Law Discovery
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Data-driven Progressive Discovery of Physical Laws
Announce Type: replace Abstract: Symbolic regression is a powerful tool for knowledge discovery, enabling the extraction of interpretable mathematical expressions directly from data. However, conventional symbolic discovery typically follows an end-to-end, "one-step" process, which often generates lengthy and physically meaningless expressions when dealing with real physical systems, leading to poor model generalization. This limitation fundamentally stems from its deviation from the basic...
Data-driven Progressive Discovery of Physical Laws
Announce Type: replace-cross Abstract: Symbolic regression is a powerful tool for knowledge discovery, enabling the extraction of interpretable mathematical expressions directly from data. However, conventional symbolic discovery typically follows an end-to-end, "one-step" process, which often generates lengthy and physically meaningless expressions when dealing with real physical systems, leading to poor model generalization. This limitation fundamentally stems from its deviation from the...
Robust Causal Discovery in Real-World Time Series with Power-Laws
arXiv:2507.12257v4 Announce Type: replace Abstract: Exploring causal relationships in stochastic time series is a challenging yet crucial task with a vast range of applications, including finance, economics, neuroscience, and climate science. Many algorithms for Causal Discovery (CD) have been proposed; however, they often exhibit a high sensitivity to noise, resulting in spurious causal inferences in real data. In this paper, we observe that the frequency spectra of many real-world time...
Robust Causal Discovery in Real-World Time Series with Power-Laws
arXiv:2507.12257v4 Announce Type: replace-cross Abstract: Exploring causal relationships in stochastic time series is a challenging yet crucial task with a vast range of applications, including finance, economics, neuroscience, and climate science. Many algorithms for Causal Discovery (CD) have been proposed; however, they often exhibit a high sensitivity to noise, resulting in spurious causal inferences in real data. In this paper, we observe that the frequency spectra of many real-world...
Data-driven discovery of governing differential equations across physical systems
Announce Type: new Abstract: Differential equations play a critical role in scientific discovery because they provide a mathematical framework to describe the behaviour of physical phenomena. As a promising alternative to traditional first principles, data-driven differential equation discovery has attracted increasing attention for its ability to infer governing laws directly from experimental or simulated data, especially when the underlying physics is unclear. However, the field has...
Data-driven discovery of governing differential equations across physical systems
Announce Type: cross Abstract: Differential equations play a critical role in scientific discovery because they provide a mathematical framework to describe the behaviour of physical phenomena. As a promising alternative to traditional first principles, data-driven differential equation discovery has attracted increasing attention for its ability to infer governing laws directly from experimental or simulated data, especially when the underlying physics is unclear. However, the field has...
Predictable Scaling Laws of Optimal Hyperparameters for LLM Continued Pre-training
arXiv:2606.05610v1 Announce Type: new Abstract: The efficacy of continued pre-training for Large Language Models (LLMs) hinges upon hyperparameter configurations, such as learning rate and batch size. However, current practices often rely on heuristics or grid searches, leading to training instability and excessive costs. In this work, we first empirically discover that optimal hyperparameters follow stable and predictable scaling laws throughout the continued pre-training process.
How plants survive constant DNA damage: Newly identified repair protein protects growth-critical stem cells
How plants survive constant DNA damage: Newly identified repair protein protects growth-critical stem cells Robert Egan Associate Editor Similar to the way DNA damage can contribute to human diseases such as cancer, it can also disrupt growth, development and survival in plants. Every day, plants endure environmental stresses such as sunlight, radiation, drought and soil stress—all of which can damage their DNA. However, they cannot move away from danger.
Deep reinforcement learning with spatial and temporal awareness for active boundary control of buoyancy-driven convection
arXiv:2606.06191v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) applied to thermal convection control consistently produces \textit{degenerate actuation}: wall-temperature policies whose outputs are saturated, pseudo-random, or spatially incoherent. Two compounding deficiencies are responsible: multilayer-perceptron policies that discard spatial flow structure, and memoryless policies that cannot distinguish self-induced flow changes from background evolution. Together they...
Trump’s $10 billion BBC lawsuit hits a wall over missing financial records
Trump’s $10 billion BBC lawsuit hits a wall over missing financial records Trump initiated the lawsuit, alleging the publicly funded BBC defamed him by selectively splicing together parts of a January 6, 2021, speech to make it appear that he directed supporters to storm the U.S. Capitol - Bookmark - CommentsGo to comments Donald Trump's legal team has reportedly declined to provide crucial financial information sought by BBC lawyers in his $10 billion defamation lawsuit against the British...