20 accepted papers
Finding the maximum common induced subgraph (MCIS) between two graphs is a well-known NP-hard problem. While sequential MCIS algorithms have been extensively studied, parallel computing has emerged as an important direction for further performance enhancement with the advancement of computing resour
Engineering design operates through hierarchical abstraction from system specifications to component implementations, requiring visual understanding coupled with mathematical reasoning at each level. While Multi-modal Large Language Models (MLLMs) excel at natural image tasks, their ability to extra…
Gaussian Splatting (GS) enables immersive rendering, but realistic 3D object–scene composition remains challenging. Baked appearance and shadow information in GS radiance fields cause inconsistencies when combining objects and scenes. Addressing this requires relightable object reconstruction and sc…
Temporal link prediction with temporal graph neural networks (TGNNs) is increasingly used to model spatio-temporal dependencies in temporal graphs and to forecast future interactions among entities. Existing sampling-based training methods typically rely on random negative sampling and pointwise los
Significant progress has been made in spatial intelligence, spanning both spatial reconstruction and world exploration. However, the scalability and real-world fidelity of current models remain severely constrained by the scarcity of large-scale, high-quality training data. While several datasets pr
The sustainable performance improvements of integrated circuits (ICs) drive the continuous advancement of nearly all transformative technologies. Since its invention, IC performance enhancements have been dominated by scaling the semiconductor technology. Yet, as Moore's law tapers off, a crucial qu…
The massive and large-scale design of foundational semiconductor integrated circuits (ICs) is crucial to sustaining the advancement of many emerging and future technologies, such as generative AI, 5G/6G, and quantum computing. Excitingly, recent studies have shown the great capabilities of foundatio…
Causal language models acquire vast amount of knowledge from general text corpus during pretraining, but the efficiency of knowledge learning is known to be unsatisfactory, especially when learning from knowledge-dense and small-sized corpora. The deficiency can come from long-distance dependencies…
The minimum vertex cover (MVC) problem is a classic NP-hard combinatorial optimization problem with extensive real-world applications. In this paper, we propose an efficient local search algorithm, InfVC, to solve the MVC in massive graphs, which comprises three ideas. First, we introduce an inferen
The emergence of text-to-image synthesis (TIS) models has significantly influenced digital image creation by producing high-quality visuals from written descriptions. Yet these models are sensitive on textual prompts, posing a challenge for novice users who may not be familiar with TIS prompt writin
Pedestrian pre-collision pose is one of the key factors to determine the degree of pedestrian-vehicle injury in collision. Human pose estimation algorithm is an effective method to estimate pedestrian emergency pose from accident video. However, the pose estimation model trained by the existing dail…
"In this paper, we present a novel differentiable point-based rendering framework to achieve photo-realistic relighting. To make the reconstructed scene relightable, we enhance vanilla 3D Gaussians by associating extra properties, including normal vectors, BRDF parameters, and incident lighting from…
Cross-domain learning aims to transfer knowledge learned from one or more datasets to other datasets in different domains, so that less data will be required for learning in new tasks and datasets. One big challenge in cross-domain learning is to effectively synergize the knowledge learning between…
Modern deep neural network models have made remarkable success in many areas, supported by large sets of training samples. Yet the hunger for huge data has also become fatal in further expanding the use of deep models. Limited sample learning aims at learning generalized and transferable representat…
The Maximum k-Defective Clique Problem (MDCP), as a clique relaxation model, has been used to solve various problems. Because it is a hard computational task, previous works can hardly solve the MDCP for massive sparse graphs derived from real-world applications. In this work, we propose a novel bra…
Aspect-based sentiment analysis (ABSA) has drawn more and more attention because of its extensive applications. However, towards the sentence carried with more than one aspect, most existing works generate an aspect-specific sentence representation for each aspect term to predict sentiment polarity,…
Satellite multi-view stereo (MVS) imagery is particularly suited for large-scale Earth surface reconstruction. Differing from the perspective camera model (pin-hole model) that is commonly used for close-range and aerial cameras, the cubic rational polynomial camera (RPC) model is the mainstream mod…
Constraints propagation and backtracking are two basic techniques for solving constraint satisfaction problems (CSPs). During the search for a solution, the variable and value pairs that do not belong to any solution can be discarded by constraint propagation to ensure generalized arc consistency so…
Distributional uncertainty exists broadly in many real-world applications, one of which in the form of domain discrepancy. Yet in the existing literature, the mathematical definition of it is missing. In this paper, we propose to formulate the distributional uncertainty both between the source(s) an…
Piezoelectric ceramics(PZT)actuator has been widely used in flexure-guided nanopositioning stage because of their high resolution. However, it is quite hard to achieve high-rate precision positioning control because of the complex hysteresis nonlinearity effect of PZT actuator. Thus, an online RELM…