We introduce the Brain Language Model (BrainLM), a foundation model for brain activity dynamics trained on 6,700 hours of fMRI recordings. Utilizing self-supervised masked-prediction training, BrainLM demonstrates proficiency in both fine-tuning and zero-shot inference tasks. Fine-tuning allows for the accurate prediction of clinical variables like age, anxiety, and PTSD as well as forecasting of future brain states. Critically, the model generalizes well to entirely new external cohorts not seen during training. In zero-shot inference mode, BrainLM can identify intrinsic functional networks directly from raw fMRI data without any network-based supervision during training. The model also generates interpretable latent representations that reveal relationships between brain activity patterns and cognitive states. Overall, BrainLM offers a versatile and interpretable framework for elucidating the complex spatiotemporal dynamics of human brain activity. It serves as a powerful "lens" through which massive repositories of fMRI data can be analyzed in new ways, enabling more effective interpretation and utilization at scale. The work demonstrates the potential of foundation models to advance computational neuroscience research.

Modeling continuous dynamical systems from discretely sampled observations is a fundamental problem in data science. Often, such dynamics are the result of non-local processes that present an integral over time. As such, these systems are modeled with Integro-Differential Equations (IDEs); generalizations of differential equations that comprise both an integral and a differential component. For example, brain dynamics are not accurately modeled by differential equations since their behavior is non-Markovian, i.e. dynamics are in part dictated by history. Here, we introduce the Neural IDE (NIDE), a novel deep learning framework based on the theory of IDEs where integral operators are learned using neural networks. We test NIDE on several toy and brain activity datasets and demonstrate that NIDE outperforms other models. These tasks include time extrapolation as well as predicting dynamics from unseen initial conditions, which we test on whole-cortex activity recordings in freely behaving mice. Further, we show that NIDE can decompose dynamics into their Markovian and non-Markovian constituents, via the learned integral operator, which we test on fMRI brain activity recordings of people on ketamine. Finally, the integrand of the integral operator provides a latent space that gives insight into the underlying dynamics, which we demonstrate on wide-field brain imaging recordings. Altogether, NIDE is a novel approach that enables modeling of complex non-local dynamics with neural networks.

We address a topological vulnerability analysis of the Lebanese power grid subject to random and cascading failures. Using an Apache Spark implementation that maps the topology of the grid to a complex network, we begin by developing a local structural understanding of the Lebanese power grid that reveals a certain level of decentralization via numerous connected components. Our Apache Spark implementation simulates the random and cascading sequences of events by which energy centers in Lebanon can be exposed and are at risk. The implementation is based on the bulk-synchronous parallel model and maintains optimal work, linear communication time, and a constant number of synchronization barriers. We complement this paper with a spatial understanding of the exposed hotspots. Our results reveal that failures in the power grid are spatially long-range correlated and correlations decay with distance. In a couple of attack scenarios, our Spark implementation achieves significant speedup on 16 cores for a graph with about 9 × 105nodes. Scalability toward 32 nodes improves when experimenting with replicas of the power grid graph which are double and quadruple the original size. This renders this paper suitable to larger networks at many vital levels beyond the power grid.

This paper presents tomographic analysis using L-band NASA/JPL UAVSAR from AfriSAR data conducted over the Gabon Lope Park on February 2016. Prior to tomographic imaging, a phase residual correction methodology based on Sum Kronecker Product have been implemented. The estimated vertical structure of the forest extracted from the correct tomographic data is validated with small footprint light detection collected during the AfriSAR campaign on July 2015. The result demonstrates that L-band tomographic imaging can now be carried out even in the dense tropical forest.

The application of remote sensing (RS) for providing basic data for environmental decision-making is growing rapidly. Whilst such application is becoming more routine within national boundaries, there is greater difficulty in bilateral or multi-national policy formulation and management intervention. This paper gives an example on the el Kabir River Basin shared between Lebanon and Syria, focusing on inter-related environmental issues. The natural and anthropogenic fabric of the watershed is provided by RS and GIS which contribute to improve water quality by securing information for managing land effectively. The fabric includes both natural and anthropogenic aspects such as geology, drainage, soils, land cover/land use, roads, utilities, agricultural practice, etc. Evaluation of water quality is derived from four seasonal synoptic water sampling episodes and from a single sediment sampling programme taken during summer flow. Sediment is being analyzed for trace metals, whereas water is being analyzed for nutrients and bacteria, and characterized by temperature, TDS, pH and dissolved oxygen. It is clear that management and conservation should relate to land use practices and policies to assure sustainability. The impact on the basin communities implies that these communities be a part of both evaluation and decision making. This explains the approach which integrates institutional, geomatics and public participation aspects. It will ensure there is an environmental "conservation competency" leading, within a bilateral policy framework, to the communities themselves being self-regulating. This will aid in the provision of sustainability within water sharing needs to give a high quality of living to the inhabitants of the watershed.