Recent advancements in convolutional neural networks (CNNs) have significantly propelled the field of remote sensing forestry mapping. However, traditional convolution operations exhibit inherent limitations in extracting complex forest features: their fixed receptive fields struggle to accommodate multi-scale forest attributes, and their insufficient focus on background information impairs the overall feature representation. To address these challenges, we propose Similar Convolution (SimConv), which introduces dynamic convolution kernel size selection by modeling feature relationships. SimConv adaptively adjusts the receptive field based on the semantic relevance of input features, enhancing the capture of forestry background information and improving the distinction between target features. Building upon this, we introduce SIMNet, a feature extraction network that integrates SimConv at its core. Experimental results across multiple remote sensing datasets demonstrate that SIMNet outperforms existing methods in terms of feature extraction accuracy. code in https://github.com/WangShiK/SimConv.

Supernumerary Robotic Limbs (SRLs) can assist human motions by providing extra degrees of freedom (DoFs) and body support. The extra DoFs lead to larger design space in structure and control policies, which is complex and time-consuming with the traditional manual design process. In this pilot study, we proposed a novel morphology-controller co-optimization framework to automatically generate and optimize the SRL structure based on the locomotion task input. There are two layers, with the inner layer optimizing the controller to achieve human-robot synchronization, and the outer layer optimizing the morphology parameters for performance enhancement. We validated the proposed framework through simulations using SRLs in a load-bearing locomotion task. The results demonstrate that the controller optimization can automatically generate realistic gait patterns and stable human-robot synchronization, while the SRLs significantly improve the user's load-bearing capability. Additionally, the co-optimization process reduces both the manufacturing cost of the SRL and the torque on the joints. This approach shows potential for exhaustive exploration of the design space and acceleration of the design process. Future works will be done in a more realistic SRL generative design model and achieve Sim2Real for practical uses.

Abstract Metonymy resolution (MR) is a challenging task in the field of natural language processing. The task of MR aims to identify the metonymic usage of a word that employs an entity name to refer to another target entity. Recent BERT-based methods yield state-of-the-art performances. However, they neither make full use of the entity information nor explicitly consider syntactic structure. In contrast, in this paper, we argue that the metonymic process should be completed in a collaborative manner, relying on both lexical semantics and syntactic structure (syntax). This paper proposes a novel approach to enhancing BERT-based MR models with hard and soft syntactic constraints by using different types of convolutional neural networks to model dependency parse trees. Experimental results on benchmark datasets (e.g., ReLocaR, SemEval 2007 and WiMCor) confirm that leveraging syntactic information into fine pre-trained language models benefits MR tasks.