Disease grading is a crucial task in medical image analysis. Due to the continuous progression of diseases, i.e., the variability within the same level and the similarity between adjacent stages, accurate grading is highly challenging.Furthermore, in real-world scenarios, models trained on limited source domain datasets should also be capable of handling data from unseen target domains.Due to the cross-domain variants, the feature distribution between source and unseen target domains can be dramatically different, leading to a substantial decrease in model performance.To address these challenges in cross-domain disease grading, we propose a Severity-aware Recurrent Modeling (Samba) method in this paper.As the core objective of most staging tasks is to identify the most severe lesions, which may only occupy a small portion of the image, we propose to encode image patches in a sequential and recurrent manner.Specifically, a state space model is tailored to store and transport the severity information by hidden states.Moreover, to mitigate the impact of cross-domain variants, an Expectation-Maximization (EM) based state recalibration mechanism is designed to map the patch embeddings into a more compact space.We model the feature distributions of different lesions through the Gaussian Mixture Model (GMM) and reconstruct the intermediate features based on learnable severity bases.Extensive experiments show the proposed Samba outperforms the VMamba baseline by an average accuracy of 23.5\%, 5.6\% and 4.1\% on the cross-domain grading of fatigue fracture, breast cancer and diabetic retinopathy, respectively. Source code is available at \url{https://github.com/BiQiWHU/Samba}.

Long-sequence electroencephalogram (EEG) modeling is essential for developing generalizable EEG representation models. This need arises from the high sampling rate of EEG data and the long recording durations required to capture extended neurological patterns in brain activity. Transformer-based models have shown promise in modeling short sequences of a few seconds; however, their quadratic complexity limits scalability to longer contexts. Moreover, variability in electrode montage across available datasets, along with inter-subject differences in brain signals, pose significant challenges to developing a generalizable and robust foundation model. We propose \textit{SAMBA}, a self-supervised learning framework with a Mamba-based U-shaped encoder-decoder architecture, which effectively captures long-range temporal dependencies and spatial variability in EEG data. Leveraging the inherent ability of Mamba in processing long context sizes, we introduce: (1) \textit{Temporal Semantic Random Masking} for semantic-level sequence reconstruction, (2) a \textit{Multi-Head Differential Mamba} module to suppress redundancy and emphasize salient temporal structures, and (3) a \textit{Spatial-Adaptive Input Embedding} that learns unified embeddings in a three-dimensional Euclidean space, enabling robustness across devices. Experiments on thirteen EEG datasets across diverse tasks, electrode configurations, and sequence durations demonstrate that SAMBA consistently outperforms state-of-the-art methods while maintaining low memory consumption and inference time. We also show the learned spatial weight maps from our embedding module align closely with task-relevant neurophysiological regions, demonstrating the learnability and interpretability of SAMBA. These results highlight SAMBA's scalability and practical potential as a foundation model for real-time brain-computer interface applications.

Disease grading is a crucial task in medical image analysis.

In this paper, we consider the stochastic multi-armed bandits problem with adversarial corruptions, where the random rewards of the arms are partially modified by an adversary to fool the algorithm. We apply the policy gradient algorithm SAMBA to this setting, and show that it is computationally efficient, and achieves a state-of-the-art $O(K\log T/\Delta) + O(C/\Delta)$ regret upper bound, where $K$ is the number of arms, $C$ is the unknown corruption level, $\Delta$ is the minimum expected reward gap between the best arm and other ones, and $T$ is the time horizon. Compared with the best existing efficient algorithm (e.g., CBARBAR), whose regret upper bound is $O(K\log^2 T/\Delta) + O(C)$, we show that SAMBA reduces one $\log T$ factor in the regret bound, while maintaining the corruption-dependent term to be linear with $C$. This is indeed asymptotically optimal. We also conduct simulations to demonstrate the effectiveness of SAMBA, and the results show that SAMBA outperforms existing baselines.

Neuroscience employs diverse neuroimaging techniques, each offering distinct insights into brain activity, from electrophysiological recordings such as EEG, which have high temporal resolution, to hemodynamic modalities such as fMRI, which have increased spatial precision. However, integrating these heterogeneous data sources remains a challenge, which limits a comprehensive understanding of brain function. We present the Spatiotemporal Alignment of Multimodal Brain Activity (SAMBA) framework, which bridges the spatial and temporal resolution gaps across modalities by learning a unified latent space free of modality-specific biases. SAMBA introduces a novel attention-based wavelet decomposition for spectral filtering of electrophysiological recordings, graph attention networks to model functional connectivity between functional brain units, and recurrent layers to capture temporal autocorrelations in brain signal. We show that the training of SAMBA, aside from achieving translation, also learns a rich representation of brain information processing. We showcase this classify external stimuli driving brain activity from the representation learned in hidden layers of SAMBA, paving the way for broad downstream applications in neuroscience research and clinical contexts.

Efficiently modeling sequences with infinite context length has been a long-standing problem. Past works suffer from either the quadratic computation complexity or the limited extrapolation ability on length generalization. In this work, we present Samba, a simple hybrid architecture that layer-wise combines Mamba, a selective State Space Model (SSM), with Sliding Window Attention (SWA). Samba selectively compresses a given sequence into recurrent hidden states while still maintaining the ability to precisely recall memories with the attention mechanism. We scale Samba up to 3.8B parameters with 3.2T training tokens and show that Samba substantially outperforms the state-of-the-art models based on pure attention or SSMs on a wide range of benchmarks. When trained on 4K length sequences, Samba can be efficiently extrapolated to 256K context length with perfect memory recall and show improved token predictions up to 1M context length. As a linear-time sequence model, Samba enjoys a 3.73x higher throughput compared to Transformers with grouped-query attention when processing user prompts of 128K length, and 3.64x speedup when generating 64K tokens with unlimited streaming. A sample implementation of Samba is publicly available in https://github.com/microsoft/Samba.

Community detection is a well-studied problem with applications in domains ranging from networking to bioinformatics. Due to the rapid growth in the volume of real-world data, there is growing interest in accelerating contemporary community detection algorithms. However, the more accurate and statistically robust methods tend to be hard to parallelize. One such method is stochastic block partitioning (SBP) - a community detection algorithm that works well on graphs with complex and heterogeneous community structure. In this paper, we present a sampling-based SBP (SamBaS) for accelerating SBP on sparse graphs. We characterize how various graph parameters affect the speedup and result quality of community detection with SamBaS and quantify the trade-offs therein. To evaluate SamBas on real-world web graphs without known ground-truth communities, we introduce partition quality score (PQS), an evaluation metric that outperforms modularity in terms of correlation with F1 score. Overall, SamBaS achieves speedups of up to 10X while maintaining result quality (and even improving result quality by over 150% on certain graphs, relative to F1 score).

The multi-armed bandit is a reinforcement learning model where a learning agent repeatedly chooses an action (pull a bandit arm) and the environment responds with a stochastic outcome (reward) coming from an unknown distribution associated with the chosen arm. Bandits have a wide-range of application such as Web recommendation systems. We address the cumulative reward maximization problem in a secure federated learning setting, where multiple data owners keep their data stored locally and collaborate under the coordination of a central orchestration server. We rely on cryptographic schemes and propose Samba, a generic framework for Secure federAted Multi-armed BAndits. Each data owner has data associated to a bandit arm and the bandit algorithm has to sequentially select which data owner is solicited at each time step. We instantiate Samba for five bandit algorithms. We show that Samba returns the same cumulative reward as the nonsecure versions of bandit algorithms, while satisfying formally proven security properties. We also show that the overhead due to cryptographic primitives is linear in the size of the input, which is confirmed by our proof-of-concept implementation.

In this paper, we propose SAMBA, a novel framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics. Our method builds upon PILCO to enable active exploration using novel(semi-)metrics for out-of-sample Gaussian process evaluation optimised through a multi-objective problem that supports conditional-value-at-risk constraints. We evaluate our algorithm on a variety of safe dynamical system benchmarks involving both low and high-dimensional state representations. Our results show orders of magnitude reductions in samples and violations compared to state-of-the-art methods. Lastly, we provide intuition as to the effectiveness of the framework by a detailed analysis of our active metrics and safety constraints.