Advanced manufacturing technologies allow for the production of intricate parts featuring high shape complexity and spatially-varying material composition. Data fusion of point clouds with chromatic attributes provides 4D point clouds, a compact and informative representation that encodes both shape and material information. In this paper, we present a registration-free framework for Simultaneous Monitoring of shApe and Color (SMAC) via 4D point clouds. The proposed framework leverages Laplace-Beltrami operator spectral properties to capture and monitor geometric features and the relationship between shape and surface color. A combined monitoring scheme is proposed to effectively detect shape deformations and color anomalies, along with a spatially-aware post-signal diagnostic procedure to determine the source of change and localize color anomalies. Importantly, neither component relies on registration or mesh reconstruction, eliminating error-prone and computationally expensive preprocessing steps. A Monte Carlo simulation study and a case study on functionally graded materials demonstrate that SMAC achieves effective detection performance, particularly for subtle defects, while providing diagnostic capabilities to identify the source and location of anomalies.

Recent work on causal abstraction, in particular graphical approaches focusing on causal structure between clusters of variables, aims to summarize a high-dimensional causal structure in terms of a low-dimensional one. Existing methods for learning such summaries from data assume that both the high- and low-dimensional structures are acyclic, which is helpful for causal effect identification and reasoning but excludes many high-dimensional models and thus limits applicability. We show that in the linear non-Gaussian (LiNG) setting, the high-dimensional acyclicity assumption can be relaxed while still allowing recovery of a low-dimensional causal directed acyclic graph (DAG). We further connect identifiability of this low-dimensional DAG to existing results: LiNG models with cycles are observationally identifiable only up to an equivalence class whose members differ by reversals of directed cycles; our low-dimensional DAG, which is invariant across all members of a given equivalence class, thus forms a natural representative of the class. While existing approaches for learning this observational equivalence class over high-dimensional variables have exponential time complexity, our low-dimensional summary is learned in worst-case cubic time and comes with explicit bounds on the sample complexity. We provide open source code and experiments on synthetic data to corroborate our theoretical results.

How do you go about integrating causal knowledge into decision systems or agents? We sat down with Matteo Ceriscioli to find out about his research in this space. This interview is the latest in our series featuring the AAAI/SIGAI Doctoral Consortium participants. Could you start by telling us a bit about your PhD - where are you studying, and what's the broad topic of your research? The idea is to integrate causal knowledge into agents or decision systems to make them more reliable.

Multi-layered distributed representation is believed to be the key ingredient of deep neural networks especially in cognitive tasks like computer vision. While non-differentiable models such as gradient boosting decision trees (GBDTs) are still the dominant methods for modeling discrete or tabular data, they are hard to incorporate with such representation learning ability. In this work, we propose the multi-layered GBDT forest (mGBDTs), with an explicit emphasis on exploring the ability to learn hierarchical distributed representations by stacking several layers of regression GBDTs as its building block. The model can be jointly trained by a variant of target propagation across layers, without the need to derive backpropagation nor differentiability. Experiments confirmed the effectiveness of the model in terms of performance and representation learning ability.

Neural Information Processing Systems http://nips.cc/

PCWorld explains how Safe Mode serves as a critical troubleshooting tool when Windows fails to boot by loading only essential system components. Safe Mode enables users to identify problematic drivers, uninstall recent programs, run system repairs like SFC and DISM, and access System Restore. Key diagnostic tools include boot logging to identify crash-causing drivers, Device Manager for driver rollbacks, and startup management through Task Manager. If your Windows PC won't start properly or keeps crashing, Safe Mode can help you identify the cause and fix the problem. In Safe Mode, Windows only loads the most essential drivers and services, skips third-party autostart programs, and uses a simple graphical user interface. This allows you to disable faulty drivers, software, or malware-since these do not run in Safe Mode.

In fact, 8 out of the 19 datasets used in the experiments were13 multiclass tasks.