Insects control unsteady aerodynamic forces on flapping wings to navigate complex environments. While understanding these forces is vital for biology, physics, and engineering, existing evaluation methods face trade-offs: high-fidelity simulations are computationally or experimentally expensive and lack explanatory power, whereas theoretical models based on quasi-steady assumptions offer insights but exhibit low accuracy. To overcome these limitations and thus enhance the accuracy of quasi-steady aerodynamic models, we applied a data-driven approach involving discovery and formulation of previously overlooked critical mechanisms. Through selection from 5,000 candidate kinematic functions, we identified mathematical expressions for three key additional mechanisms -- the effect of advance ratio, effect of spanwise kinematic velocity, and rotational Wagner effect -- which had been qualitatively recognized but were not formulated. Incorporating these mechanisms considerably reduced the prediction errors of the quasi-steady model using the computational fluid dynamics results as the ground truth, both in hawkmoth forward flight (at high Reynolds numbers) and fruit fly maneuvers (at low Reynolds numbers). The data-driven quasi-steady model enables rapid aerodynamic analysis, serving as a practical tool for understanding evolutionary adaptations in insect flight and developing bio-inspired flying robots.

Breakthroughs, discoveries, and DIY tips sent every weekday. No responsibilities and little to do but enjoy yourself. Yet somehow, after a whole day of blissful nothing, you find yourself completely zonked. If taking in the sea air is supposed to be restorative, why can a restful day at the beach end up feeling so tiring? There's no one certain answer, but science offers a few possibilities.

Breakthroughs, discoveries, and DIY tips sent every weekday. In a world first, scientists at the University of Utah have engineered fruit flies susceptible to cocaine addiction. But as strange as it sounds, there are potentially life-saving reasons for genetically altering the insects to crave the drug. The novel biological model could help addiction treatment therapies development and expedite research timelines. The findings are detailed in the Journal of Neuroscience.

Reinforcement Learning from Human Feedback (RLHF) is crucial for aligning large language models with human preferences. While recent research has focused on algorithmic improvements, the importance of prompt-data construction has been overlooked. This paper addresses this gap by exploring data-driven bottlenecks in RLHF performance scaling, particularly reward hacking and decreasing response diversity. We introduce a hybrid reward system combining reasoning task verifiers (RTV) and a generative reward model (GenRM) to mitigate reward hacking. We also propose a novel prompt-selection method, Pre-PPO, to maintain response diversity and enhance learning effectiveness. Additionally, we find that prioritizing mathematical and coding tasks early in RLHF training significantly improves performance. Experiments across two model sizes validate our methods' effectiveness and scalability. Results show that RTV is most resistant to reward hacking, followed by GenRM with ground truth, and then GenRM with SFT Best-of-N responses. Our strategies enable rapid capture of subtle task-specific distinctions, leading to substantial improvements in overall RLHF performance. This work highlights the importance of careful data construction and provides practical methods to overcome performance barriers in RLHF.

This study develops a transfer learning model for the automated classification of two species of fruit flies, Anastrepha fraterculus and Ceratitis capitata, in a controlled laboratory environment. The research addresses the need to optimize identification and classification, which are currently performed manually by experts, being affected by human factors and facing time challenges. The methodological process of this study includes the capture of high-quality images using a mobile phone camera and a stereo microscope, followed by segmentation to reduce size and focus on relevant morphological areas. The images were carefully labeled and preprocessed to ensure the quality and consistency of the dataset used to train the pre-trained convolutional neural network models VGG16, VGG19, and Inception-v3. The results were evaluated using the F1-score, achieving 82% for VGG16 and VGG19, while Inception-v3 reached an F1-score of 93%. Inception-v3's reliability was verified through model testing in uncontrolled environments, with positive results, complemented by the Grad-CAM technique, demonstrating its ability to capture essential morphological features. These findings indicate that Inception-v3 is an effective and replicable approach for classifying Anastrepha fraterculus and Ceratitis capitata, with potential for implementation in automated monitoring systems.

Brains are bewilderingly complicated systems of connections between neurons. Mapping those connections is an important step in understanding how brains work. Scientists have recently completed the most ambitious effort yet to construct such a map: a complete document of every neuron and every connection in the brain of an adult fruit fly. The research represents the first such map for an animal that can walk and see, and the first complete map of the brain of an adult animal. It traces each and every one of the 139,255 neurons in the brain of Drosophila melanogaster, along with the 50 million connections between them, and is by far the largest and most detailed ever produced.

A broad coalition of AI experts recently released a brief public statement warning of "the risk of extinction from AI." There are many different ways in which AIs might become serious dangers to humanity, and the exact nature of the risks is still debated, but imagine a CEO who acquires an AI assistant. They begin by giving it simple, low-level assignments, like drafting emails and suggesting purchases. As the AI improves over time, it progressively becomes much better at these things than their employees. So the AI gets "promoted."

Yet, in their own way, fly larvae lead rich and interesting lives full of sensory inputs, social behaviors, and learning. If you've ever doubted that a lot goes on inside a maggot's head, now we have the map to prove to it. An interdisciplinary team of scientists have released a complete reconstruction and analysis of a larval fruit fly's brain, published Thursday in the journal Science. The resulting map, or connectome, as its called in neuroscience, includes each one of the 3,016 neurons and 548,000 of the synapses running between neurons that make up the baby fly's entire central nervous system. The connectome includes both of the larva's brain lobes, as well as the nerve cord. The first (mostly) complete connectome was of a nematode (C.

Scientists have fully mapped a fruit fly brain for the first time, uncovering every individual neuron (messenger cell) and how they are wired together. The 3,016 neurons resemble an AI neural network of sorts and between those cells, they discovered 548,000 points of connection, or synapses, where cells transmit chemical messages, thus triggering electrical signals that travel through their wiring. How a brain circuit is structured heavily influences the computations it can perform, and up until this point, researchers have only seen the structure of the roundworm C. elegans, the tadpole of a low chordate and the larva of a marine annelid, all of which only have several hundred neurons. Now they can start gaining a mechanisitic understanding of how the brain actually works. We previously had only limited information on gene expression in the zebrafish brain.

By melding two microscopy methods and a computational tool, researchers can quickly and precisely quantify neuronal connections in individual animals, according to a new study. The technique could make it faster to map the connectomes of autism mouse models and track how mutations in autism-associated genes rewire neural circuits. A human neuron has thousands of synaptic connections, which light-based microscopy lacks the resolution to detect. Electron microscopy can resolve neuronal links in exquisite detail -- theoretically down to 0.12 nanometers, a length slightly shorter than a carbon-carbon chemical bond -- but the process is slow and laborious. In one study, it took about three years to section and image a single fly brain with a scanning electron microscope.