11 accepted papers
Fidelity estimation is a critical yet resource-intensive step in testing quantum programs on noisy intermediate-scale quantum (NISQ) devices, where the required number of measurements is difficult to predefine due to hardware noise, device heterogeneity, and transpilation-induced circuit transformat
Recent video generation models have revealed the emergence of Chain-of-Frame (CoF) reasoning, enabling frame-by-frame visual inference. With this capability, video models have been successfully applied to various visual tasks (*e.g.*, maze solving, visual puzzles). However, their potential to enhanc…
Federated optimization under data heterogeneity presents a significant challenge, often leading to suboptimal model performance. While numerous methods aim to replicate the ideal performance of centralized training, they frequently fall short in highly heterogeneous settings. In this paper, we intro
Optimizing quantum programs is key to mitigating noise, reducing error-correction overhead, and improving performance on both near-term and fault-tolerant devices. Existing heuristic and learning-based optimizers, however, lack formal guarantees and risk semantic errors in the presence of entangleme
With the rapid advancements in quantum computing, cloud-based quantum services have gained increasing prominence. However, due to quantum noise, optimizing the deployment of quantum circuits remains an NP-hard problem with an expansive search space. Existing methods usually use heuristic algorithms
Time series analysis has evolved from traditional autoregressive models to deep learning, Transformers, and Large Language Models (LLMs). While vision models have also been explored along the way, their contributions are less recognized due to the predominance of sequence modeling. However, challeng
Hypergraphs are increasingly utilized in both unimodal and multimodal data scenarios due to their superior ability to model and extract higher-order relationships among nodes, compared to traditional graphs. However, current hypergraph models are encountering challenges related to imbalanced data, a…
Time series, typically represented as numerical sequences, can also be transformed into images and texts, offering multi-modal views (MMVs) of the same underlying signal. These MMVs can reveal complementary patterns and enable the use of powerful pre-trained large models, such as large vision models…
Sleep stage classification is crucial for detecting patients’ health conditions. Existing models, which mainly use Convolutional Neural Networks (CNN) for modelling Euclidean data and Graph Convolution Networks (GNN) for modelling non-Euclidean data, are unable to consider the heterogeneity and inte…
This letter proposes an envelop-climbing locomotion planning for vertical obstacles by concave polyhedron construction in order to achieve high terrain adaptability of polyhedron robots. Using the triangle outline of the supporting area, a Z-shape path planning along the section of terrains is propo
Neural networks are susceptible to data inference attacks such as the membership inference attack, the adversarial model inversion attack and the attribute inference attack, where the attacker could infer useful information such as the membership, the reconstruction or the sensitive attributes of a…