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Interpretability has drawn increasing attention in machine learning. Most works focus on post-hoc explanations rather than building a self-explaining model. So, we propose a Neural Partially Linear Additive Model (NPLAM), which automatically distinguishes insignificant, linear, and nonlinear features in neural networks. On the one hand, neural network construction fits data better than spline function under the same parameter amount; on the other hand, learnable gate design and sparsity regular-term maintain the ability of feature selection and structure discovery. We theoretically establish the generalization error bounds of the proposed method with Rademacher complexity. Experiments based on both simulations and real-world datasets verify its good performance and interpretability.

The present research utilized diffusion bonding to create a metallurgically bonded Incoloy 825/carbon steel bimetal. The interfacial bonding behavior of bimetals was investigated using a variety of heat treatment technologies. The interfacial bonding shear strength increased initially and then decreased with increasing tempering time, reaching a maximum at 45 minutes. The cohesion strength was attributed to the diffusion distance and diffusion gradient of chromium in terms of interfacial diffusion behavior. The precipitation behavior of carbides and their associated cohesion strength were observed as a function of tempering time using an image-based analysis technique. The uniform diameter, area, and sphericity of the carbides resulted in a high cohesion strength. Additionally, a nanoindentation technique was used to characterize the interfacial cohesion behavior, taking into account dislocation multiplication and localized mechanical behavior during deformation. The critical shear strength of dissimilar metals near the bimetal interface reaches its maximum and the geometrically necessary dislocation (GN(D) densities remain constant after 45 minutes of tempering, indicating that a high interfacial bonded shear strength may be attributed to localized compatible deformation of dissimilar metals near the bimetal interface.

Robust group lasso regression plays an important role in high-dimensional regression modeling such as biological data analysis for disease diagnosis and gene expression. However, most existing methods are optimized with prior variable structure knowledge under the traditional empirical risk minimization (ERM) framework, in which the estimators are excessively dependent on prior structure information and sensitive to outliers. To address this issue, we propose a new robust variable structure discovery method for group lasso based on a convergent bilevel optimization framework. In this paper, we adopt tilted empirical risk minimization (TERM) as the target function to improve the robustness of the estimator by assigning less weight to the outliers. Moreover, we modify the TERM objective function to calculate its Fenchel conjugate while maintaining its robust property, which is proven theoretically and empirically. Experimental results on both synthetic and real-world datasets show that the proposed method can improve the robust performance on prediction and variable structure discovery compared to the existing techniques.

Autonomous agents for desktop automation struggle with complex multi-step tasks due to poor coordination and inadequate quality control. We introduce Agentic Lybic, a novel multi-agent system where the entire architecture operates as a finite-state machine (FSM). This core innovation enables dynamic orchestration. Our system comprises four components: a Controller, a Manager, three Workers (Technician for code-based operations, Operator for GUI interactions, and Analyst for decision support), and an Evaluator. The critical mechanism is the FSM-based routing between these components, which provides flexibility and generalization by dynamically selecting the optimal execution strategy for each subtask. This principled orchestration, combined with robust quality gating, enables adaptive replanning and error recovery. Evaluated officially on the OSWorld benchmark, Agentic Lybic achieves a state-of-the-art 57.07% success rate in 50 steps, substantially outperforming existing methods. Results demonstrate that principled multi-agent orchestration with continuous quality control provides superior reliability for generalized desktop automation in complex computing environments.