Figure 4: Impact of the proportion of injected noise for learning Paragraph Em-beddings on XSum dataset. (Figure 4). The results of the ablation study are presented in Table 5. Embedder in providing clean and denoised reconstructions. In general, it has been observed that generations progress in a coarse-to-fine manner. The early time step, which is close to 1, tends to be less fluent and generic. This was the nicest stay we have ever had. Turtle Bay was a great resort. This was the nicest stay we have ever had.

Fears over a drug cartel drone over Texas sparked a recent airspace shutdown in El Paso and New Mexico, highlighting just how tricky it can be to deploy anti-drone weapons near cities. A shocking but ultimately brief airspace closure over El Paso, Texas, and parts of New Mexico last week is stoking unease among pilots and the broader public about the status of United States anti-drone defenses. As low-cost UAV equipment proliferates around the world, analysts have repeatedly warned that destructive attacks perpetrated using drones are inevitable . It is challenging to develop nimble and safe countermeasures, though, given that things like jamming or attempting to shoot down a drone are difficult--or even impossible--to carry out safely in populated areas, much less densely populated cities. In the case of the El Paso incident, the Federal Aviation Administration originally set the airspace closure to last 10 days, but ultimately lifted it after eight hours.

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FAA closes El Paso airspace citing cartel drone incursions near military facilities, revealing over 60,000 cartel drone flights along the border in 2024.

Why was El Paso airspace shut down? A new United States military laser-based anti-drone system led authorities to halt air traffic in and out of El Paso, Texas, after aviation officials raised serious concerns about risks to commercial aircraft. The Federal Aviation Administration (FAA) initially announced a 10-day airspace closure on Wednesday but removed the restriction less than eight hours later, a decision reports said stemmed from miscommunication between the Pentagon and aviation regulators. The FAA and the military had planned to discuss the issue at a February 20 meeting, but the army moved ahead without final FAA approval, prompting the agency to halt flights in El Paso, sources said. What happened when El Paso's airspace was shut down?

What is the US critical minerals stockpile? Has the Trump administration overplayed its spin? United States aviation authorities have announced that the airspace over El Paso, Texas, has been reopened after initially closing the airspace due to an alleged drone incursion from a Mexican cartel. Wednesday's announcement walked back an earlier statement from the Federal Aviation Administration (FAA), abruptly pausing air traffic over the southern border city for 10 days. By late morning, though, the FAA announced that flights would resume in and out of the area as normal, prompting questions about the legitimacy of the drone claims. "The temporary closure of airspace over El Paso has been lifted.

The U.S. military shot down what was later identified as a party balloon near El Paso, Texas, after drone concerns triggered a temporary FAA airspace restriction.

We derive an estimator of the spectral density of a functional time series that is the output of a multilayer perceptron neural network. The estimator is motivated by difficulties with the computation of existing spectral density estimators for time series of functions defined on very large grids that arise, for example, in climate compute models and medical scans. Existing estimators use autocovariance kernels represented as large $G \times G$ matrices, where $G$ is the number of grid points on which the functions are evaluated. In many recent applications, functions are defined on 2D and 3D domains, and $G$ can be of the order $G \sim 10^5$, making the evaluation of the autocovariance kernels computationally intensive or even impossible. We use the theory of spectral functional principal components to derive our deep learning estimator and prove that it is a universal approximator to the spectral density under general assumptions. Our estimator can be trained without computing the autocovariance kernels and it can be parallelized to provide the estimates much faster than existing approaches. We validate its performance by simulations and an application to fMRI images.

Effective feature selection is essential for high-dimensional data analysis and machine learning. Unsupervised feature selection (UFS) aims to simultaneously cluster data and identify the most discriminative features. Most existing UFS methods linearly project features into a pseudo-label space for clustering, but they suffer from two critical limitations: (1) an oversimplified linear mapping that fails to capture complex feature relationships, and (2) an assumption of uniform cluster distributions, ignoring outliers prevalent in real-world data. To address these issues, we propose the Robust Autoencoder-based Unsupervised Feature Selection (RAEUFS) model, which leverages a deep autoencoder to learn nonlinear feature representations while inherently improving robustness to outliers. We further develop an efficient optimization algorithm for RAEUFS. Extensive experiments demonstrate that our method outperforms state-of-the-art UFS approaches in both clean and outlier-contaminated data settings.

In this work, we propose a simple and computationally efficient framework for evaluating whether machine learning models align with the structure of the data they learn from; that is, whether the model says what the data says. Unlike existing interpretability methods that focus exclusively on explaining model behavior, our approach establishes a baseline derived directly from the data itself. Drawing inspiration from Rubin's Potential Outcomes Framework, we quantify how strongly each feature separates the two outcome groups in a binary classification task, moving beyond traditional descriptive statistics to estimate each feature's effect on the outcome. By comparing these data-derived feature rankings with model-based explanations, we provide practitioners with an interpretable and model-agnostic method for assessing model-data alignment.