Lifeboat Foundation

Quantum computers, systems that perform computations by exploiting quantum mechanics phenomena, could help to efficiently tackle several complex tasks, including so-called combinatorial optimization problems. These are problems that entail identifying the optimal combination of variables among several options and under a series of constraints.

Quantum computers that can tackle these problems should be based on reliable hardware systems, which have an intricate all-to-all node connectivity. This connectivity ultimately allows graphs representing arbitrary dimensions of a problem to be directly mapped onto the computer hardware.

Researchers at University of Minnesota recently developed a new electronic device based on standard complementary metal oxide semiconductor (CMOS) technology that could support this crucial mapping process. This device, introduced in a paper in Nature Electronics, is a physics-based Ising solver comprised of coupled ring oscillators and an all-to-all node connected architecture.

Another excellent paper from Johann G. Danzl’s research group. They develop methods that combine novel negative staining techniques, deep learning, and super-resolution STED microscopy or expansion microscopy to facilitate nanoscale-resolution imaging of brain tissue volumes. They also show semi-automated (and some fully automated) segmentation of neuron morphology and identification of synapses. Very cool work and I’m excited to see how it influences connectomics in the future! #brain #neuroscience #imaging #microscopy #neurotech

Mapping fixed brain samples with extracellular labeling and optical microscopy reveals synaptic connections.

“Operating and navigating in home environments is very challenging for robots. Every home is unique, with a different combination of objects in distinct configurations that change over time. To address the diversity a robot faces in a home environment, we teach the robot to perform arbitrary tasks with a variety of objects, rather than program the robot to perform specific predefined tasks with specific objects. In this way, the robot learns to link what it sees with the actions it is taught. When the robot sees a specific object or scenario again, even if the scene has changed slightly, it knows what actions it can take with respect to what it sees.

We teach the robot using an immersive telepresence system, in which there is a model of the robot, mirroring what the robot is doing. The teacher sees what the robot is seeing live, in 3D, from the robot’s sensors. The teacher can select different behaviors to instruct and then annotate the 3D scene, such as associating parts of the scene to a behavior, specifying how to grasp a handle, or drawing the line that defines the axis of rotation of a cabinet door. When teaching a task, a person can try different approaches, making use of their creativity to use the robot’s hands and tools to perform the task. This makes leveraging and using different tools easy, allowing humans to quickly transfer their knowledge to the robot for specific situations.

Continue reading “Toyota Robots That Do Housework!” »

Sights and sounds are easily digitized, but scents have eluded researchers until now.

Deep Learning (DL) performs classification tasks using a series of layers. To effectively execute these tasks, local decisions are performed progressively along the layers. But can we perform an all-encompassing decision by choosing the most influential path to the output rather than performing these decisions locally?

In an article published today in Scientific Reports, researchers from Bar-Ilan University in Israel answer this question with a resounding “yes.” Pre-existing deep architectures have been improved by updating the most influential paths to the output.

Continue reading “Better paths yield better AI: Enhancing pre-existing architectures” »

On Thursday, NASA released the first data maps from its new instrument launched to space earlier this year, which now is successfully transmitting information about major air pollutants over North America.

Summary: Researchers successfully mapped the neural activity of the C. elegans worm, correlating it to its behaviors such as movement and feeding.

Using novel technologies and methodologies, they developed a comprehensive atlas that showcases how most of the worm’s neurons encode its various actions.

This study provides an intricate look into how an animal’s nervous system controls behavior. The team’s findings, data, and models are available on the “WormWideWeb.”

How molecules change when they react to stimuli such as light is fundamental in biology, for example during photosynthesis. Scientists have been working to unravel the workings of these changes in several fields, and by combining two of these, researchers have paved the way for a new era in understanding the reactions of protein molecules fundamental for life.

The large international research team, led by Professor Jasper van Thor from the Department of Life Sciences at Imperial, report their results in the journal Nature Chemistry.

Crystallography is a powerful technique in structural biology for taking ‘snapshots’ of how molecules are arranged. Over several large-scale experiments and years of theory work, the team behind the new study integrated this with another technique that maps vibrations in the electronic and nuclear configuration of molecules, called spectroscopy.

Scientists working in connectomics, a research field occupied with the reconstruction of neuronal networks in the brain, are aiming at completely mapping of the millions or billions of neurons found in mammalian brains. In spite of impressive advances in electron microscopy, the key bottleneck for connectomics is the amount of human labor required for the data analysis. Researchers at the Max Planck Institute for Brain Research in Frankfurt, Germany, have now developed reconstruction software that allows researchers to fly through the brain tissue at unprecedented speed. Together with the startup company scalable minds they created webKnossos, which turns researchers into brain pilots, gaining an about 10-fold speedup for data analysis in connectomics.

Billions of nerve cells are working in parallel inside our brains in order to achieve behaviours as impressive as hypothesizing, predicting, detecting, thinking. These neurons form a highly complex network, in which each nerve cell communicates with about one thousand others. Signals are sent along ultrathin cables, called axons, which are sent from each neuron to its about one thousand “followers.”

Continue reading “10-fold Speed up For The Reconstruction of Neuronal Networks” »