico, South America, Japan, and Southern China-Northern India, respectively. Finally, we note that our algorithms can be further applied to perform probabilistic forecasts in any seismic zone.

Our algorithm for analyzing strong earthquakes in extensive seismic areas can also be applied to smaller or specific seismic zones where moderate historical earthquakes with magnitudes between 5 and 7 occur, as is the case of the Parkfield section of the San Andreas fault (California, United States). Our analysis shows why a moderate earthquake could never occur in 1988 ± 5 as proposed by Bakun and Lindh (1985) and why the long-awaited characteristic Parkfield earthquake occurred in 2004. Furthermore, our Bayesian model of Machine Learning adopting a periodicity of 35 years predicts that possible seismic events may occur between 2019 and 2031, with a high probability of event(s) around 2025 ± 2. The Parkfield section of the San Andreas fault is an excellent seismic laboratory for developing, testing, and demonstrating earthquake forecasts. In a few years, it will be possible to demonstrate whether our algorithm effectively forecasts strong and moderate earthquakes.

The algorithm correctly associated a whoop bout with its hyena around 54 percent of the time.

Scientists from the University of Nebraska, Lincoln, U.S. have discovered that Hyenas’ whoops have specific signals unique to each individual animal.

The researchers determined that hyena whoops have specific characteristics that can be attributed to each individual animal by using machine learning on audio files collected from a field trip, according to a press release published by EurekAlert on Saturday.

Recent advancements in the development of machine learning and optimization techniques have opened new and exciting possibilities for identifying suitable molecular designs, compounds, and chemical candidates for different applications. Optimization techniques, some of which are based on machine learning algorithms, are powerful tools that can be used to select optimal solutions for a given problem among a typically large set of possibilities.

Researchers at Colorado State University and the National Renewable Energy Laboratory have been applying state-of-the-art molecular optimization models to different real-world problems that entail identifying new and promising molecular designs. In their most recent study, featured in Nature Machine Intelligence, they specifically applied a newly developed, open-source optimization framework to the task of identifying viable organic radicals for aqueous redox flow batteries, energy devices that convert chemical energy into electricity.

“Our project was funded by an ARPA-E program that was looking to shorten how long it takes to develop new energy materials using machine learning techniques,” Peter C. St. John, one of the researchers who carried out the study, told TechXplore. “Finding new candidates for redox flow batteries was an interesting extension of some of our previous work, including a paper published in Nature Communications and another in Scientific Data, both looking at organic radicals.”

When quantum computers become powerful enough, they could theoretically crack the encryption algorithms that keep us safe. The race is on to find new ones.

Master’s Theorem is the best method to quickly find the algorithm’s time complexity from its recurrence relation. This theorem can be applied to decreasing as well as dividing functions, each of which we’ll be looking into detail ahead.

The equations of quantum mechanics provide a roadmap to predicting the properties of chemicals starting from basic scientific theories. However, these equations quickly become too expensive in terms of computer time and power when used to predict behavior in large systems. Machine learning offers a promising approach to accelerating such large-scale simulations.

Researchers have shown that machine learning models can mimic the basic structure of the fundamental laws of nature. These laws can be very difficult to simulate directly. The machine learning approach enables predictions that are easy to compute and are accurate in a wide range of chemical systems.

The improved machine learning model can quickly and accurately predict a wide range of properties of molecules (Proceedings of the National Academy of Sciences, “Deep Learning of Dynamically Responsive Chemical Hamiltonians with Semi-Empirical Quantum Mechanics”). These approaches score very well on important benchmarks in computational chemistry and show how deep learning methods can continue to improve by incorporating more data from experiments. The model can also succeed at challenging tasks such as predicting excited state dynamics—how systems behave with elevated energy levels.

In recent years, engineers and computer scientists have created a wide range of technological tools that can enhance fitness training experiences, including smart watches, fitness trackers, sweat-resistant earphones or headphones, smart home gym equipment and smartphone applications. New state-of-the-art computational models, particularly deep learning algorithms, have the potential to improve these tools further, so that they can better meet the needs of individual users.

Researchers at University of Brescia in Italy have recently developed a computer vision system for a smart mirror that could improve the effectiveness of fitness training both in home and gym environments. This system, introduced in a paper published by the International Society of Biomechanics in Sports, is based on a deep learning algorithm trained to recognize human gestures in video recordings.

Continue reading “A deep learning-augmented smart mirror to enhance fitness training” »

How can mobile robots perceive and understand the environment correctly, even if parts of the environment are occluded by other objects? This is a key question that must be solved for self-driving vehicles to safely navigate in large crowded cities. While humans can imagine complete physical structures of objects even when they are partially occluded, existing artificial intelligence (AI) algorithms that enable robots and self-driving vehicles to perceive their environment do not have this capability.

Robots with AI can already find their way around and navigate on their own once they have learned what their environment looks like. However, perceiving the entire structure of objects when they are partially hidden, such as people in crowds or vehicles in traffic jams, has been a significant challenge. A major step towards solving this problem has now been taken by Freiburg robotics researchers Prof. Dr. Abhinav Valada and Ph.D. student Rohit Mohan from the Robot Learning Lab at the University of Freiburg, which they have presented in two joint publications.

The two Freiburg scientists have developed the amodal panoptic segmentation task and demonstrated its feasibility using novel AI approaches. Until now, self-driving vehicles have used panoptic segmentation to understand their surroundings.