11 accepted papers
Spatiotemporal forecasting is a fundamental task in areas such as traffic flow prediction, environmental sensing, and urban planning. Recent advances have shown that decomposing temporal signals into multiple frequencies and modeling them jointly with spatial structures can significantly enhance for
End-to-end prediction of high-order crystal tensor properties from atomic structures remains challenging: while spherical-harmonic equivariant models are expressive, their Clebsch-Gordan tensor products incur substantial compute and memory costs for higher-order targets. We propose the Cartesian Env…
Pretrained equivariant graph neural networks based on spherical harmonics offer efficient and accurate alternatives to computationally expensive ab-initio methods, yet adapting them to new tasks and chemical environments still requires fine-tuning. Conventional parameter-efficient fine-tuning (PEFT)
Real-world systems increasingly require coherent reasoning and generation over diverse data modalities simultaneously. Current generative frameworks rely on complex, multi-stage training, resulting in low efficiency due to iterative inference and high computational cost. They also struggle with unif
Citation Sentiment Analysis (CSA) plays a crucial role in understanding academic influence and knowledge diffusion. While pre-trained language models (PLMs) and large language models (LLMs) showed remarkable success in general sentiment analysis, they encounter specialized challenges in CSA due to t…
Digital watermarking has long been used to protect digital images from abuse. However, applying digital watermarking to semantic communication remains a challenge. This work introduces a secure coding method that combines semantic coding and digital watermarking techniques. The proposed method selec…
Dynamic adaptive video streaming over HTTP (DASH) plays a key role in video transmission over the Internet. The conventional DASH adaptation approaches concentrate on optimizing the overall quality of experience (QoE) for all client sides, neglecting the QoE diversity of different users. In this pap…
Recent years have witnessed the growing interest in visual quality assessment (VQA) for 360deg video. Unfortunately, the existing VQA approaches do not consider the facts that: 1) Observers only see viewports of 360deg video, rather than patches or whole 360deg frames. 2) Within the viewport, only s…
In this paper, we present the boundary objectness network (BON), an effective convolutional neural network (CNN) for object detection. Its core contribution is to accurately localize the objects. Generally, the CNN-based localizers predict four bounding box coordinates by learning a regression funct…
Nonparametric Bayesian models have been implemented in dictionary learning. However, for signal samples from multiple subspaces, existing methods only learn one uniform dictionary and thus are not optimal for representing the subspace structures. To address this issue, we first utilize a combination…
Hyper-spectral image fusion has been a hot topic in medical imaging and remote sensing. This paper proposes a Bayesian fusion model which combines the panchromatic (PAN) image and the low spatial resolution hyper-spectral (HS) image under the same framework. Sparsity constraint is introduced as doub…