Neural coupling is a fundamental mechanism in neuroscience that facilitates the emergence of cognitive functions through dynamic interactions and synchronization among distributed brain regions. Inspired by this principle, we pose the question: Might the biological mechanism of neural oscillatory synchronization inspire the feature representation learning for neuroscience? By addressing this question through the Kuramoto model, renowned for simulating oscillatory dynamics, we present a novel physics-informed deep model, `SyncBrain`, it models brain regions as interacting oscillatory units and simulates their temporal dynamics and synchronization patterns to distinguish cognitive states. Furthermore, inspired by the brain's inherent ability to dynamically attend to critical temporal information, we incorporate an adaptive control module that introduces an attention-like mechanism to guide information flow. We evaluate our model on multiple functional neuroimaging datasets, it demonstrates promising performance and enhanced interpretability in both cognitive state decoding and early disease diagnosis, outperforming existing computational methods. These results demonstrate the effectiveness of neural oscillatory mechanisms in shaping robust and interpretable machine learning models for neuroscience applications.

In both neuroscience and artificial intelligence (AI), it is well-established that neural “coupling” gives rise to dynamically distributed systems. These systems exhibit self-organized spatiotemporal patterns of synchronized neural oscillations, enabling the representation of abstract concepts. By capitalizing on the unprecedented amount of human neuroimaging data, we propose that advancing the theoretical understanding of rhythmic coordination in neural circuits can offer powerful design principles for the next generation of machine learning models with improved efficiency and robustness. To this end, we introduce a physics-informed deep learning framework for \underline{B}rain \underline{R}hythm \underline{I}dentification by \underline{K}uramoto and \underline{C}ontrol (coined \textit{BRICK}) to characterize the synchronization of neural oscillations that shapes the dynamics of evolving cognitive states. Recognizing that brain networks are structurally connected yet behaviorally dynamic, we further conceptualize rhythmic neural activity as an artificial dynamical system of coupled oscillators, offering a shared mechanistic bridge to brain-inspired machine intelligence. By treating each node as an oscillator interacting with its neighbors, this approach moves beyond the conventional paradigm of graph heat diffusion and establishes a new regime of representation compression through oscillatory synchronization. Empirical evaluations demonstrate that this synchronization-driven mechanism not only mitigates over-smoothing in deep GNNs but also enhances the model’s capacity for reasoning and solving complex graph-based problems.

A fundamental challenge in cognitive neuroscience is understanding how cognition emerges from the interplay between structural connectivity (SC) and dynamic functional connectivity (FC) in the brain. Network neuroscience has emerged as a powerful framework to understand brain function through a holistic perspective on structure-function relationships. In this context, current machine learning approaches typically seek to establish direct mappings between structural connectivity (SC) and functional connectivity (FC) associated with specific cognitive states. However, these state-independent methods often yield inconsistent results due to overlapping brain networks across cognitive states. To address this limitation, we conceptualize to uncover the dendritic coupling mechanism between one static SC and multiple FCs by solving a flow problem that bridges the distribution of SC to a mixed distribution of FCs, conditioned on various cognitive states, along a Riemannian manifold of symmetric positive-definite (SPD) manifold. We further prove the equivalence between flow matching on the SPD manifold and on the computationally efficient Cholesky manifold. Since a spare of functional connections is shared across cognitive states, we introduce the notion of consensus control to promote the shared kinetic structures between multiple FC-to-SC pathways via synchronized coordination, yielding a biologically meaningful underpinning on SC-FC coupling mechanism. Together, we present BrainFlow, a reversible generative model that achieves state-of-the-art performance on not only synthetic data but also large-scale neuroimaging datasets from UK Biobank and Human Connectome Project.

Radar composite reflectivity is a crucial component of Earth observation data, playing a significant role in applications such as weather forecasting and climate disaster tracking. Due to the deployment challenges and limited coverage of meteorological radars, it is impossible to collect corresponding radar reflectivity in areas such as mountains and oceans. In such cases, using deep learning methods to reconstruct radar reflectivity from meteorological satellite data becomes an effective solution. However, Earth observation data is complex and exhibits strong long-range dependencies. Such data characteristics require the ability to model long-distance dependencies, making the Transformer more suitable for this specific data. With the ultimate goal of producing high reconstruction quality, we propose in this paper a novel architecture, designated by SU3plus, based on the Swin Transformer with a multi-scale feature fusion mechanism, while leveraging multiple channels of satellite data. Moreover, we define an appropriate loss function by combining the weighted versions of standard metrics, to take into account the data distribution imbalance and improve the reconstruction performance. Extensive experiments, carried out on the SEVIR storm dataset, confirm the effectiveness of the proposed approach compared to several state-of-the-art models.

One of the fundamental scientific problems in neuroscience is to have a good understanding of how cognition and behavior emerge from brain function. Since the neuroscience concept of cognitive control parallels the notion of system control in engineering, many computational models formulate the dynamics neural process into a dynamical system, where the hidden states of the complex neural system are modulated by energetic simulations. However, the human brain is a quintessential complex biological system. Current computation models either use neural networks to approximate the underlying dynamics, which makes it difficult to fully understand the system mechanics, or compromise to simplified linear models with very limited power to characterize non-linear and self-organized dynamics along with complex neural activities. To address this challenge, we devise an end-to-end deep model to identify the underlying brain dynamics based on Koopman operator theory, which allows us to model a complex non-linear system in an infinite-dimensional linear space. In the context of reverse engineering, we further propose a biology-inspired control module that adjusts the input (neural activity data) based on feedback to align brain dynamics with the underlying cognitive task. We have applied our deep model to predict cognitive states from a large scale of existing neuroimaging data by identifying the latent dynamic system of functional fluctuations. Promising results demonstrate the potential of establishing a system-level understanding of the intricate relationship between brain function and cognition through the landscape of explainable deep models.

The transaction efficiency of blockchains has been a painful point haunting researchers all the time. The metrics of Transaction per Second (TPS) present a low value, especially as the number of network nodes increases. In order to improve the blockchain's TPS, numerous articles explored the solutions from multiple aspects, such as: using a new block system based on the Directed Acyclic Graph (DAG), adjusting the block structure, optimizing the consensus algorithm or adopting a sharding protocol. The sharding protocols play an extremely important role among these schemes, while the existing ones suffer from secure risks due to their static sharding. In this paper, we proposed a dynamic sharding protocol for consortium blockchains, employing random values to randomly partition the network. Our protocol aims for improving the throughput of the blockchain system as well as ensuring its security. The OMNET++ simulation results showed that, in the case of a large-scale consortium blockchain network of 400 nodes which is divided into 16 shards, the performance of our scheme is able to attain up to 50 times promotion, compared to the system without sharding.

News plays a significant role in shaping people's beliefs and opinions. Fake news has always been a problem, which wasn't exposed to the mass public until the past election cycle for the 45th President of the United States. While quite a few detection methods have been proposed to combat fake news since 2015, they focus mainly on linguistic aspects of an article without any fact checking. In this paper, we argue that these models have the potential to misclassify fact-tampering fake news as well as under-written real news. Through experiments on Fakebox, a state-of-the-art fake news detector, we show that fact tampering attacks can be effective. To address these weaknesses, we argue that fact checking should be adopted in conjunction with linguistic characteristics analysis, so as to truly separate fake news from real news. A crowdsourced knowledge graph is proposed as a straw man solution to collecting timely facts about news events.

Recent programming frameworks enable small computing systems to achieve high performance on large-scale graph analytics by supporting out-of-core graph analytics (i.e., processing graphs that exceed main memory capacity). These frameworks make out-of-core programming easy by automating the tedious process of scheduling data transfer between memory and disk. This paper presents two innovations that improve the performance of software frameworks for out-of-core graph analytics. The first is degree-ordered storage, a new storage format that dramatically lowers book-keeping overhead when graphs are larger than memory. The second innovation replaces existing static messages with novel ordered dynamic messages which update their destination immediately, reducing both the memory required for intermediate storage and IO pressure. We implement these innovations in a framework called GraphZ-which we release as open source-and we compare its performance to two state-of-the-art out-of-core graph frameworks. For graphs that exceed memory size, GraphZ's harmonic mean performance improvements are 1.8-8.3× over existing solutions.