Abstracts:Neural networks have achieved remarkable success across various fields. However, the lack of interpretability limits their practical use, particularly in critical decision-making scenarios. Posthoc interpretability, which provides explanations for pretrained models, is often at risk of fidelity and robustness. This has inspired a rising interest in self-interpretable neural networks (SINNs), which inherently reveal the prediction rationale through model structures. Despite this progress, existing research remains fragmented, relying on intuitive designs tailored to specific tasks. To bridge these efforts and foster a unified framework, we first collect and review existing works on SINNs and provide a structured summary of their methodologies from five key perspectives: attribution-based, function-based, concept-based, prototype-based, and rule-based self-interpretation. We also present concrete, visualized examples of model explanations and discuss their applicability across diverse scenarios, including image, text, graph data, and deep reinforcement learning (DRL). Additionally, we summarize existing evaluation metrics for self-interpretation and identify open challenges in this field, offering insights for future research. To support ongoing developments, we present a publicly accessible resource to track advancements in this domain: https://github.com/yangji721/Awesome-Self-Interpretable-Neural-Network

Abstracts:A correction is given to a previously published result concerned with the relationship between a suitably defined matrix seminorm for consensus analysis and a coefficient of ergodicity.

Abstracts:Deformation of flexible robots can be practically assessed using extension/compression, shear, curvature, and torsion. Sensing based on one or more of the above characteristics enables closed-loop control for delicate tasks that require precision and dexterity. Due to the increasing popularity of flexible robotics in recent years, significant research effort has been directed to this burgeoning field. Although numerous studies have addressed soft sensing technologies, their successful integration into flexible robotic systems remains limited. This article provides a comprehensive review of sensing methods, from multidimensional deformation to the underlying principles of deriving hard-to-measure deformation from surrogate parameters. It focuses on sensing modalities such as strain measurement via piezoelectric, capacitive, resistive, and optical techniques. The applications of deformation sensing in industrial and service robotics are described. Future challenges and potential research issues including resolution, conformability, multifunctionality, crosstalk, and miniaturization are discussed. The need for a synergistic approach across disciplines is highlighted, emphasizing the integration of new materials, microstructures, advanced manufacturing technologies, and state-of-the-art signal processing techniques.

Abstracts:In the past few years, there has been a renewed effort to advance general-purpose architectures. In particular, to deliver performance and energy efficiency advantages, several techniques have been applied based on new forms of specialization while maintaining usability. As a result, data movement and communication have become the primary bottlenecks in computer systems. To overcome this, one of the most recent breakthroughs has been the introduction of data streaming mechanisms, just like those used in accelerators, into modern general-purpose processors (GPPs). This article comprehensively reviews stream-based architectures, tracing their development from accelerator solutions to their recent adoption in GPPs. This survey starts by introducing the fundamental principles of stream specialization, followed by a taxonomy for memory accesses, and formal mathematical models to represent them as data streams. Then, it categorizes different topologies of data stream specialization and examines them from a compiler’s perspective. Some of the most representative architectures proposed in the past few years, including instruction set architecture (ISA) and streaming engines, are described, followed by a comparative analysis that highlights their key features and presents quantitative evaluations. Then, we discuss some open challenges and suggest directions for future research in stream-based architectures.