This paper introduces RaLMSpec, a framework that accelerates iterative retrieval-augmented language model (RaLM) with *speculative retrieval* and *batched verification*. RaLMSpec further introduces several important systems optimizations, including prefetching, optimal speculation stride scheduler, and asynchronous verification. The combination of these techniques allows RaLMSPec to significantly outperform existing systems. For document-level iterative RaLM serving, evaluation over three LLMs on four QA datasets shows that RaLMSpec improves over existing approaches by $1.75$-$2.39\times$, $1.04$-$1.39\times$, and $1.31$-$1.77\times$ when the retriever is an exact dense retriever, approximate dense retriever, and sparse retriever respectively. For token-level iterative RaLM (KNN-LM) serving, RaLMSpec is up to $7.59\times$ and $2.45\times$ faster than existing methods for exact dense and approximate dense retrievers, respectively.

Modeling spatio-temporal dynamics in functional brain networks is critical for underlying the functional mechanism of autism spectrum disorder (ASD). In our study, we propose an end-to-end framework called temporal graph representation learning for brain networks, which thoroughly captures spatio-temporal features in resting-state functional magnetic resonance imaging (rs-fMRI) data. Specifically, we first transform rs-fMRI time-series into temporal multi-graph using a sliding window technique. A temporal multi-graph clustering is then designed to eliminate the inconsistency of the temporal multi-graph series. Then, a graph structure aware LSTM (GSA-LSTM) is proposed to capture the spatio-temporal embedding for temporal graphs. The proposed GSA-LSTM can not only capture discriminative features for prediction but also impute the incomplete graphs for the temporal multi-graph series. Extensive experiments on autism brain imaging data exchange (ABIDE) dataset shows the effectiveness of our proposed framework. The results demonstrate that the proposed dynamic brain network embedding learning outperforms the state of-the-art brain network classification models. Furthermore, the obtained clustering results are consistent with the previous neuroimaging-derived evidence of biomarkers for autism spectrum disorder (ASD).

MRI brain segmentation plays an important part in computer-aided diagnosis, which visually reveals the changes in brain structure for doctors to quickly and accurately discover and treat diseases related to brain tissue morphology. The fuzzy C-means (FCM) algorithm performs well when the segmenting images with no noise and with intensity uniformity. However, the MRI brain images are always defective in noise and intensity nonuniformity and thus we propose a novel FCM algorithm named adaptive FCM with neighborhood membership (FCM_anm). We design a filtering process with neighborhood membership to reduce the negative influence of noise and a novel objective function which further considers the spatial membership information adaptively. Finally, to verify the performance of our method, several experiments comparing among the Experimental results demonstrate the proposed method consistently outperforms the state-of-the-art FCM-based algorithms in synthetic images, simulated and real brain MR images with effects of the noise and intensity non-uniformity.