In the realm of autonomous driving, achieving precise 3D reconstruction of the driving environment is critical for ensuring safety and effective navigation. Neural Radiance Fields (NeRF) have shown promise in creating highly detailed and accurate models ...

3D object detection plays a pivotal role in autonomous driving and robotics, demanding precise interpretation of Bird's Eye View (BEV) images. The dynamic nature of real-world environments necessitates the use of dynamic query mechanisms in 3D object ...

In recent years, subspace clustering (SC) methods have been widely used in machine learning and computer vision. However, the self-expressive matrix obtained by the existing SC methods is not clean block diagram and still contains some of typical ...

Pathological images are important for diagnosis and prognosis of oral squamous cell carcinoma (OSCC). However, it is difficult for pathologists to directly apply intuitive pathological image information to predict prognosis. Applying supervised ...

• An intelligent system for the diagnosis and prognosis of OSCC was designed.
• The new SPAT_SmSL paradigm demonstrated superior performance in WSIs recognition.
• SPAT_SmSL-based TIL-score exhibited strong predictive ability of overall ...

Generating high-quality 3D assets from a given image is highly desirable in various applications such as AR/VR. Recent advances in single-image 3D generation explore feed-forward models that learn to infer the 3D model of an object without ...

Seam carving is an image editing method that enables content-aware resizing, including operations like removing objects. However, the seam-finding strategy based on dynamic programming or graph-cut limits its applications to broader visual data ...

6D object pose estimation aims at determining an object’s translation, rotation, and scale, typically from a single RGBD image. Recent advancements have expanded this estimation from instance-level to category-level, allowing models to generalize ...

Witness validation is a formal verification method to independently verify software verification tool results, with two main categories: violation and correctness witness validators. Validators for violation witnesses in Java include Wit4Java and GWIT, ...

A comprehensive and high-quality lexicon plays a crucial role in traditional text classification approaches. It improves the utilization of linguistic knowledge. Although it is helpful for this task, the lexicon has received little attention in ...

The increasing complexity of social activities requires an expanding number of people to be involved in decision-making, so that the large-scale multi-attribute group decision-making problems have gained widespread attention. After overviewing ...

• Propose a novel dual consensus method which considers alternatives and attributes.
• The decision-making process is realized by particle swarm optimization algorithm.
• Attribute weights contain both subjective and objective ...

ReRAM-based accelerators have become prevalent in accelerating deep neural network inference owing to their in-situ computing capability of ReRAM crossbars. However, most existing ReRAM-based accelerators are designed with homogeneous crossbars, leading ...

• Large groups need to seek decision representatives to participate in consensus negotiations.
• Local and global influence can be estimated based on centrality metrics of social networks.
• Representatives codetermined by strong and ...

In the era of social media, the issue of large-scale group decision-making (LSGDM) is becoming increasingly prominent. The complexity of large group interactions increases rapidly with the expansion of the group size. Current research has often ...

Unpleasant social interactions on the road can negatively affect driving safety. At the same time, researchers have attempted to address social discomfort by exploring Conversational User Interfaces (CUIs) as social mediators. Before knowing whether ...

As container technology and microservices mature, applications increasingly shift to microservices and cloud deployment. Growing microservices scale complicates resource scheduling. Traditional methods, based on fixed thresholds, are simple but ...

Given the escalating global prevalence of non-communicable chronic diseases (NCDs), how to maintain health and obtain a higher quality of life has emerged as a prominent societal issue. To meet this problem, Proactive Health, regarded as the ...

There is substantial attention to federated learning with its ability to train a powerful global model collaboratively while protecting data privacy. Despite its many advantages, federated learning is vulnerable to backdoor attacks, where an adversary ...

This study is concerned with the computationally efficient robust model predictive control (MPC) for discrete-time stochastic switching systems. Aiming at achieving optimal control synthesis under the requirement of bumpless transfer control (BTC)...

The Centralized Training and Decentralized Execution (CTDE) paradigm, where a centralized critic is allowed to access global information during the training phase while maintaining the learned policies executed with only local information in a ...

Diffusion models (DM) can gradually learn to remove noise, which have been widely used in artificial intelligence generated content (AIGC) in recent years. The property of DM for eliminating noise leads us to wonder whether DM can be applied to wireless ...

This paper aspires to explore and construct a more objective and automated large-scale group decision-making (LSGDM) model. The concept of vacillation degree based on probabilistic double hierarchy linguistic term set is proposed to describe the ...