This research introduces a novel hydrofoil-based propulsion framework for unmanned aquatic robots, inspired by the undulating locomotion observed in select aquatic species. The proposed system incorporates a camber-modulating mechanism to enhance hydrofoil propulsive force generation and eventually efficiency. Through dynamic simulations, we validate the effectiveness of the camber-adjusting hydrofoil compared to a symmetric counterpart. The results demonstrate a significant improvement in horizontal thrust, emphasizing the potential of the cambering approach to enhance propulsive performance. Additionally, a prototype flipper design is presented, featuring individual control of heave and pitch motions, as well as a camber-adjustment mechanism. The integrated system not only provides efficient water-based propulsion but also offers the capacity for generating vertical forces during take-off maneuvers for seaplanes. The design is tailored to harness wave energy, contributing to the exploration of alternative energy resources. This work advances the understanding of bionic oscillatory principles for aquatic robots and provides a foundation for future developments in environmentally safe and agile underwater exploration.

As large language models continue to scale in size rapidly, so too does the computational power required to run them. Event-based networks on neuromorphic devices offer a potential way to reduce energy consumption for inference significantly. However, to date, most event-based networks that can run on neuromorphic hardware, including spiking neural networks (SNNs), have not achieved task performance even on par with LSTM models for language modeling. As a result, language modeling on neuromorphic devices has seemed a distant prospect. In this work, we demonstrate the first-ever implementation of a language model on a neuromorphic device - specifically the SpiNNaker 2 chip - based on a recently published event-based architecture called the EGRU. SpiNNaker 2 is a many-core neuromorphic chip designed for large-scale asynchronous processing, while the EGRU is architected to leverage such hardware efficiently while maintaining competitive task performance. This implementation marks the first time a neuromorphic language model matches LSTMs, setting the stage for taking task performance to the level of large language models. We also demonstrate results on a gesture recognition task based on inputs from a DVS camera. Overall, our results showcase the feasibility of this neuro-inspired neural network in hardware, highlighting significant gains versus conventional hardware in energy efficiency for the common use case of single batch inference.

Artificial Neural Networks (ANN) have gained large popularity thanks to their ability to learn using the well-known backpropagation algorithm. On the other hand, Spiking Neural Networks (SNNs), despite having wider abilities than ANNs, have always presented a challenge in the training phase. This paper shows a new method to perform supervised learning on SNNs, using Spike Timing Dependent Plasticity (STDP) and homeostasis, aiming at training the network to identify spatial patterns. The method is tested using the SpiNNaker digital architecture. A SNN is trained to recognise one or multiple patterns and performance metrics are extracted to measure the performance of the network. Some considerations are drawn from the results showing that, in the case of a single trained pattern, the network behaves as the ideal detector, with 100% accuracy in detecting the trained pattern. However, as the number of trained patterns on a single network increases, the accuracy of the identification is linked to the similarities between these patterns. This method of training an SNN to detect spatial patterns may be applied on pattern recognition in static images or traffic analysis in computer networks, where each network packet represents a spatial pattern. It will be stipulated that the homeostatic factor may enable the network to detect patterns with some degree of similarities, rather than only perfectly matching patterns.

In this study, we investigated the relationship between sailboat technical specifications and their prices, as well as regional pricing influences. Utilizing a dataset encompassing characteristics like length, beam, draft, displacement, sail area, and waterline, we applied multiple machine learning models to predict sailboat prices. The gradient descent model demonstrated superior performance, producing the lowest MSE and MAE. Our analysis revealed that monohulled boats are generally more affordable than catamarans, and that certain specifications such as length, beam, displacement, and sail area directly correlate with higher prices. Interestingly, lower draft was associated with higher listing prices. We also explored regional price determinants and found that the United States tops the list in average sailboat prices, followed by Europe, Hong Kong, and the Caribbean. Contrary to our initial hypothesis, a country's GDP showed no direct correlation with sailboat prices. Utilizing a 50% cross-validation method, our models yielded consistent results across test groups. Our research offers a machine learning-enhanced perspective on sailboat pricing, aiding prospective buyers in making informed decisions.

Underwater structures that can change their shapes dynamically, the way fish do, push through water much more efficiently than conventional rigid hulls. But constructing deformable devices that can change the curve of their body shapes while maintaining a smooth profile is a long and difficult process. MIT's RoboTuna, for example, was composed of about 3,000 different parts and took about two years to design and build. Now, researchers at MIT and their colleagues--including one from the original RoboTuna team--have come up with an innovative approach to building deformable underwater robots, using simple repeating substructures instead of unique components. The team has demonstrated the new system in two different example configurations, one like an eel and the other a wing-like hydrofoil.

Underwater structures that can change their shapes dynamically, the way fish do, push through water much more efficiently than conventional rigid hulls. But constructing deformable devices that can change the curve of their body shapes while maintaining a smooth profile is a long and difficult process. MIT's RoboTuna, for example, was composed of about 3,000 different parts and took about two years to design and build. Now, researchers at MIT and their colleagues -- including one from the original RoboTuna team -- have come up with an innovative approach to building deformable underwater robots, using simple repeating substructures instead of unique components. The team has demonstrated the new system in two different example configurations, one like an eel and the other a wing-like hydrofoil.

In the wake of storms of the near future, swarms of drones could replace helicopters and planes, providing emergency crews with more rapid and accurate data on the coming threats of lightning-sparked bushfires or flash floods heading for homes. Authorities now rely on satellites, which require clear weather during daytime and may only provide resolution down to 10 metres. Alternatively, pilots of aircraft may burn as much as $3,400 worth of fuel an hour and often can't fly for safety reasons. Enter firms such as Sydney-based Carbonix, a developer that started out designing America's Cup racing yachts before changing tack to make drones capable of flying eight hours or longer with resolution fine enough to read words on a piece of paper. Dario Valenza, chief technology officer and founder of Carbonix, says thermal cameras on the drones could quickly verify fires started by lightning in remote regions, helping to direct fire crews to the scene "with only a few per cent of the fuel" used by conventional aircraft that might have their operations curtailed by weather.

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We were blown away by the success of Bored Ape Yacht Club. As tech art fans, we wanted to see what would happen if we used these cool Apes to teach AI how to make art. The beautiful remakes of the famous Bored Ape Yacht Club were even better than we had expected. We thought that everyone should see this, so we made a collection of 10,000 Apes that were made by AI. For us, this is the start of a big push in the NFT community to make generative AI art.

Forget opposable thumbs--our greatest evolutionary advantage has been a capacity to deal with variability, adapting to the prevailing conditions and finding opportunity in the unpredictable. Elite yacht-racing is a fine and fearsome showcase for those human qualities. And it doesn't get more elite than the America's Cup: 11 top-of-their-game sailors in a highly-engineered boat pitched against inconstant waves and another similarly sized, crewed and engineered yacht. All things being equal, the race would be a simple case of best crew wins. But all things are not equal.