Lifeboat Foundation

The fascinating collective behaviors of biological systems have inspired extensive studies on shape assembly of robot swarms^6,7,8,9. One class of strategies widely studied in the literature are based on goal assignment in either centralized or distributed ways^10,11,12. Once a swarm of robots are assigned unique goal locations in a desired shape, the consequent task is simply to plan collision-free trajectories for the robots to reach their goal locations¹⁰ or conduct distributed formation control based on locally sensed information^6,13,14. It is notable that centralized goal assignment is inefficient to support large-scale swarms since the computational complexity increases rapidly as the number of robots increases^15,16. Moreover, when robots fail to function normally, additional algorithms for fault-tolerant detection and goal re-assignment are required to handle such situations¹⁷. As a comparison, distributed goal assignment can support large-scale swarms by decomposing the centralized assignment into multiple local ones^11,12. It also exhibits better robustness to robot faults. However, since distributed goal assignments are based on locally sensed information, conflicts among local assignments are inevitable and must be resolved by sophisticated algorithms such as local task swapping^11,12.

Another class of strategies for shape assembly that have also attracted extensive research attention are free of goal assignment^18,19,20,21. For instance, the method proposed in ref. ¹⁸ can assemble complex shapes using thousands of homogeneous robots. An interesting feature of this method is that it does not rely on external global positioning systems. Instead, it establishes a local positioning system based on a small number of pre-localized seed robots. As a consequence of the local positioning system, the proposed edge-following control method requires that only the robots on the edge of a swarm can move while those inside must stay stationary. The method in ref. ¹⁹ can generate swarm shapes spontaneously from a reaction-diffusion network similar to embryogenesis in nature. However, this method is not able to generate user-specified shapes precisely. The method in ref. ²¹ can aggregate robots on the frontier of shapes based on saliency detection. The user-defined shape is specified by a digital light projector. An interesting feature of this method is that it does not require centralized edge detectors. Instead, edge detection is realized in a distributed manner by fusing the beliefs of a robot with its neighbors. However, since the robots cannot self-localize themselves relative to the desired shape, they make use of random walks to search for the edges, which would lead to random trajectories. Another class of methods that do not require goal assignment is based on artificial potential fields^22,23,24,25. One limitation of this class of methods is that robots may easily get trapped in local minima, making it difficult to assemble nonconvex complex shapes.

Here, we propose a strategy for shape assembly of robot swarms based on the idea of mean-shift exploration: when a robot is surrounded by neighboring robots and unoccupied locations, it would actively give up its current location by exploring the highest density of nearby unoccupied locations in the desired shape. This idea does not rely on goal assignment. It is realized by adapting the mean-shift algorithm^26,27,28, which is an optimization technique widely used in machine learning for locating the maxima of a density function. Moreover, a distributed negotiation mechanism is designed to allow robots to negotiate the final desired shape with their neighbors in a distributed manner. This negotiation mechanism enables the swarm to maneuver while maintaining a desired shape based on a small number of informed robots. The proposed strategy empowers robot swarms to assemble nonconvex complex shapes with strong adaptability and high efficiency, as verified by numerical simulation results and real-world experiments with swarms of 50 ground robots. The strategy can be adapted to generate interesting behaviors including shape regeneration, cooperative cargo transportation, and complex environment exploration.

Summary: Researchers developed one of the most comprehensive 3D models of the synapse, the neuron juncture crucial for intercellular communication. This breakthrough allows an unprecedented view of the complex interactions between individual cells at the synapse, offering fresh insights into neurodegenerative diseases such as Huntington’s disease and schizophrenia.

The team used this novel approach to compare healthy mice brains to those with the Huntington’s mutant gene, revealing structural flaws potentially disrupting cellular communication. The researchers believe this technique could significantly advance our understanding of various neurodegenerative and neuropsychiatric diseases.

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Continue reading “Vitamin B6 Didn’t Impact NAD (Test Results)” »

A novel type of neural network is helping physicists with the daunting challenge of data analysis.

The aim of non-pharmacologic interventions for brain health is to preserve cognitive function and safeguard brain structure. This review explores various diets (MeDi, DASH, MIND, ketogenic), exercise approaches (endurance, resistance, yoga, HIIT), and highlights the need for further research to uncover the underlying mechanisms.

Seeing a supernova, or an exploding star, is a unique spectacle in itself. But recently, astronomers have found something even more unique: A star explosion so “extremely warped” that it looked like it was multiple images in the sky.

So how did this happen?

It’s not magic, according to the California Institute of Technology, but an effect known as “gravitational lensing,” which happens when gravity from a dense object in space “distorts and brightens the light of an object behind it.” In the case of supernova SN Zwicky, it was the gravity of another galaxy that impacted its appearance.

As people get older, they tend to have lower levels of anxiety. But why? A new brain imaging study has found that older individuals are faster at recognizing and responding to negative emotions. The findings, published NeuroImage, go against the idea that older adults are less engaged with negative emotions due to cognitive decline or that they are better at regulating negative emotions. Instead, the results suggest that older adults may develop a more automatic way of processing negative emotions.

The study aimed to investigate the relationship between aging, trait anxiety, and changes in cognitive and affective functions. The researchers were motivated by previous findings that older adults tend to have lower susceptibility to anxiety disorders compared to younger and middle-aged adults. However, it was not clear how age-related changes in anxiety symptoms, such as worry and somatic symptoms, were related to changes in cognitive and affective processes.

“We are interested in emotion dysfunction in early dementia, including those people with subjective complaints of memory problem and mild cognitive impairment,” said study author Chiang-shan Ray Li, a professor of psychiatry and neuroscience at Yale University School of Medicine.

In my new Newsweek Op-Ed, I tackle a primary issue many people have with trying to stop aging and death via science. Hopefully this philosophical argument will allow more resources & support into the life extension field:

Philosophers often say if humans didn’t die, we’d be bored out of our minds. This idea, called temporal scarcity, argues the finitude of death is what makes life worth living. Transhumanists, whose most urgent goal is to use science to overcome biological death, emphatically disagree.

For decades, the question of temporal scarcity has been debated and analyzed in essays and books. But an original idea transhumanists are putting forth is reinvigorating the debate. It doesn’t discount temporal scarcity in biological humans; it discounts it in what humans will likely become in the future—cyborgs and digitized consciousnesses.

Continue reading “Eliminating Death Doesn’t Mean Life Will Get Boring” »

Researchers at IBM pitted their 127-qubit Eagle quantum computer against a conventional supercomputer in a challenge to perform a complex calculation – and the quantum computer won.

By Karmela Padavic-Callaghan