Association for the Advancement of Artificial Intelligence: AAAI Publications
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Spatiotemporal-Aware Neural Fields for Dynamic CT Reconstruction
No full textWe propose a dynamic Computed Tomography (CT) reconstruction framework called STNF4D (SpatioTemporal-aware Neural Fields). First, we represent the 4D scene using four orthogonal volumes and compress these volumes into more compact hash grids. Compared to the plane decomposition method, this method enhances the model's capacity while keeping the representation compact and efficient. However, in densely predicted high-resolution dynamic CT scenes, the lack of constraints and hash conflicts in the hash grid features lead to obvious dot-like artifact and blurring in the reconstructed images. To address these issues, we propose the Spatiotemporal Transformer (ST-Former) that guides the model in selecting and optimizing features by sensing the spatiotemporal information in different hash grids, significantly improving the quality of reconstructed images. We conducted experiments on medical and industrial datasets covering various motion types, sampling modes, and reconstruction resolutions. Experimental results show that our method outperforms the second-best by 5.99 dB and 4.11 dB in medical and industrial scenes, respectively
Exploring Task-Level Optimal Prompts for Visual In-Context Learning
No full textWith the development of Vision Foundation Models (VFMs) in recent years, Visual In-Context Learning (VICL) has become a better choice compared to modifying models in most scenarios. Different from retraining or fine-tuning models, VICL does not require modifications to the model's weights and architecture, and only needs a prompt with demonstrations to teach VFM how to solve tasks. Currently, significant computational cost for finding optimal prompts for every test sample hinders the deployment of VICL, as determining which demonstrations to use for constructing the prompt is very costly. In this paper, however, we find a counterintuitive phenomenon that most test samples actually achieve optimal performance under the same prompts, and searching for sample-level prompts only costs much time but results in completely identical prompts actually. Therefore, we propose task-level prompting to reduce the cost of searching for prompts during the inference stage and introduce two time-saving yet effective task-level prompt search strategies accordingly. Extensive experimental results show that our proposed method can identify near-optimal prompts and reach the best VICL performance with a minimal cost that prior work has never achieved
Motif-aware Graph Neural Networks for Networked Time Series Imputation
No full textNetworked time series are time series on a graph, one for each node, with applications in traffic and weather monitoring. Graph neural networks are natural candidates for networked time series imputation and have recently outperformed existing alternatives such as recurrent and generative models for time series imputation as they utilize a relational inductive bias for imputation. However, existing GNN-based approaches fail to capture the higher-order topological structure between sensors, which are shaped by recurring substructures in the graph, referred to as temporal motifs. In addition, it remains uncertain which motifs are the most pivotal motifs guiding the imputation task in networked time series. In this paper, we fill in this gap by proposing a graph neural network designed to leverage motif structures within the network by employing weighted motif adjacency matrices to capture higher-order neighborhood information. In particular, (1) we design a motif-wise multi-view attention module that explicitly captures various higher-order structures along with an attention mechanism that automatically assigns high weights to informative ones in order to maximize the use of higher-order information. (2) We introduce a gated fusion module by merging gated recurrent networks and graph convolutional networks to capture the spatial and temporal dependency in order to reflect the intricate impacts of temporal and spatial influence. Experimental results demonstrate that when compared to state-of-the-art models for time-series imputation tasks, our proposed model can reduce the error by around 19%
Revisiting Multimodal Emotion Recognition in Conversation from the Perspective of Graph Spectrum
No full textEfficiently capturing consistent and complementary semantic features in context is crucial for Multimodal Emotion Recognition in Conversations (MERC). However, limited by the over-smoothing or low-pass filtering characteristics of spatial graph neural networks, are insufficient to accurately capture the long-distance consistency low-frequency information and complementarity high-frequency information of the utterances. To this end, this paper revisits the task of MERC from the perspective of the graph spectrum and proposes a Graph-Spectrum-based Multimodal Consistency and Complementary collaborative learning framework GS-MCC. First, GS-MCC uses a sliding window to construct a multimodal interaction graph to model conversational relationships and designs efficient Fourier graph operators (FGO) to extract long-distance high-frequency and low-frequency information, respectively. FGO can be stacked in multiple layers, which can effectively alleviate the over-smoothing problem. Then, GS-MCC uses contrastive learning to construct self-supervised signals that reflect complementarity and consistent semantic collaboration with high and low-frequency signals, thereby improving the ability of high and low-frequency information to reflect genuine emotions. Finally, GS-MCC inputs the coordinated high and low-frequency information into the MLP network and softmax function for emotion prediction. Extensive experiments have proven the superiority of the GS-MCC architecture proposed in this paper on two benchmark data sets
DHMoE: Diffusion Generated Hierarchical Multi-Granular Expertise for Stock Prediction
No full textStock prediction stands as a pivotal research objective within the Fintech. Existing deep learning research revolves around the development and scaling of one individual neural network predictor. However, in the dynamic and noisy landscape of the stock market, reliance solely on a single predictor poses risks of limited adaptability to diverse market conditions and challenges in effectively integrating multi-source information. Besides, top-down teaching and bottom-up hierarchical decision-making paradigms are critical for robust and accurate stock prediction within successful quantitative firms. Nonetheless, there is scarcely any research that integrates this workflow into stock prediction. To this end, we propose Diffusion Generated Hierarchical Mixture-of-Experts (DHMoE) to emulate such workflow in stock prediction. Specifically, DHMoE is crafted as a three-layer tree structure, where each expert functions as a node within the tree and their parameters are generated in a top-down, recursive manner. Recognizing the leading role of the top-level root expert, we harness the robust capabilities of diffusion models for generating and introduce the Diffusion Inverted Transformer (DIT) as the root expert. The DIT is tailored to receive information from various modalities as conditional inputs and allocate parameters to bottom-level experts. These bottom-level experts are responsible for performing predictions specific to their respective input modalities. The prediction results are then synthesized in a bottom-up manner, culminating in the final prediction outcomes. Experiments on three stock trading datasets reveal that DHMoE outperforms state-of-the-art methods in terms of both cumulative and risk-adjusted returns
Modeling Latent Non-Linear Dynamical System over Time Series
No full textWe study the problem of modeling a non-linear dynamical system when given a time series by deriving equations directly from the data. Despite the fact that time series data are given as input, models for dynamics and estimation algorithms that incorporate long-term temporal dependencies are largely absent from existing studies. In this paper, we introduce a latent state to allow time-dependent modeling and formulate this problem as a dynamics estimation problem in latent states. We face multiple technical challenges, including (1) modeling latent non-linear dynamics and (2) solving circular dependencies caused by the presence of latent states. To tackle these challenging problems, we propose a new method, Latent Non-Linear equation modeling (LaNoLem), that can model a latent non-linear dynamical system and a novel alternating minimization algorithm for effectively estimating latent states and model parameters. In addition, we introduce criteria to control model complexity without human intervention. Compared with the state-of-the-art model, LaNoLem achieves competitive performance for estimating dynamics while outperforming other methods in prediction
DABL: Detecting Semantic Anomalies in Business Processes Using Large Language Models
No full textDetecting anomalies in business processes is crucial for ensuring operational success. While many existing methods rely on statistical frequency to detect anomalies, it's important to note that infrequent behavior doesn't necessarily imply undesirability. To address this challenge, detecting anomalies from a semantic viewpoint proves to be a more effective approach. However, current semantic anomaly detection methods treat a trace (i.e., process instance) as multiple event pairs, disrupting long-distance dependencies. In this paper, we introduce DABL, a novel approach for detecting semantic anomalies in business processes using large language models (LLMs). We collect 143,137 real-world process models from various domains. By generating normal traces through the playout of these process models and simulating both ordering and exclusion anomalies, we fine-tune Llama 2 using the resulting log. Through extensive experiments, we demonstrate that DABL surpasses existing state-of-the-art semantic anomaly detection methods in terms of both generalization ability and learning of given processes. Users can directly apply DABL to detect semantic anomalies in their own datasets without the need for additional training. Furthermore, DABL offers the ability to interpret anomalies' causes in natural language, providing valuable insights into the detected anomalies
HePa: Heterogeneous Graph Prompting for All-Level Classification Tasks
No full textHeterogeneous graphs, which are common in real-world downstream tasks, have recently sparked a wave of research interest. The performance of end-to-end heterogeneous graph neural networks (HGNNs) greatly relies on supervised training for specific tasks. To reduce the labeling cost, the "pretrain-finetune" paradigm has been widely adopted, but it leads to a knowledge gap between the pre-trained model and downstream tasks. In an effort to address this gap, the "pretrain-prompt" paradigm has emerged as a promising approach. This involves fine-tuning randomly initialized learnable vectors in downstream tasks. However, this approach may result in an insufficient representation of downstream task features. Existing techniques for heterogeneous graph prompting restructure the heterogeneous graph to align with the homogeneous graph prompting scheme. This can potentially introduce the same limitations as homogeneous graph prompt learning. In this paper, we propose HePa, short for Heterogeneous Graph Prompting for all-level classification tasks. It not only includes a unified prompt template-graph adapted for heterogeneous graphs but also introduces a novel pre-prompt token optimized during the pre-training phase to convey task information downstream. With these designs, HePa can complete all levels of classification tasks toward few-shot scenarios while activating in-context learning. Finally, we conducted a comprehensive experimental analysis of HePa on three benchmark datasets
Self-Explainable Graph Transformer for Link Sign Prediction
No full textSigned Graph Neural Networks (SGNNs) have been shown to be effective in analyzing complex patterns in real-world situations where positive and negative links coexist. However, SGNN models suffer from poor explainability, which limit their adoptions in critical scenarios that require understanding the rationale behind predictions. To the best of our knowledge, there is currently no research work on the explainability of the SGNN models. Our goal is to address the explainability of decision-making for the downstream task of link sign prediction specific to signed graph neural networks. Since post-hoc explanations are not derived directly from the models, they may be biased and misrepresent the true explanations. Therefore, in this paper we introduce a Self-Explainable Signed Graph transformer (SE-SGformer) framework, which can not only outputs explainable information while ensuring high prediction accuracy. Specifically, we propose a new Transformer architecture for signed graphs and theoretically demonstrate that using positional encoding based on signed random walks has greater expressive power than current SGNN methods and other positional encoding graph Transformer-based approaches. We construct a novel explainable decision process by discovering the K-nearest (farthest) positive (negative) neighbors of a node to replace the neural network-based decoder for predicting edge signs. These K positive (negative) neighbors represent crucial information about the formation of positive (negative) edges between nodes and thus can serve as important explanatory information in the decision-making process. We conducted experiments on several real-world datasets to validate the effectiveness of SE-SGformer, which outperforms the state-of-the-art methods by improving 2.2% prediction accuracy and 73.1% explainablity accuracy in the best-case scenario
Coherency Improved Explainable Recommendation via Large Language Model
No full textExplainable recommender systems are designed to elucidate the explanation behind each recommendation, enabling users to comprehend the underlying logic. Previous works perform rating prediction and explanation generation in a multi-task manner. However, these works suffer from incoherence between predicted ratings and explanations. To address the issue, we propose a novel framework that employs a large language model (LLM) to generate a rating, transforms it into a rating vector, and finally generates an explanation based on the rating vector and user-item information. Moreover, we propose utilizing publicly available LLMs and pre-trained sentiment analysis models to automatically evaluate the coherence without human annotations. Extensive experimental results on three datasets of explainable recommendation show that the proposed framework is effective, outperforming state-of-the-art baselines with improvements of 7.3% in explainability and 4.4% in text quality