Graph neural architecture search (NAS) has emerged as a promising approach for autonomously designing graph neural network architectures by leveraging correlations between graphs and architectures. However, existing methods merely rely on correlations, which may be spurious and vary across distributions. This reliance, without considering causal graph-architecture relationships, limits their ability to generalize under distribution shifts that are ubiquitous in real-world graph scenarios. In this paper, we propose to handle the distribution shifts in NAS process by exploiting the causal graph-architecture relationship to search for optimal architectures that can generalize under distribution shifts. Key challenges remain unexplored: discovering causal graph-architecture relationships with stable cross-distribution predictive abilities, and leveraging them to handle distribution shifts. To address these challenges, we propose a novel approach, Causal-aware Graph Neural Architecture Search (CARNAS), which is capable of capturing causal graph-architecture relationship during NAS process and discovering optimal graph architecture under distribution shifts. We propose Disentangled Causal Subgraph Identification to extract causal subgraphs with stable predictive power across distributions, followed by Graph Embedding Intervention to intervene on these subgraphs in latent space by preserving essential features while filtering out non-causal elements, and Invariant Architecture Customization to enhance their causal invariance for optimizing graph architectures. Extensive experiments on synthetic and real-world datasets show that CARNAS enhances out-of-distribution generalization by uncovering causal graph-architecture relationships during NAS.

The existing deep hashing methods for image retrieval typically modeling hash-coding layer in real number space. However, these methods frequently overlook the intrinsic information loss that occurs during the hash-coding process, as the hash layer performs two tasks simultaneously: spatial transformation and dimensionality reduction. Especially in multi-label image retrieval, the exponential increase in the number of combinations with the labels further amplifies the information loss in Hamming space. Consequently, the efficiency of the hash-coding is unsatisfactory. To mitigate this limitation, we introduce a novel approach termed WalshHash, which is grounded in the principles of Walsh transformation in signal processing. Unlike conventional techniques, WalshHash formulates the hash-coding layer as a filtering process based on Kolmogorov-Arnold Networks (KANs) in the Walsh domain accompanied by constraint loss functions on multiple domains. It ensures the dimensionality reduction in the Walsh domain can be effectively projected onto the real number domain with minimal information loss, because the Walsh space encapsulates the critical information components. As a result, WalshHash demonstrates superior performance in multi-label image retrieval compared to State-of-the-Art (SOTA) methods.

In the field of Artificial Intelligence for Information Technology Operations, causal discovery is pivotal for operation and maintenance of systems, facilitating downstream industrial tasks such as root cause analysis. Temporal causal discovery, as an emerging method, aims to identify temporal causal relations between variables directly from observations by utilizing interventional data. However, existing methods mainly focus on synthetic datasets with heavy reliance on interventional targets and ignore the textual information hidden in real-world systems, failing to conduct causal discovery for real industrial scenarios. To tackle this problem, in this paper we investigate temporal causal discovery in industrial scenarios, which faces two critical challenges: how to discover causal relations without the interventional targets that are costly to obtain in practice, and how to discover causal relations via leveraging the textual information in systems which can be complex yet abundant in industrial contexts. To address these challenges, we propose the RealTCD framework, which is able to leverage domain knowledge to discover temporal causal relations without interventional targets. We first develop a score-based temporal causal discovery method capable of discovering causal relations without relying on interventional targets through strategic masking and regularization. Then, by employing Large Language Models (LLMs) to handle texts and integrate domain knowledge, we introduce LLM-guided meta-initialization to extract the meta-knowledge from textual information hidden in systems to boost the quality of discovery. We conduct extensive experiments on both simulation datasets and our real-world application scenario to show the superiority of our proposed RealTCD over existing baselines in temporal causal discovery.

Humans show a remarkable ability in solving the cocktail party problem. Decoding auditory attention from the brain signals is a major step toward the development of bionic ears emulating human capabilities. Electroencephalography (EEG)-based auditory attention detection (AAD) has attracted considerable interest recently. Despite much progress, the performance of traditional AAD decoders remains to be improved, especially in low-latency settings. State-of-the-art AAD decoders based on deep neural networks generally lack the intrinsic temporal coding ability in biological networks. In this study, we first propose a bio-inspired spiking attentional neural network, denoted as BSAnet, for decoding auditory attention. BSAnet is capable of exploiting the temporal dynamics of EEG signals using biologically plausible neurons and an attentional mechanism. Experiments on two publicly available datasets confirm the superior performance of BSAnet over other state-of-the-art systems across various evaluation conditions. Moreover, BSAnet imitates realistic brain-like information processing, through which we show the advantage of brain-inspired computational models.

Causal learning from observational data has garnered attention as controlled experiments can be costly. To enhance identifiability, incorporating intervention data has become a mainstream approach. However, these methods have yet to be explored in the context of time series data, despite their success in static data. To address this research gap, this paper presents a novel contribution. Firstly, a temporal interventional dataset with causal labels is introduced, derived from a data center IT room of a cloud service company. Secondly, this paper introduces TECDI, a novel approach for temporal causal discovery. TECDI leverages the smooth, algebraic characterization of acyclicity in causal graphs to efficiently uncover causal relationships. Experimental results on simulated and proposed real-world datasets validate the effectiveness of TECDI in accurately uncovering temporal causal relationships. The introduction of the temporal interventional dataset and the superior performance of TECDI contribute to advancing research in temporal causal discovery. Our datasets and codes have released at~\hrefhttps://github.com/lpwpower/TECDI https://github.com/lpwpower/TECDI.

Existing image retrieval deep hashing methods employ full connection as the hash encoding layer and output each bit of hash code in parallel. They regards the hash code as the information of the image, ignoring the relevance between each bit and the redundancy of the whole hash code, resulting in limited performance. Hence, based on the concept of code verification in digital communication (called code verification), the code verification hashing (CVH) is proposed. Different from the exiting methods, CVH constructs hash encoding layer to output the hash code serially, and each output of partial hash code relies on the output of the previous one, so as to make full use of the relevance and redundancy of hash code to generate a more discriminative one. Moreover, CVH also progresses the hash center loss to constraint by all hash centers and makes further improvement. As experiment results, CVH demonstrates excellent retrieval ability.

X-ray security check is one of the most effective security measures widely used in airports, high-speed trains, subways, and other important places. Due to the difference in imaging mechanism between X-ray images and visible images, X-ray contraband detection suffers from problems of large intra-class differences and small inter-class differences. The Softmax loss does not encourage compactness within class and separability between classes explicitly, resulting in insensitivity to target. We address this problem by proposing an attention mechanism based ACD-Net, which can quickly focus on the obscured objects in luggage. To optimize inter-class interval, we evaluate impacts of several cosine-based loss function on X-ray contraband detection performance, including Cosine Embedding Loss, L-Softmax Loss, CosFace and ArcFace. Then we propose a cosine loss function DA-Loss focusing on intra-class compactness and inter-class difference at the same time. Extensive experiments show the superiority of our proposed ACD-Net model with DALoss classification loss function, which has better separability of large intra-class differences and small inter-class differences, and outperform the state-of-the-arts.

Decoding auditory attention in a cocktail party from neural activities is crucial in the brain-computer interfaces (BCIs). Given that the speech-electroencephalography (EEG) relationships are informative about attentional focus, we propose a novel framework called the biologically inspired attention network (BIAnet) to capture the interactions between EEG and speech. With the neural attention mechanism, the BIAnet can model how each EEG frequency band is related to the subband envelopes of speech by dynamically assigning weights to individual frequency bands at run-time. Results show that the proposed BIAnet outperforms state-of-the-art AAD methods on two publicly available datasets. We also analyze how the BIAnet works and the frequency-specific interactions between EEG and speech signals through data visualization. Overall, the proposed BIAnet provides an accurate, low-latency, and interpretable AAD approach, which has the potential to be extended to general problems in BCIs.

Humans have the ability to focus on one of the sound sources in a noisy scene, which is critical for everyday communication. Auditory attention detection (AAD) seeks to detect selective attention from one’s brain signals. For AAD to be useful in brain–computer interface applications, new approaches with low computational cost, high classification performance, and low latency are required to be developed. In this study, we proposed a novel neural-inspired architecture to mimic the neural computation and coding strategy in the brain for electroencephalography-based AAD. We validated our model through data visualization, and conducted experiments on two publicly available databases. For both KUL and DTU databases, it outperforms both linear and convolutional neural network (CNN) models with consistent improvements from 1 s to 5 s decision windows in terms of detection accuracy. Although the accuracy of the proposed neural-inspired model is inferior to the state-of-the-art spatio-spectral feature (SSF)-CNN model, the computational cost of our model is less than 1% of SSF-CNN’s. Moreover, the neural-inspired decoder is more hardware friendly and energy-efficient due to its biological computing scheme. Overall, the proposed neural-inspired architecture realizes a fast, accurate, and low energy expenditure AAD, which is a big step forward towards practical neuro-steered hearing aids.