Patterns最新文献

Complex systems can often be analyzed at either the microscale of their individual components or the macroscale of their collective organization, yet it remains debated which level of description offers the most meaningful causal understanding. Hoel's recent study in Patterns addresses this challenge by introducing Causal Emergence 2.0, a novel formalization showing that a system's causal workings are best described by how causal influence is distributed across its hierarchy of scales.

Complex systems can be described at myriad different scales, and their causal workings often have a multiscale structure (e.g., a computer can be described at the microscale of its hardware circuitry, the mesoscale of its machine code, and the macroscale of its operating system). While scientists study and model systems across the full hierarchy of their scales, from microphysics to macroeconomics, there is debate about what the macroscales of systems can possibly add beyond mere compression. To resolve this long-standing issue, here, a new theory of emergence is introduced that can distinguish which scales irreducibly contribute to a system's causal workings. The theory's application is demonstrated in coarse grains of Markov chains, revealing a novel measure of emergent complexity: how widely distributed a system's causal contributions are across its hierarchy of scales.

The integration of generative AI (GenAI) into academic workflows represents a fundamental shift in scientific practice. While these tools can amplify productivity, they risk eroding the cognitive foundations of expertise by simulating the very tasks through which scientific competence is developed, from synthesis to experimental design to writing. Uncritical reliance can lead to skill atrophy and AI complacency. We propose a framework of essential AI meta-skills: strategic direction, critical discernment, and systematic calibration. These constitute a new form of scientific literacy that builds on traditional critical thinking. Through domain-specific examples and a pedagogical model based on situated learning, we show how these meta-skills can be cultivated to ensure that researchers, particularly trainees, maintain intellectual autonomy. Without deliberate cultivation of these meta-skills, we risk creating the first generation of researchers who serve their tools rather than direct them.

Generative artificial intelligence can be used to create realistic new data, even for complex real-world processes that cannot be exhaustively modeled: the model is simply learned from preexisting data. Generative artificial intelligence is therefore expected to be a game changer in omics research, where data collection is hampered by considerable experimental constraints. However, it can also "hallucinate"-i.e., create data that are too original to be realistic-which is a critical issue in molecular biology, as hallucinated inferences could have devastating consequences. The author thus explores various use cases to mitigate hallucination-induced risks and to safely unleash the full potential of generative methods.

Multiplexed spatial proteomics profiling platforms expose the intricate geometric structure of cells in the tumor microenvironment (TME). The spatial arrangement of cells has been shown to have important clinical implications, correlating with disease prognosis and treatment response. These datasets require new statistical methods to test whether cell-level images are associated with patient-level outcomes. We propose the topological kernel association test (TopKAT), which combines persistent homology with kernel testing to determine whether geometric structures created by cells predict continuous, binary, or survival outcomes. TopKAT quantifies the topological structure of cells in each image using persistence diagrams and compares the similarities between persistence diagrams on the basis of the number and lifespan of the detected homologies among cells. We show that TopKAT can be more powerful than existing approaches, particularly when cells arise along the boundary of a ring and demonstrate its utility in breast cancer and colorectal cancer applications.

Large language models (LLMs) excel in textual tasks but often struggle with physical-world reasoning tasks. Inspired by human cognition-where perception is fundamental to reasoning-we explore augmenting LLMs with enhanced perception abilities using Internet of Things (IoT) data and pertinent knowledge. In this work, we systematically study LLMs' capability to address IoT-sensory tasks, by augmenting their perception and knowledge base, and then propose a unified framework, IoT-LLM, to enhance such capability. In IoT-LLM, we customize three steps: preprocessing IoT data into suitable formats, expanding LLMs' knowledge via IoT-oriented retrieval-augmented generation, and activating LLMs' commonsense knowledge through chain-of-thought prompting. We design a benchmark comprising five real-world tasks with varying data types and reasoning complexities to evaluate the performance of IoT-LLM. Experimental results reveal that IoT-LLM significantly improves the performance of IoT-sensory task reasoning of LLMs, with models such as GPT-4o-mini showing a 49.4% average improvement over previous methods.

Autonomous AI-to-AI creative systems promise new frontiers in machine creativity, yet we show that they systematically converge toward generic outputs. We built iterative feedback loops between Stable Diffusion XL (SDXL; image generation) and Large Language and Vision Assistant (LLaVA; image description), forming autonomous text → image → text → image cycles. Across 700 trajectories with diverse prompts and 7 temperature settings over 100 iterations, all runs converged to nearly identical visuals-what we term "visual elevator music." Quantitative analysis revealed just 12 dominant motifs with commercially safe aesthetics, such as stormy lighthouses and palatial interiors. This convergence persisted across model pairs, indicating structural limits in cross-modal AI creativity. The effect mirrors human cultural transmission, where iterated learning amplifies cognitive biases, but here, diversity collapses entirely as AI loops gravitate to high-probability attractors in training data. Our findings expose hidden homogenizing tendencies in current architectures and underscore the need for anti-convergence mechanisms and sustained human-AI interplay to preserve creative diversity.

Although there are ways to estimate the global power demand of artificial intelligence (AI) systems, it remains challenging to quantify the associated carbon and water footprints. The lack of distinction between AI and non-AI workloads in the environmental reports of data center operators makes it possible to assess the environmental impact of AI workloads only by approximating them through data centers' general performance metrics. The environmental disclosure of tech companies is, however, often insufficient to determine even the total data center performance of these companies. The shortcomings in the environmental disclosure of data center operators could be remedied with new policies mandating the disclosure of additional metrics. Because the environmental impact of data centers is growing rapidly, the urgency of transparency in the tech sector is also increasing. The carbon footprint of AI systems alone could be between 32.6 and 79.7 million tons of CO₂ emissions in 2025, while the water footprint could reach 312.5-764.6 billion L.

Record linkage and pseudonymization are crucial tasks in collaborative biomedical research. Data for a patient are rarely stored in one place and therefore often need to be linked and integrated across multiple institutions. Mainzelliste is an open-source software solution designed to solve these challenges by providing a comprehensive and flexible toolkit for pseudonymization, record linkage, and consent management. It supports a variety of pseudonyms, record linkage methods, and modular, informed patient consents. A highly flexible REST application programming interface (API) allows tight integration into existing applications and workflows. Since its initial release in 2015, Mainzelliste has evolved into a vibrant open-source software solution "by researchers, for researchers" including a user-friendly graphical interface, support for HL7 FHIR for consent and patient data, and record linkage based on secure multi-party computation, thereby supporting secure and efficient biomedical research.

Sleep is one of the most essential parts of our daily lives. The gold standard for studying sleep is polysomnography (PSG) recordings. The first step of analyzing PSG recordings involves splitting them into sleep stages, which is performed manually. Machine learning algorithms have attempted to automate the tedious task of sleep scoring, mostly via supervised learning. A recent study in Patterns introduces AISleep, a novel algorithm approaching the task of sleep scoring in an unsupervised framework. This algorithm is based on humanly interpretable features and provides robust results across different datasets and age groups.