The three-pound crustacean will live at an aquarium, offering a fun genetics lesson. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The exciting discovery offers a lesson in genetics. Breakthroughs, discoveries, and DIY tips sent six days a week. A two-toned lobster is set to make a splash at the Woods Hole Science Aquarium in southeastern Massachusetts.

Pain killers seem to work on lobsters, so being boiled alive may be just as gruesome as it sounds. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. A growing body of research suggests that the crustaceans can feel pain. Breakthroughs, discoveries, and DIY tips sent six days a week. When it's time to cook a lobster, the crustaceans are infamously boiled alive.

Monitoring aquatic species, especially elusive ones like lobsters, presents challenges. This study focuses on Homarus gammarus (European lobster), a key species for fisheries and aquaculture, and leverages non-invasive Passive Acoustic Monitoring (PAM). Understanding lobster habitats, welfare, reproduction, sex, and age is crucial for management and conservation. While bioacoustic emissions have classified various aquatic species using Artificial Intelligence (AI) models, this research specifically uses H. gammarus bioacoustics (buzzing/carapace vibrations) to classify lobsters by age (juvenile/adult) and sex (male/female). The dataset was collected at Johnshaven, Scotland, using hydrophones in concrete tanks. We explored the efficacy of Deep Learning (DL) models (1D-CNN, 1D-DCNN) and six Machine Learning (ML) models (SVM, k-NN, Naive Bayes, Random Forest, XGBoost, MLP). Mel-frequency cepstral coefficients (MFCCs) were used as features. For age classification (adult vs. juvenile), most models achieved over 97% accuracy (Naive Bayes: 91.31%). For sex classification, all models except Naive Bayes surpassed 93.23%. These strong results demonstrate the potential of supervised ML and DL to extract age- and sex-related features from lobster sounds. This research offers a promising non-invasive PAM approach for lobster conservation, detection, and management in aquaculture and fisheries, enabling real-world edge computing applications for underwater species.

Jackie (short for jack-o'-lantern) owes her unique colors to a mixture of chemical compounds. Breakthroughs, discoveries, and DIY tips sent every weekday. A rare and seasonally-colored lobster is joining spiders, bats, and even some oozing fungi as some of nature's best Halloween ambassadors. Jackie is a calico lobster and the odds of catching a crustacean like this are about one-in-20 million, according to the Marine Science Center outreach coordinator Sierra Munoz. This makes Jackie even more rare than the center's other recent star, Neptune the blue lobster .

Neurosymbolic programs combine deep learning with symbolic reasoning to achieve better data efficiency, interpretability, and generalizability compared to standalone deep learning approaches. However, existing neurosymbolic learning frameworks implement an uneasy marriage between a highly scalable, GPU-accelerated neural component with a slower symbolic component that runs on CPUs. We propose Lobster, a unified framework for harnessing GPUs in an end-to-end manner for neurosymbolic learning. Lobster maps a general neurosymbolic language based on Datalog to the GPU programming paradigm. This mapping is implemented via compilation to a new intermediate language called APM. The extra abstraction provided by APM allows Lobster to be both flexible, supporting discrete, probabilistic, and differentiable modes of reasoning on GPU hardware with a library of provenance semirings, and performant, implementing new optimization passes. We demonstrate that Lobster programs can solve interesting problems spanning the domains of natural language processing, image processing, program reasoning, bioinformatics, and planning. On a suite of 8 applications, Lobster achieves an average speedup of 5.3x over Scallop, a state-of-the-art neurosymbolic framework, and enables scaling of neurosymbolic solutions to previously infeasible tasks.

Whether a large and fuzzy social media sensation or deep-sea slug slunking around the ocean's Midnight Zone, there are still so many exciting animals on Earth just waiting for their close-up. In that spirit, here are the 11 of the most exciting animal stories that Popular Science covered this year. A wildlife filmmaker and biology doctoral student took what could be the first picture of a newborn great white shark. Filmmaker Carlos Gauna and University of California, Riverside biology doctoral student Phillip Sternes were looking for sharks near Santa Barbara on California's central coast. Most great whites are gray on top with white bellies, but Gauana's drone camera showed a roughly 5-foot-long shark pup that had more white on its body than normal.

This doesn't always necessarily mean grand shots of spaceships or far-flung planets. For every lavish spectacle like Dune, there are many more smaller-scale sci-fi movies with modest or nonexistent special effects budgets. These movies must use other methods to flesh out their futuristic visions. An atmospheric soundtrack can go far to create a thrilling mood. Clever set design, like the homebrewed time machine in Primer or the quantum-computer cables strung through the woods in Lapsis, can immerse audiences in a new world without cutting-edge CGI.

LOBSTER (LOss-Based SensiTivity rEgulaRization) is a method for training neural networks having a sparse topology. Let the sensitivity of a network parameter be the variation of the loss function with respect to the variation of the parameter. Parameters with low sensitivity, i.e. having little impact on the loss when perturbed, are shrunk and then pruned to sparsify the network. Our method allows to train a network from scratch, i.e. without preliminary learning or rewinding. Experiments on multiple architectures and datasets show competitive compression ratios with minimal computational overhead.

We present an approach to synthesizing new graph structures from empirically specified distributions. The generative model is an auto-decoder that learns to synthesize graphs from latent codes. The graph synthesis model is learned jointly with an empirical distribution over the latent codes. Graphs are synthesized using self-attention modules that are trained to identify likely connectivity patterns. Graph-based normalizing flows are used to sample latent codes from the distribution learned by the auto-decoder. The resulting model combines accuracy and scalability. On benchmark datasets of large graphs, the presented model outperforms the state of the art by a factor of 1.5 in mean accuracy and average rank across at least three different graph statistics, with a 2x speedup during inference.

We know that they have the same sensors – called nociceptors – that cause us to flinch or cry when we are hurt. And they certainly behave like they are sensing something unpleasant. When a chef places them in boiling water, for instance, they twitch their tails as if they are in agony. But are they actually "aware" of the sensation? Or is that response merely a reflex?