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The origin of Recurring Slope Linea, the long, dark markings that stretches across the slopes of Martian surface for hundreds of feet, has been a topic of debate for a long time. They appear during warmer season and they were seen as one of the most compelling signs that liquid water might still exist on Mars, suggesting a rare pocket of habitability on an otherwise arid planet. However, this view has been challenged by a new study by a group of researchers Brown University and the University of Bern. Using machine learning and leveraging satellite images, they have found evidence about the dry processes being the origin of these streaks.

Machine learning breakthrough brings new evidences

According to Adomas Valantinas, a postdoctoral researcher at Brown who coauthored the research with Valentin Bickel, a researcher at Bern, a big focus of Mars research is understanding modern-day processes on Mars, including the possibility of liquid water on the surface. This study reviewed these features but found no evidence of water. Their model favours dry formation processes.

Valantinas and Bickel integrated over 86000 high-resolution satellite images with a machine learning algorithm for analysis. They created the first global map of Martian slope streaks, cataloguing over 500,000 individual features across the planet’s surface. This database enables an unprecedented statistical analysis for the origin and location of these streaks.

Researchers used the complete streak map to compare the locations of these features with environmental factors like wind speed, temperature, dust accumulation, and surface hydration. They did not find any correlation between the streaks and water or frost. Rather, these features are more common in areas with high wind activity and dust deposition.

Future of Mars exploration

This study provides insights in understanding the surface activities of current Martian environment. The streak sites were used to be treated with caution because of environmental contamination concerns. Ruling out the possibility of liquid water as origin of streaks, the study almost cancels out the probable risk of Earth based contamination.

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AI Model Learns to Predict Human Gait for Smarter, Pre-Trained Exoskeleton Control

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Scientists at Georgia Tech have created an AI technique that pre-trains exoskeleton controllers using existing human motion datasets, removing the need for lengthy lab-based retraining. The system predicts joint behavior and assistance needs, enabling controllers that work as well as hand-tuned versions. This advance accelerates prototype development and could improve…

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Scientists Build One of the Most Detailed Digital Simulations of the Mouse Cortex Using Japan’s Fugaku Supercomputer

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Researchers from the Allen Institute and Japan’s University of Electro-Communications have built one of the most detailed mouse cortex simulations ever created. Using Japan’s Fugaku supercomputer, the team modeled around 10 million neurons and 26 billion synapses, recreating realistic structure and activity. The virtual cortex offers a new platform for studying br…

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UC San Diego Engineers Create Wearable Patch That Controls Robots Even in Chaotic Motion

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UC San Diego engineers have developed a soft, AI-enabled wearable patch that can interpret gestures with high accuracy even during vigorous or chaotic movement. The armband uses stretchable sensors, a custom deep-learning model, and on-chip processing to clean motion signals in real time. This breakthrough could enable intuitive robot control for rehabilitation, indus…

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