Lifeboat Foundation

EPFL scientists have developed a digital model of the fruit fly, Drosophila melanogaster, that realistically simulates the movements of the animal. The twin is a big step towards reverse engineering the neuromechanical control of animal behavior, and developing bioinspired robots.

“We used two kinds of data to build NeuroMechFly,” says Professor Pavan Ramdya at EPFL’s School of Life Sciences. “First, we took a real fly and performed a CT scan to build a morphologically realistic biomechanical model. The second source of data were the real limb movements of the fly, obtained using pose estimation software that we’ve developed in the last couple of years that allow us to precisely track the movements of the animal.”

Ramdya’s group, working with the group of Professor Auke Ijspeert at EPFL’s Biorobotics Laboratory, has published a paper in Nature Methods showcasing the first ever accurate “digital twin” of the fly Drosophila melanogaster, dubbed “NeuroMechFly”.

Recent technological advances have enabled the creation of increasingly sophisticated sensors that can track movements and changes in real-world environments with remarkable levels of precision. Many engineers are now working to make these sensors thinner so that they can be embedded in a variety of devices, including robotic limbs and wearable devices.

Researchers at Hong Kong University of Science and Technology have recently developed a thin sensor for computer vision applications, which is based on a micro lens array (MLA). MLAs are 1D or 2D arrays comprising several small lenses, which are generally arranged in either squared or hexagonal patterns.

“In this study, we combined an old technology, a micro lens array, with vision-based tactile sensors,” Xia Chen, one of the researchers who carried out the study, told TechXplore. “This work builds on the work using the pinhole arrays to capture the image. We wanted to achieve a thin-format vision-based tactile sensor, as few studies so far focused on changing the imaging system of vison-based tactile sensor.”

The manipulation of electromagnetic waves and information has become an important part of our everyday lives. Intelligent metasurfaces have emerged as smart platforms for automating the control of wave-information-matter interactions without manual intervention. They evolved from engineered composite materials, including metamaterials and metasurfaces. As a society, we have seen significant progress in the development of metamaterials and metasurfaces of various forms and properties.

In a paper published in the journal eLight on May 6, 2022, Professor Tie Jun Cui of Southeast University and Professor Lianlin Li of Peking University led a research team to review intelligent metasurfaces. “Intelligent metasurfaces: Control, Communication and Computing” investigated the development of intelligent metasurfaces with an eye for the future.

This field has refreshed human insights into many fundamental laws. They have unlocked many novel devices and systems, like cloaking, tunneling, and holograms. Conventional structure-alone or passive metasurfaces has moved towards intelligent metasurfaces by integrating algorithms and nonlinear materials (or active devices).

A technological demonstration from China recently presented the power of super drones that track objects and people with high precision. The remote-powered vehicles, developed by scholars from Zhejiang University, were deployed into a thick bamboo forest to test their capabilities.

A video released by the researchers shows that the drones maneuvered effectively over the complex obstacles of the forest. The demonstration of the machines creeped out many audiences, as the precision and navigation of the drones exceeded far more than those of the technologies we see today.

Continue reading “Palm-Sized Drone With Flight Performance Like in Sci-Fi Films Can Attack Humans in Pack [WATCH]” »

The latest “machine scientist” algorithms can take in data on dark matter, dividing cells, turbulence, and other situations too complicated for humans to understand and provide an equation capturing the essence of what’s going on.

Despite rediscovering Kepler’s third law and other textbook classics, BACON remained something of a curiosity in an era of limited computing power. Researchers still had to analyze most data sets by hand, or eventually with Excel-like software that found the best fit for a simple data set when given a specific class of equation. The notion that an algorithm could find the correct model for describing any data set lay dormant until 2009, when Lipson and Michael Schmidt, roboticists then at Cornell University, developed an algorithm called Eureqa.

Their main goal had been to build a machine that could boil down expansive data sets with column after column of variables to an equation involving the few variables that actually matter. “The equation might end up having four variables, but you don’t know in advance which ones,” Lipson said. “You throw at it everything and the kitchen sink. Maybe the weather is important. Maybe the number of dentists per square mile is important.”

Continue reading “‘Machine Scientists’ Distill the Laws of Physics From Raw Data” »

The freighter will help get China’s Tianhe core module ready for a new crewed mission.

China has launched a new cargo mission to its space station module in preparation for the arrival of a new crew in June.

A Long March 7 rocket carrying the robotic Tianzhou 4 spacecraft lifted off from Wenchang Satellite Launch Center in southern China’s Hainan Province today (May 9) at 1:56 p.m. EDT (1756 GMT; 1:56 a.m. local time on May 10).

Giving robots sight and hearing is fairly straightforward these days, but equipping them with a robust sense of touch is far more difficult.

As ML aficionados, we’ve all come across interesting projects on GitHub only to discover that they are not in the framework we want and are familiar with. It can be tedious at times to reimplement the whole codebase in our framework, let alone deal with any errors that may arise throughout the process. It is a tedious chore that no one wants to do. Isn’t it good to have something that doesn’t care what framework you’re using? It will provide you with code in your desired framework, whether it is JAX, PyTorch, MXNet, Numpy, or TensorFlow. This is what IVY is attempting to do by unifying all ML frameworks.

The number of open-source machine learning projects has surged significantly over the past. This is evident by the fast-growing number of Github repositories using the keyword Deep learning. Because of different frameworks, code sharability has been considerably hampered. Aside from that, many frameworks become obsolete in comparison to newer frameworks. For software development where collaboration is vital, this is a significant bottleneck. As newer frameworks come into the scene framework-specific code quickly becomes obsolete, and transferring code across frameworks is akin to reinventing the wheel.

In today’s collaborative environment, it is vital to find a common level of abstraction. The development of IVY began with the language, with Python emerging as the clear choice we go further into Python frameworks, and we see that they all operate on the same fundamental principles. A tensor can be manipulated in a variety of ways, but the core tensor operations are constant across frameworks. As a result, IVY was formed as a basic abstraction layer.