FBI Director Kash Patel says the Trump administration is ramping up efforts to combat drone threats from criminals and foreign adversaries through new training center.

Huge plumes of ash and smoke were filmed erupting from Mount Etna, Sicily on 27 December as skiers used the slopes below. Scientists at the Istituto Nazionale di Geofisica e Vulcanologia said volcanic activity at the site, which frequently erupts, had intensified, with craters continuously emitting ash. In response, scientists issued a red Volcano Observatory Notice for Aviation, its highest level, though authorities said flights would continue to operate normally at a nearby airport unless ashfall increased. The annual food fight festival ''Els Enfarinats'' has left the Spanish town of Ibi covered in flour and egg shells. The French model and actress has died at the age of 91.

What can be learned about causality and experimentation from passive data?

Our core idea is to reformulate Mamba's

Due to its low storage cost and fast search speed, cross-modal retrieval based on hashing has attracted widespread attention and is widely used in real-world applications of social media search.

Text-to-image diffusion models have made significant progress in generating naturalistic images from textual inputs, and demonstrate the capacity to learn and represent complex visual-semantic relationships. While these diffusion models have achieved remarkable success, the underlying mechanisms driving their performance are not yet fully accounted for, with many unanswered questions surrounding what they learn, how they represent visual-semantic relationships, and why they sometimes fail to generalize.

Estimating the probabilities of connections between vertices in a random network using an observed adjacency matrix is an important task for network data analysis. Many existing estimation methods are based on certain assumptions on network structure, which limit their applicability in practice. Without making strong assumptions, we develop an iterative connecting probability estimation method based on neighborhood averaging. Starting at a random initial point or an existing estimate, our method iteratively updates the pairwise vertex distances, the sets of similar vertices, and connecting probabilities to improve the precision of the estimate. We propose a two-stage neighborhood selection procedure to achieve the trade-off between smoothness of the estimate and the ability to discover local structure. The tuning parameters can be selected by cross-validation. We establish desirable theoretical properties for our method, and further justify its superior performance by comparing with existing methods in simulation and real data analysis.