Pope Leo XIV speaks during a meeting with bishops, members of the clergy and families whose members have been victims of environmental pollution at the Cathedral of Santa Maria Assunta, in Acerra, Italy, on 23 May 2026. Pope Leo XIV speaks during a meeting with bishops, members of the clergy and families whose members have been victims of environmental pollution at the Cathedral of Santa Maria Assunta, in Acerra, Italy, on 23 May 2026. Americans echo Pope Leo's concerns about AI: 'It threatens workers, privacy and human life' Guardian readers in the US spoke of fears about unregulated AI in response to the pope's encyclical warning about the risks of the technology I n his first major papal text since assuming leadership of the Catholic church last year, Pope Leo issued a stark warning about the rise of artificial intelligence this week, denouncing the "culture of power" driving the AI age. Calling for the "most rigorous" ethical constraints on AI - which he described as one of the greatest threats facing humanity today - the first US-born pope also warned of "new forms of slavery" emerging through the digital economy. Speaking to the Guardian, readers in the US echoed the pope's concerns, describing AI as an "unregulated" industry increasingly being used to the "detriment of too many people", while also raising fears about surveillance, labor displacement, war and environmental harm .

Learning meaningful latent representations from nonlinear fMRI data remains a fundamental challenge in neuroimaging analysis. Traditional independent component analysis, widely used due to its ability to estimate interpretable functional brain networks, relies on a linear mixing assumption for latent sources, limiting its ability to capture the inherently nonlinear and complex organization of brain dynamics. More recently, deep representation learning methods have emerged as promising alternatives for modeling nonlinear latent structure. However, many of these approaches have been evaluated primarily on simulated datasets or natural image benchmarks, with comparatively limited validation on real-world neuroimaging data such as fMRI. In this work, we are motivated by the $β$-TCVAE (Total Correlation Variational Autoencoder), a refinement of the $β$-VAE framework for learning latent representations without introducing additional hyperparameters during training. We adapt and modify this model to fMRI data for nonlinear source disentanglement, aiming to separate mixed spatial and temporal brain signals into interpretable components. We show that the $β$-TCVAE framework can recover meaningful nonlinear spatial components with biological relevance, including well-established intrinsic connectivity networks such as the default mode network. Furthermore, we evaluate the learned representations using functional network connectivity, showing that the latent structure captures coherent and interpretable brain organization patterns. This study provides a pilot investigation that bridges nonlinear representation learning and fMRI analysis.

Science Archaeology 1.3 million people share DNA with Maryland's earliest colonists Some are even related to the former colony's first governor. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The exterior of the reconstructed Catholic chapel at Historic St.Mary's City in St. Mary's City, Maryland. Breakthroughs, discoveries, and DIY tips sent six days a week. In 1634, English settlers established St. Mary's City as the first permanent outpost in the colony of Maryland.

Figure 4: Impact of the proportion of injected noise for learning Paragraph Embeddings on XSum dataset. PPLint and the PPL of the generation obtained from training PLANNER on the corresponding z at different noise level. We observed when the value of p is within (0, 0.7), there Performing a grid search on each task using diffusion models is an expensive process. However, it has been observed that an increase in the value of p leads to a deviation between the two. This could be attributed to a higher conversion error that occurs when p is excessively large. A.2 Selection of number of latent code k The parameter k determines the number of latent codes used to represent a paragraph and therefore controls the compression level. Latent codes with smaller values of k are easier to model using the diffusion model, but may struggle to accurately preserve all the information in the original text. Additionally, smaller values of k offer computational efficiency as the sequence length for the diffusion model is k. To determine the best set of latent codes, we conducted experiments using three different methods: 1) selecting the first k hidden vectors, 2) selecting the last k hidden vectors, and 3) selecting interleaving hidden vectors, one for every L k hidden vectors. The results of the ablation study are presented in Table 5. Based on our findings, we observed no significant difference among the different choices, so we opted for option 1). Furthermore, we discovered that increasing the value of k does not lead to a dramatic improvement in performance. To balance between efficiency and performance, in most of our study we only use k =16 Setup BLEU_clean BLEU_robust First k (k=16) 79.59 43.17 A.3 Reconstruction, denoising and interpolation examples In Table 6, we present examples that demonstrate the adeptness of the trained Variational Paragraph Embedder in providing clean and denoised reconstructions. Additionally, we showcase interpolation results (Table 7, 8) derived from two random sentences in the hotel review dataset. The interpolated paragraph is usually coherent and incorporates inputs from both sentences, characterizing the distributional smoothness of the latent space. Reconstructed text complaints: after two nights stay, i asked the maid to clean our room (empty the wastebasket & make the bed). Denoising reconstruction (hotel review), noise level 0.3 Original text * * * check out the bathroom picture * * * i was in nyc by myself to watch some friends participate in the us olympic marathon trials. Corrupted text * * [unused697] check exams the bathroom picture * * slams i was in nyc mead myself yankee 2016 some scotch ruin in the outfielder olympicnca trials.

Matching is one of the most fundamental and broadly applicable problems across many domains. In these diverse real-world applications, there is often a degree of uncertainty in the input which has led to the study of stochastic matching models. Here, each edge in the graph has a known, independent probability of existing derived from some prediction. Algorithms must probe edges to determine existence and match them irrevocably if they exist. Further, each vertex may have a patience constraint denoting how many of its neighboring edges can be probed. We present new ordered contention resolution schemes yielding improved approximation guarantees for some of the foundational problems studied in this area. For stochastic matching with patience constraints in general graphs, we provide a 0.382-approximate algorithm, significantly improving over the previous best 0.31-approximation of Baveja et al. (2018). When the vertices do not have patience constraints, we describe a 0.432-approximate random order probing algorithm with several corollaries such as an improved guarantee for the Prophet Secretary problem under Edge Arrivals. Finally, for the special case of bipartite graphs with unit patience constraints on one of the partitions, we show a 0.632-approximate algorithm that improves on the recent 1/3-guarantee of Hikima et al. (2021).

Maryland is set to become the first state to ban surveillance pricing in grocery stores after Gov. Wes Moore said he will sign the new law taking effect October 2026.

The minimax problems arise throughout machine learning applications, ranging from adversarial training and policy evaluation in reinforcement learning to AUROC maximization. To address the large-scale distributed data challenges across multiple clients with communication-efficient distributed training, federated learning (FL) is gaining popularity. Many optimization algorithms for minimax problems have been developed in the centralized setting (i.e., single-machine). Nonetheless, the algorithm for minimax problems under FL is still underexplored. In this paper, we study a class of federated nonconvex minimax optimization problems. We propose FL algorithms (FedSGDA+ and FedSGDA-M) and reduce existing complexity results for the most common minimax problems. For nonconvex-concave problems, we propose FedSGDA+ and reduce the communication complexity to O(ε 6). Under nonconvex-strongly-concave and nonconvex-PL minimax settings, we prove that FedSGDA-M has the best-known sample complexity of O(κ3N 1ε 3) and the best-known communication complexity of O(κ2ε 2). FedSGDA-M is the first algorithm to match the best sample complexity O(ε 3) achieved by the single-machine method under the nonconvex-strongly-concave setting.