How do the synthetic neurons in language models create "thought categories" to segment and analyze their informational environment? What are the cognitive characteristics, at the very level of formal neurons, of this artificial categorical thought? Based on the mathematical nature of algebraic operations inherent to neuronal aggregation functions, we attempt to identify mathematico-cognitive factors that genetically shape the categorical reconstruction of the informational world faced by artificial cognition. This study explores these concepts through the notions of priming, attention, and categorical phasing.

PearSAN is a machine learning-assisted optimization algorithm applicable to inverse design problems with large design spaces, where traditional optimizers struggle. The algorithm leverages the latent space of a generative model for rapid sampling and employs a Pearson correlated surrogate model to predict the figure of merit of the true design metric. As a showcase example, PearSAN is applied to thermophotovoltaic (TPV) metasurface design by matching the working bands between a thermal radiator and a photovoltaic cell. PearSAN can work with any pretrained generative model with a discretized latent space, making it easy to integrate with VQ-VAEs and binary autoencoders. Its novel Pearson correlational loss can be used as both a latent regularization method, similar to batch and layer normalization, and as a surrogate training loss. We compare both to previous energy matching losses, which are shown to enforce poor regularization and performance, even with upgraded affine parameters. PearSAN achieves a state-of-the-art maximum design efficiency of 97%, and is at least an order of magnitude faster than previous methods, with an improved maximum figure-of-merit gain.

Machine learning systems increasingly drive innovation across scientific fields and industry, yet challenges in compute overhead, specifically during inference, limit their scalability and sustainability. Responsible AI guardrails, essential for ensuring fairness, transparency, and privacy, further exacerbate these computational demands. This study addresses critical gaps in the literature, chiefly the lack of generalized predictive techniques for latency and energy consumption, limited cross-comparisons of classifiers, and unquantified impacts of RAI guardrails on inference performance. Using Theory Construction Methodology, this work constructed a model-agnostic theoretical framework for predicting latency and energy consumption in binary classification models during inference. The framework synthesizes classifier characteristics, dataset properties, and RAI guardrails into a unified analytical instrument. Two predictive equations are derived that capture the interplay between these factors while offering generalizability across diverse classifiers. The proposed framework provides foundational insights for designing efficient, responsible ML systems. It enables researchers to benchmark and optimize inference performance and assists practitioners in deploying scalable solutions. Finally, this work establishes a theoretical foundation for balancing computational efficiency with ethical AI principles, paving the way for future empirical validation and broader applications.

Remote Sensing (RS) data contains a wealth of multi-dimensional information crucial for Earth observation. Owing to its vast volume, diverse sources, and temporal properties, RS data is highly suitable for the development of large Visual Foundation Models (VFMs). VFMs act as robust feature extractors, learning from extensive RS data, and are subsequently fine-tuned for deployment in various geoscientific tasks. However, current VFMs in the RS domain are predominantly pretrained and tailored exclusively for specific characteristics of RS imagery, neglecting the potential of utilizing the multi-dimensional properties of RS data. Therefore, in this work, we propose SeaMo, a pioneering visual foundation model that integrates multi-seasonal and multimodal information in the RS field. SeaMo is designed to harness multiple properties of RS data. Within the masked image modeling framework, we employ non-aligned cropping techniques to extract spatial properties, use multi-source inputs for multimodal integration, and incorporate temporal-multimodal fusion blocks for effective assimilation of multi-seasonal data. SeaMo explicitly models the multi-dimensional properties of RS data, making the model more comprehensive, robust, and versatile. We applied SeaMo to several downstream geoscience tasks, which demonstrated exceptional performance. Extensive ablation studies were conducted to validate the model's superiority.

Remote sensing image change description, as a novel multimodal task in the field of remote sensing processing, not only enables the detection of changes in surface conditions but also provides detailed descriptions of these changes, thereby enhancing human interpretability and interactivity. However, previous methods mainly employed Convolutional Neural Network (CNN) architectures to extract bitemporal image features. This approach often leads to an overemphasis on designing specific network architectures and limits the captured feature distributions to the current dataset, resulting in poor generalizability and robustness when applied to other datasets or real-world scenarios. To address these limitations, this paper proposes a novel approach for remote sensing image change detection and description that integrates diffusion models, aiming to shift the focus from conventional feature learning paradigms to data distribution learning. The proposed method primarily includes a simple multi-scale change detection module, whose output features are subsequently refined using a diffusion model. Additionally, we introduce a frequency-guided complex filter module to handle high-frequency noise during the diffusion process, which helps to maintain model performance. Finally, we validate the effectiveness of our proposed method on several remote sensing change detection description datasets, demonstrating its superior performance. The code available at MaskApproxNet.

This study presents an autonomous experimental machine learning protocol for high-frequency trading (HFT) stock price forecasting that involves a dual competitive feature importance mechanism and clustering via shallow neural network topology for fast training. By incorporating the k-means algorithm into the radial basis function neural network (RBFNN), the proposed method addresses the challenges of manual clustering and the reliance on potentially uninformative features. More specifically, our approach involves a dual competitive mechanism for feature importance, combining the mean-decrease impurity (MDI) method and a gradient descent (GD) based feature importance mechanism. This approach, tested on HFT Level 1 order book data for 20 S&P 500 stocks, enhances the forecasting ability of the RBFNN regressor. Our findings suggest that an autonomous approach to feature selection and clustering is crucial, as each stock requires a different input feature space. Overall, by automating the feature selection and clustering processes, we remove the need for manual topological grid search and provide a more efficient way to predict LOB's mid-price.

The rise of AI comes with a growing carbon footprint and greater demand for electricity. Analysts project that AI could drive up data centers' energy consumption by 160% this decade. So how worried should we be? Editor at large David Rotman, senior AI reporter Melissa Heikkilä, and senior editor for energy James Temple explored the energy trade-offs involved in AI. Gene editing can correct or improve the DNA of human embryos, potentially opening the door to the "technological evolution" of our species.

Hsinchu, Taiwan – A crane bird flies across a silent rice paddy, the water slowly trickling in the background. It is a tranquil and stereotypical image of an East-Asian countryside. Little seems to suggest I am just a few kilometres removed from one of the hearts of the global economy. This is Hsinchu, a small city close to Taipei in Taiwan. It is what you could literally call the Silicon Valley of the world.

This paper proposes a Clustering, Labeling, then Augmenting framework that significantly enhances performance in Semi-Supervised Text Classification (SSTC) tasks, effectively addressing the challenge of vast datasets with limited labeled examples. Unlike traditional SSTC approaches that rely on a predefined small set of labeled data to generate pseudo-labels for the unlabeled data, this framework innovatively employs clustering to select representative "landmarks" for labeling. These landmarks subsequently act as intermediaries in an ensemble of augmentation techniques, including Retrieval-Augmented Generation (RAG), Large Language Model (LLMs)-based rewriting, and synonym substitution, to generate synthetic labeled data without making pseudo-labels for the unlabeled data. Empirical results show that even in complex text document classification scenarios involving over 100 categories, our method achieves state-of-the-art accuracies of 95.41% on the Reuters dataset and 82.43% on the Web of Science dataset. Our approach significantly reduces the reliance on human labeling efforts and the associated expenses, while simultaneously ensuring high data quality and minimizing privacy risks. The finetuning results further show the efficiency of fine-tuning LLMs for text classification tasks, highlighting a robust solution for leveraging limited labeled data.

Understanding thermal stress evolution in metal additive manufacturing (AM) is crucial for producing high-quality components. Recent advancements in machine learning (ML) have shown great potential for modeling complex multiphysics problems in metal AM. While physics-based simulations face the challenge of high computational costs, conventional data-driven ML models require large, labeled training datasets to achieve accurate predictions. Unfortunately, generating large datasets for ML model training through time-consuming experiments or high-fidelity simulations is highly expensive in metal AM. To address these challenges, this study introduces a physics-informed neural network (PINN) framework that incorporates governing physical laws into deep neural networks (NNs) to predict temperature and thermal stress evolution during the laser metal deposition (LMD) process. The study also discusses the enhanced accuracy and efficiency of the PINN model when supplemented with small simulation data. Furthermore, it highlights the PINN transferability, enabling fast predictions with a set of new process parameters using a pre-trained PINN model as an online soft sensor, significantly reducing computation time compared to physics-based numerical models while maintaining accuracy.