Accurate biodiversity identification from large-scale field data is a foundational problem with direct impact on ecology, conservation, and environmental monitoring. In practice, the core task is taxonomic prediction – inferring order, family, genus, or species from imperfect inputs such as specimen images, DNA barcodes, or both. Existing multimodal methods often treat taxonomy as a flat label space and therefore fail to encode the hierarchical structure of biological classification, which is critical for robustness under noise and missing modalities. […]

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Aristotle wanders the living world like an ancient naturalist with a notebook, cataloging creatures from cuttlefish to camels. He observes how animals live, breed, and differ—sometimes brilliantly, sometimes oddly—laying one of science’s first foundations: that nature, in all its strangeness, can be studied with patient curiosity.

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Learning robust audio-visual embeddings requires bringing genuinely related audio and visual signals together while filtering out incidental co-occurrences – background noise, unrelated elements, or unannotated events. Most contrastive and triplet-loss methods use sparse annotated labels per clip and treat any co-occurrence as semantic similarity. For example, a video labeled "train" might also contain motorcycle audio and visual, because "motorcycle" is not the chosen annotation; standard methods treat these co-occurrences as negatives to true motorcycle anchors elsewhere, creating false […]

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arXiv:2603.02231v1 Announce Type: new Abstract: Large-scale wave field reconstruction requires precise solutions but faces challenges with computational efficiency and accuracy. The physics-based numerical methods like Finite Element Method (FEM) provide high accuracy but struggle with large-scale or high-frequency problems due to prohibitive computational costs. Pure data-driven approaches excel in speed but often lack sufficient labeled data for complex scenarios. Physics-informed neural networks (PINNs) integrate physical principles into machine learning models, offering a promising solution by bridging these gaps. […]

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arXiv:2603.17069v1 Announce Type: new Abstract: Falls in wet bathroom environments are a major safety risk for seniors living alone. Recent work has shown that mmWave-only, vibration-only, and existing multimodal schemes, such as vibration-triggered radar activation, early feature concatenation, and decision-level score fusion, can support privacy-preserving, non-intrusive fall detection. However, these designs still treat motion and impact as loosely coupled streams, depending on coarse temporal alignment and amplitude thresholds, and do not explicitly encode the causal link between radar-observed […]

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Writing a research paper is brutal. Even after the experiments are done, a researcher still faces weeks of translating messy lab notes, scattered results tables, and half-formed ideas into a polished, logically coherent manuscript formatted precisely to a conference’s specifications. For many fresh researchers, that translation work is where papers go to die. A team at Google Cloud AI Research propose ‘PaperOrchestra‘, a multi-agent system that autonomously converts unstructured pre-writing materials — a rough idea summary and raw […]

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Traditional data platforms rely on centralized monoliths and complex engineering pipelines that often create bottlenecks for business decision-makers. This article introduces Da2a, an open-source prototype that proposes a decentralized, agentic architecture where specialized Domain Agents (e.g., Marketing, E-commerce) collaborate through a Root Orchestrator using the Agent-to-Agent (A2A) protocol. By treating data sources as autonomous experts that communicate via standardized “agent cards” rather than static tables, Da2a demonstrates how multi-agent systems can intuitively solve complex cross-domain problems, offering a […]

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Creating and sharing knowledge are defining traits of humankind, yet copyright law has grown so restrictive that it can require acts of civil disobedience to ensure that students and scholars have the books they need and to preserve swaths of culture from being lost forever. Reputable research generally follows a familiar pattern: Scientific articles are written by scholars based on their research—often with public funding. Those articles are then peer-reviewed by other scholars in their fields and revisions […]

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arXiv:2601.08100v1 Announce Type: new Abstract: Understanding the generalization behavior of deep neural networks remains a fundamental challenge in modern statistical learning theory. Among existing approaches, PAC-Bayesian norm-based bounds have demonstrated particular promise due to their data-dependent nature and their ability to capture algorithmic and geometric properties of learned models. However, most existing results rely on isotropic Gaussian posteriors, heavy use of spectral-norm concentration for weight perturbations, and largely architecture-agnostic analyses, which together limit both the tightness and practical […]

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arXiv:2601.19965v1 Announce Type: new Abstract: In industrial recommender systems, conversion rate (CVR) is widely used for traffic allocation, but it fails to fully reflect recommendation effectiveness because it ignores refund behavior. To better capture true user satisfaction and business value, net conversion rate (NetCVR), defined as the probability that a clicked item is purchased and not refunded, has been proposed.Unlike CVR, NetCVR prediction involves a more complex multi-stage cascaded delayed feedback process. The two cascaded delays from click […]

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