The Half-Trek Criterion (HTC) is the primary graphical tool for determining generic identifiability of causal effect coefficients in linear structural equation models (SEMs) with latent confounders. However, HTC is inherently node-wise: it simultaneously resolves all incoming edges of a node, leaving a gap of "inconclusive" causal effects (15-23% in moderate graphs). We introduce Iterative Identification Closure (IIC), a general framework that decouples causal identification into two phases: (1) a seed function S_0 that identifies an initial set of edges from any external source of information (instrumental variables, interventions, non-Gaussianity, prior knowledge, etc.); and (2) Reduced HTC propagation that iteratively substitutes known coefficients to reduce system dimension, enabling identification of edges that standard HTC cannot resolve. The core novelty is iterative identification propagation: newly identified edges feed back to unlock further identification -- a mechanism absent from all existing graphical criteria, which treat each edge (or node) in isolation. This propagation is non-trivial: coefficient substitution alters the covariance structure, and soundness requires proving that the modified Jacobian retains generic full rank -- a new theoretical result (Reduced HTC Theorem). We prove that IIC is sound, monotone, converges in O(|E|) iterations (empirically <=2), and strictly subsumes both HTC and ancestor decomposition. Exhaustive verification on all graphs with n<=5 (134,144 edges) confirms 100% precision (zero false positives); with combined seeds, IIC reduces the HTC gap by over 80%. The propagation gain is gamma~4x (2 seeds identifying ~3% of edges to 97.5% total identification), far exceeding gamma<=1.2x of prior methods that incorporate side information without iterative feedback.

The problem of model selection is considered for the setting of interpolating estimators, where the number of model parameters exceeds the size of the dataset. Classical information criteria typically consider the large-data limit, penalizing model size. However, these criteria are not appropriate in modern settings where overparameterized models tend to perform well. For any overparameterized model, we show that there exists a dual underparameterized model that possesses the same marginal likelihood, thus establishing a form of Bayesian duality. This enables more classical methods to be used in the overparameterized setting, revealing the Interpolating Information Criterion, a measure of model quality that naturally incorporates the choice of prior into the model selection. Our new information criterion accounts for prior misspecification, geometric and spectral properties of the model, and is numerically consistent with known empirical and theoretical behavior in this regime.

While widespread use of artificial intelligence (AI) and machine learning in manufacturing may still be several years off, both technologies are beginning to make their way onto the plant floor. Fueled by unprecedented amounts of data delivered via connected sensors and devices, properly trained AI algorithms can help optimize production processes, simplify quality control procedures, and enable various types of industrial autonomy. Still, being a relatively new technology, standardized frameworks for AI are currently lacking, which could hinder their further application in industry. For example, the lack of such frameworks could result in implementation difficulties, a lack of interoperability with other systems, and cybersecurity vulnerabilities. To this end, the Industry IoT Consortium (IIC) recently announced its development of the Industrial IoT Artificial Intelligence Framework (IIAIF).

While most consumers may find Internet of Things (IoT) devices like Google's Nest or Ring's doorbells new and exciting technology, the manufacturing world has embraced the IoT to optimize discrete and process manufacturing operations for decades. The industrial IoT (IIoT), which started as remote sensing of things like temperature and pressure, has today matured into a way of linking operational systems that control production with the wider world of applications outside of the control room like ERP platforms and supply chain management systems. "The major benefits of the industrial IoT is to bring more visibility to existing processes," said report author Jaques Durand, director of Standards and Engineering at Fujitsu North America and a member of the Industrial Internet Consortium Steering Committee. People want to understand what's going on." Getting to an advanced state of IIoT usage can be difficult without understanding the mistakes to avoid along the way. That's why the Industrial Internet Consortium (IIC), has spent the last six years developing and deploying testbeds for manufacturers to use when evaluating different IIoT technologies, platforms, designs, products, architectures, and use cases. Based on the results of these testbed proofs-of-concept (POC), today the IIC released a white paper, A Compilation of Testbed Results: Toward Best Practices for Developing and Deploying IIoT Solutions, detailing the best practices companies should adopt to ensure successful IIoT deployments. "The IoT problem that each company is facing or each organization is facing is different," Durand said. "Even if they use the same technologies, which is not granted, they are facing very different conditions and priorities in real-world conditions.

Ontology engineering is a hard and error-prone task, in which small changes may lead to errors, or even produce an inconsistent ontology. As ontologies grow in size, the need for automated methods for repairing inconsistencies while preserving as much of the original knowledge as possible increases. Most previous approaches to this task are based on removing a few axioms from the ontology to regain consistency. We propose a new method based on weakening these axioms to make them less restrictive, employing the use of refinement operators. We introduce the theoretical framework for weakening DL ontologies, propose algorithms to repair ontologies based on the framework, and provide an analysis of the computational complexity. Through an empirical analysis made over real-life ontologies, we show that our approach preserves significantly more of the original knowledge of the ontology than removing axioms.