Having hairy legs may make shrimp better swimmers. "Can hair help you swim better? Swimmers will say no, but shrimp would say yes," said Sara Oliveira Santos from Brown University in Rhode Island at a fluid dynamics conference this week. She and her colleagues studied how shrimp and shrimp-inspired robots swim to determine whether it is advantageous for them to be hairy. Shrimp use a special swimming technique called metachronal swimming to easily move through water.

Taxonomies are an essential knowledge representation, yet most studies on automatic taxonomy construction (ATC) resort to manual evaluation to score proposed algorithms. We argue that automatic taxonomy evaluation (ATE) is just as important as taxonomy construction. We propose RaTE, an automatic label-free taxonomy scoring procedure, which relies on a large pre-trained language model. We apply our evaluation procedure to three state-of-the-art ATC algorithms with which we built seven taxonomies from the Yelp domain, and show that 1) RaTE correlates well with human judgments and 2) artificially degrading a taxonomy leads to decreasing RaTE score.

The current approach to exploring and monitoring complex underwater ecosystems, such as coral reefs, is to conduct surveys using diver-held or static cameras, or deploying sensor buoys. These approaches often fail to capture the full variation and complexity of interactions between different reef organisms and their habitat. The CUREE platform presented in this paper provides a unique set of capabilities in the form of robot behaviors and perception algorithms to enable scientists to explore different aspects of an ecosystem. Examples of these capabilities include low-altitude visual surveys, soundscape surveys, habitat characterization, and animal following. We demonstrate these capabilities by describing two field deployments on coral reefs in the US Virgin Islands. In the first deployment, we show that CUREE can identify the preferred habitat type of snapping shrimp in a reef through a combination of a visual survey, habitat characterization, and a soundscape survey. In the second deployment, we demonstrate CUREE's ability to follow arbitrary animals by separately following a barracuda and stingray for several minutes each in midwater and benthic environments, respectively.

The AI community has embraced multi-sensory or multi-modal approaches to advance this generation of AI models to resemble expected intelligent understanding. Combining language and imagery represents a familiar method for specific tasks like image captioning or generation from descriptions. This paper compares these monolithic approaches to a lightweight and specialized method based on employing image models to label objects, then serially submitting this resulting object list to a large language model (LLM). This use of multiple Application Programming Interfaces (APIs) enables better than 95% mean average precision for correct object lists, which serve as input to the latest Open AI text generator (GPT-4). To demonstrate the API as a modular alternative, we solve the problem of a user taking a picture of ingredients available in a refrigerator, then generating novel recipe cards tailored to complex constraints on cost, preparation time, dietary restrictions, portion sizes, and multiple meal plans. The research concludes that monolithic multimodal models currently lack the coherent memory to maintain context and format for this task and that until recently, the language models like GPT-2/3 struggled to format similar problems without degenerating into repetitive or non-sensical combinations of ingredients. For the first time, an AI chef or cook seems not only possible but offers some enhanced capabilities to augment human recipe libraries in pragmatic ways. The work generates a 100-page recipe book featuring the thirty top ingredients using over 2000 refrigerator images as initializing lists.

Sparse shrunk additive models and sparse random feature models have been developed separately as methods to learn low-order functions, where there are few interactions between variables, but neither offers computational efficiency. On the other hand, $\ell_2$-based shrunk additive models are efficient but do not offer feature selection as the resulting coefficient vectors are dense. Inspired by the success of the iterative magnitude pruning technique in finding lottery tickets of neural networks, we propose a new method -- Sparser Random Feature Models via IMP (ShRIMP) -- to efficiently fit high-dimensional data with inherent low-dimensional structure in the form of sparse variable dependencies. Our method can be viewed as a combined process to construct and find sparse lottery tickets for two-layer dense networks. We explain the observed benefit of SHRIMP through a refined analysis on the generalization error for thresholded Basis Pursuit and resulting bounds on eigenvalues. From function approximation experiments on both synthetic data and real-world benchmark datasets, we show that SHRIMP obtains better than or competitive test accuracy compared to state-of-art sparse feature and additive methods such as SRFE-S, SSAM, and SALSA. Meanwhile, SHRIMP performs feature selection with low computational complexity and is robust to the pruning rate, indicating a robustness in the structure of the obtained subnetworks. We gain insight into the lottery ticket hypothesis through SHRIMP by noting a correspondence between our model and weight/neuron subnetworks.

An artificial colour-changing material inspired by the skins of chameleons can be used as a chemical sensor to determine whether seafood is fresh, a study found. Developed by experts from China, the device switches from pink to green in the presence of the amine vapours released by microbes when fish and shrimp spoil. The novel material could also find applications in the development of anticounterfeit technology, camouflage for robots and stretchable electronics, the team said. Panther chameleons are colour-changing reptiles native to the island of Madagascar in the Indian Ocean. Males of the species -- which are more brightly coloured than their female counterparts and change hue when asserting their dominance -- can grow to around 8 inches (20 cm) in length.

A tiny shrimp snaps its claw in less than 0.01 seconds, around 10,000 times quicker than the blink of a human eye. The movement is so rapid it creates an audible pop above the water and produces bubbles. The engorged claw shuts in just 93 microseconds, moving at around 38 mph. Human eyes take about 150 milliseconds to complete the process of blinking. Researchers from Duke University in the US say the shrimp combines a unique set of traits which make the speed all the more impressive. It is not the fastest appendage movement in the animal kingdom, with the jaws of some terrestrial animals matching and exceeding its velocity.

Agricultural and fishing industries in Asia are being transformed by technology as the cheap and abundant labor that they long relied on erodes due to demographic pressures. Rising wages across other industries have created a labor shortage in the traditional staples of economies, especially in Southeast Asia. To make up for a shrinking population of farmers, companies are adopting artificial intelligence and drones to help grow food more cheaply and efficiently. In Vietnam, Minh Phu Seafood is building new vinyl shrimp tanks that are shaped like deep bowls. Water is swirled around the tank so that waste collects at the bottom where it can easily be drained.

The pistol shrimp, aka the snapping shrimp, is a peculiar contradiction. At just a few inches long, it wields one proportionally sized claw and another massive one that snaps with such force the resulting shockwave knocks its prey out cold. As the two bits of the claw come together, bubbles form and then rapidly collapse, shooting out a bullet of plasma that in turn produces a flash of light and temperatures of 8,000 degrees Fahrenheit. That's right--an underwater creature that fits in the palm of your hand can, with a flick of its claw, weaponize a blast of insanely hot bubbles. Now scientists are learning how to wield this formidable force themselves.

The mantis shrimp packs a mean punch, smashing its victims' shells with the force of a .22 But that's not because it has particularly powerful muscles – instead of big biceps, it has arms that are naturally spring-loaded, allowing it to swing its fistlike clubs to speeds up to 23 metres per second. We know that the key part of a mantis shrimp's punch is a saddle-shaped structure on the arm just above the shrimp's club. This shape works a bit like a bow and arrow, says Ali Miserez at Nanyang Technological University in Singapore: the muscles pull on the saddle to bend it like an archer's bow, and when it is released that energy transfers into the club. Miserez and his colleagues used a series of tiny pokes and prods, as well as a computer model, to examine exactly how the shrimp's saddle holds all that energy without snapping.