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Hybrid System Planning Using a Mixed-Integer ADMM Heuristic and Hybrid Zonotopes 基于混合整数ADMM启发式和混合分区的混合系统规划

IF 7.8 1区计算机科学 Q1 ROBOTICS

IEEE Transactions on Robotics

Pub Date : 2026-10-16 DOI: 10.1109/tro.2026.3744513

Joshua A. Robbins, Andrew F. Thompson, Jonah J. Glunt, Herschel C. Pangborn

引用次数: 0

The Quasi-Systematic Nature of Splitter Cells. 分裂细胞的准系统性质。

IF 2.1 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Computation

Pub Date : 2026-10-16 DOI: 10.1162/NECO.a.1582

Naomi Chaix-Eichel, Snigdha Dagar, Fréderic Alexandre, Thomas Boraud, Nicolas P Rougier

During the past decades, hippocampal formation has undergone extensive studies, leading researchers to identify a vast collection of cells with functional properties. Several investigations, supported by carefully crafted models, have examined the origin of such cells. The most recent models hypothesize that temporal sequences underlie the observed spatial properties. We aim at investigating whether a random recurrent structure is sufficient to allow such latent sequence to appear. To do so, we simulated an agent with egocentric sensory inputs that must navigate and alternate choices at intersections. We were subsequently able to identify several splitter cells inside the model. Remarkably, when we systematically lesioned the identified splitter cells, the model's behavioral performance remained intact: in the vast majority of cases, new splitter cells reemerged through network reorganization, while in the remaining cases, the task was solved without any detectable splitter cells, demonstrating that splitter cells are not necessary to the task resolution. Position, orientation, and decision representations could also be successfully decoded from the reservoir activity, even after repeated lesioning. Subspace alignment analysis further revealed that this reorganization preserves the task-relevant population geometry while redistributing activity within the null subspace, with the trajectory-encoding dimension rotating in neuron space across lesions. Together, these findings demonstrate that splitter cell activity is primarily task driven and does not derive from a specific architecture or learning rule: splitter cells emerge generically across random recurrent networks that successfully solve the task, across a broad and robust range of dynamical parameters and are not necessary for task performance. Our results therefore challenge the notion of functional necessity for specific neural populations.

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引用次数: 0

DMG: Dual-Stream multiple instance learning network for hunting vulnerable smart contracts via adaptive granularity fusion DMG：双流多实例学习网络，通过自适应粒度融合寻找易受攻击的智能合约

IF 3.9 2区计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Automated Software Engineering

Pub Date : 2026-10-16 DOI: 10.1007/s10515-026-00687-w

Na Wang, Kun Zhang, Moroti Sonde, Victor S. Sheng

With the increasing complexity of smart contracts and the increasing number of vulnerabilities in real-world scenarios, ensuring smart contract security has become a critical challenge. Despite various detection methods being proposed, existing approaches primarily trained on synthetic (toy) contracts with oversimplified semantics exhibit limited generalization to deployment environments. Furthermore, their reliance on predefined patterns or single-perspective representations with fixed granularity, such as token sequences or control-flow graphs, limits their ability to capture the hierarchical structures and cross-functional dependencies of complex contracts. To this end, we propose a novel Dual-stream Multi-instance and Granularity Fusion Network (DMG). It is designed to capture the structural complexity of real-world contracts while maintaining effectiveness in simpler contracts, thereby ensuring both adaptability and practical relevance. Specifically, DMG learns representations at both the block-level and the function-level from bytecode with relatively structured dispatch organization. Then, it uses Bi-LSTM and a Multi-Head Attention mechanism to model block-level sequential dependencies and capture coarse-grained contextual relationships. Finally, it introduces a granularity-aware fusion mechanism to adaptively integrate both views, releasing the assumption of fixed granularity and enabling the model to learn the most informative representation under weak supervision. By integrating hierarchical granularity modeling with instance-level learning, DMG shifts from fixed to adaptive granularity, enabling structured and semantically aligned vulnerability analysis. Extensive experiments on toy and real-world datasets demonstrate strong performance, and compared with state-of-the-art methods, DMG delivers a balanced solution that bridges the gap between experimental settings and real-world applicability.

随着智能合约越来越复杂，现实场景中的漏洞越来越多，确保智能合约的安全已成为一项关键挑战。尽管提出了各种各样的检测方法，但是主要训练在语义过于简化的合成（玩具）契约上的现有方法对部署环境的泛化程度有限。此外，它们依赖于预定义的模式或具有固定粒度的单视角表示，例如令牌序列或控制流图，这限制了它们捕捉复杂契约的层次结构和跨功能依赖关系的能力。为此，我们提出了一种新的双流多实例和粒度融合网络（DMG）。它旨在捕捉现实世界契约的结构复杂性，同时保持简单契约的有效性，从而确保适应性和实际相关性。具体来说，DMG从具有相对结构化调度组织的字节码中学习块级和函数级的表示。然后，它使用Bi-LSTM和Multi-Head Attention机制来建模块级顺序依赖并捕获粗粒度的上下文关系。最后，引入粒度感知融合机制自适应整合两种视图，摆脱了固定粒度的假设，使模型能够在弱监督下学习到最具信息量的表示。通过将分层粒度建模与实例级学习集成在一起，DMG从固定粒度转变为自适应粒度，支持结构化和语义一致的漏洞分析。在玩具和现实世界数据集上进行的广泛实验显示出强大的性能，与最先进的方法相比，DMG提供了一个平衡的解决方案，弥合了实验设置和现实世界适用性之间的差距。

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引用次数: 0

A Geometry-Aware Bio-Inspired Sparse Reservoir With Online Learning. 具有在线学习功能的几何感知生物稀疏存储库。

IF 2.1 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Computation

Pub Date : 2026-10-16 DOI: 10.1162/NECO.a.1578

Alfio Borzì

This article presents a cortically motivated framework for geometry-aware sparse reservoir computing with efficient online readout adaptation. The architecture is based on FitzHugh-Nagumo units coupled through sparse connectivity motifs informed by cortical statistics (reciprocity, triadic closure, and Dale's law), resulting in a structured and biologically plausible recurrent matrix. The readout weights are trained by means of an anchor-proximal ridge objective applied to streaming time windows, ensuring temporally stable online learning with a low memory footprint. It presents the results of numerical experiments on a temporal decision-making benchmark and an image-based long-range segmentation task. The proposed sparse reservoir, combined with the windowed proximal readout, is shown to achieve competitive accuracy while substantially reducing recurrent and state-storage requirements. A favorable trade-off of biological plausibility, predictive performance, and computational efficiency is thereby demonstrated.