Marine ecosystem monitoring via Passive Acoustic Monitoring (PAM) generates vast data, but deep learning often requires precise annotations and short segments. We introduce DSMIL-LocNet, a Multiple Instance Learning framework for whale call detection and localization using only bag-level labels. Our dual-stream model processes 2-30 minute audio segments, leveraging spectral and temporal features with attention-based instance selection. Tests on Antarctic whale data show longer contexts improve classification (F1: 0.8-0.9) while medium instances ensure localization precision (0.65-0.70). This suggests MIL can enhance scalable marine monitoring. Code: https://github.com/Ragib-Amin-Nihal/DSMIL-Loc

Large language models (LLMs), with demonstrated reasoning abilities across multiple domains, are largely underexplored for time-series reasoning (TsR), which is ubiquitous in the real world. In this work, we propose TimerBed, the first comprehensive testbed for evaluating LLMs' TsR performance. Specifically, TimerBed includes stratified reasoning patterns with real-world tasks, comprehensive combinations of LLMs and reasoning strategies, and various supervised models as comparison anchors. We perform extensive experiments with TimerBed, test multiple current beliefs, and verify the initial failures of LLMs in TsR, evidenced by the ineffectiveness of zero shot (ZST) and performance degradation of few shot in-context learning (ICL). Further, we identify one possible root cause: the numerical modeling of data. To address this, we propose a prompt-based solution VL-Time, using visualization-modeled data and language-guided reasoning. Experimental results demonstrate that Vl-Time enables multimodal LLMs to be non-trivial ZST and powerful ICL reasoners for time series, achieving about 140% average performance improvement and 99% average token costs reduction.

Sperm whale calls are far more complex than we thought – and could be an animal communication system that is the closest thing to human language yet discovered. The claim is based on an analysis of thousands of exchanges made by east Caribbean sperm whales (Physeter macrocephalus), which were recorded over several years. "It's really extraordinary to see the possibility of another species on this planet having the capacity for communication," says Daniela Rus at the Massachusetts Institute of Technology. "We used to believe that we are the only ones." Sperm whales are long-lived animals with complex social lives, with females and their young living in small groups.

As the sun rises over the island of American Samoa, a chorus of animal voices drifts upward. They're not the calls of birds, though -- the purrs, clicks and groans are coming from under the water. New research shows how automation can make it increasingly easy to eavesdrop on the fish making the sounds and uncover how their environment impacts them. Jill Munger first heard about fish that make sounds while she was an undergraduate student. A veteran researcher told her about marine acoustics.

This is an Inside Science story. As the sun rises over the island of American Samoa, a chorus of animal voices drifts upward. They're not the calls of birds, though -- the purrs, clicks and groans are coming from under the water. New research shows how automation can make it increasingly easy to eavesdrop on the fish making the sounds and uncover how their environment impacts them. Jill Munger first heard about fish that make sounds while she was an undergraduate student.

Machine learning is taking the tech world by storm. Recently, an announcement that Google was open-sourcing Tensor Flow, their machine learning (ML) software, and Microsoft quickly followed suit. Baidu and Amazon unveiled their own deep learning platforms a few months later, while Facebook began supporting the development of two ML frameworks. But the revolution has spread far beyond the tech realm. As machine learning (ML) continues to take over the tech world, companies and researchers outside the tech bubble have started using ML in strange and surprising ways.