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Abstract This article presents a novel approach for generating metamaterial designs by leveraging texture information learned from stochastic microstructure samples with exceptional mechanical properties. This eXplainable Artificial Intelligence (XAI)-based approach reduces the reliance on brainstorming and trial-and-error in inspiration-driven design practices. The key research question is whether the texture information extracted from stochastic microstructure samples can be used to design metamaterials with periodic structural patterns that surpass the original stochastic microstructures in mechanical properties. The proposed approach employs a pretrained supervised neural network and applies the Activation Maximization Texture Synthesis (AMTS) method to extract representative textures from high-performance stochastic microstructure samples. These textures serve as building blocks for creating novel periodic metamaterial designs. Using three benchmark cases of stochastic microstructure-inspired periodic metamaterial design, we compare the proposed approach with an earlier XAI design approach based on Gradient-weighted Regression Activation Mapping (Grad-RAM). Unlike the proposed approach, Grad-RAM extracts local microstructure patches directly from the original sample images rather than synthesizing representative textures to generate novel periodic metamaterial designs. Both XAI-based design approaches are evaluated based on the mechanical properties of the resulting designs. The relative merits of both approaches in terms of design performance and the need for human intervention are discussed. 

We study an overdamped Langevin equation on the $$d$$-dimensional torus with stationary distribution proportional to $$p = e^{-U / \kappa}$$. When $$U$$ has multiple wells the mixing time of the associated process is exponentially large (of size $$e^{O(1/\kappa)}$$). We add a drift to the Langevin dynamics (without changing the stationary distribution) and obtain quantitative estimates on the mixing time. Our main result shows that the mixing time of the Langevin system can be made arbitrarily small by adding a drift that is sufficiently mixing. We provide one construction of a mixing drift, and our main result can be applied by using this drift with a large amplitude. For numerical purposes, it is useful to keep the size of the imposed drift small, and we show that the smallest allowable amplitude ensures that the mixing time is $$O( d/\kappa^2)$$, which is an order of magnitude smaller than $$e^{O(1/\kappa)}$$. 

Artificial intelligence (AI) supported network traffic classification (NTC) has been developed lately for network measurement and quality-of-service (QoS) purposes. More recently, federated learning (FL) approach has been promoted for distributed NTC development due to its nature of unshared dataset for better privacy and confidentiality in raw networking data collection and sharing. However, network measurement still require invasive probes and constant traffic monitoring. In this paper, we propose a non-invasive network traffic estimation and user profiling mechanism by leveraging label inference of FL-based NTC. In specific, the proposed scheme only monitors weight differences in FL model updates from a targeting user and recovers its network application (APP) labels as well as a rough estimate on the traffic pattern. Assuming a slotted FL update mechanism, the proposed scheme further maps inferred labels from multiple slots to different profiling classes that depend on, e.g., QoS and APP categorization. Without loss of generality, user profiles are determined based on normalized productivity, entertainment, and casual usage scores derived from an existing commercial router and its backend server. A slot extension mechanism is further developed for more accurate profiling beyond raw traffic measurement. Evaluations conducted on seven popular APPs across three user profiles demonstrate that our approach can achieve accurate networking user profiling without invasive physical probes nor constant traffic monitoring. 

A critical use case of SLAM for mobile robots is to support localization during task-directed navigation. Current SLAM benchmarks overlook the importance of repeatability (precision) despite its impact on real-world deployments. TaskSLAM-Bench, a task-driven approach to SLAM benchmarking, addresses this gap. It employs precision as a key metric, accounts for SLAM’s mapping capabilities, and has easy-to-meet requirements. Simulated and real-world evaluation of SLAM methods provide insights into the navigation performance of modern visual and LiDAR SLAM solutions. The outcomes show that passive stereo SLAM precision may match that of 2D LiDAR SLAM in indoor environments. TaskSLAM-Bench complements existing benchmarks and offers richer assessment of SLAM performance in navigation-focused scenarios. Publicly available code permits in-situ SLAM testing in custom environments with properly equipped robots.