Launching next week on T-Mobile's network, the cell plan takes a nuclear approach to online safety. A new US-wide cell phone network marketed to Christians is set to launch next week. It blocks porn, which experts in network security say marks the first time a US cell plan has used network-level blocking for such content that can't be turned off even by adult account owners. It's also rolling out a filter on sexual content aimed at blocking material related to gender and trans issues, which will be optional but turned on by default across all plans. The network, which is currently being tested ahead of its May 5 launch date, will be run by Radiant Mobile, a newly launched mobile virtual network operator (MVNO). These operators don't own cell towers but buy bandwidth from the big providers (in this case, T-Mobile) and sell to specific demographics (President Trump announced his own MVNO last year called Trump Mobile; CREDOMobile sends donations to progressive causes).

ARIA has a billion-dollar budget and big aspirations for tackling everything from epilepsy to Alzheimer's. The UK's Advanced Research and Innovation Agency (ARIA) was established in 2023 with the goal of pursuing "high-risk, high-reward" moonshots in sectors ranging from bolstering food security to new ways of ramping up human immunity . With more than £1 billion (about $1.3 billion) worth of government funding earmarked between now and 2030, one of ARIA's most ambitious programs is a £69 million initiative that aims to develop more tailored ways of modulating the human brain. The hope is to eventually address an entire range of disorders, from epilepsy to Alzheimer's. Reports have previously estimated that this suite of neurological conditions costs the UK economy tens of billions of dollars each year.

Causal discovery across multiple datasets is often constrained by data privacy regulations and cross-site heterogeneity, limiting the use of conventional methods that require a single, centralized dataset. To address these challenges, we introduce fedCI, a federated conditional independence test that rigorously handles heterogeneous datasets with non-identical sets of variables, site-specific effects, and mixed variable types, including continuous, ordinal, binary, and categorical variables. At its core, fedCI uses a federated Iteratively Reweighted Least Squares (IRLS) procedure to estimate the parameters of generalized linear models underlying likelihood-ratio tests for conditional independence. Building on this, we develop fedCI-IOD, a federated extension of the Integration of Overlapping Datasets (IOD) algorithm, that replaces its meta-analysis strategy and enables, for the fist time, federated causal discovery under latent confounding across distributed and heterogeneous datasets. By aggregating evidence federatively, fedCI-IOD not only preserves privacy but also substantially enhances statistical power, achieving performance comparable to fully pooled analyses and mitigating artifacts from low local sample sizes. Our tools are publicly available as the fedCI Python package, a privacy-preserving R implementation of IOD, and a web application for the fedCI-IOD pipeline, providing versatile, user-friendly solutions for federated conditional independence testing and causal discovery.

PageRank (PR), an algorithm originally proposed by Page et al. for ranking web-pages [1] has found manysuccessful applications, including community detection [2,3],linkprediction [4]and recommendersystemdesign[5,6].

Note that a metric space is calledcompact if X is closed and bounded.

We revisit Deep Linear Discriminant Analysis (Deep LDA) from a likelihood-based perspective. While classical LDA is a simple Gaussian model with linear decision boundaries, attaching an LDA head to a neural encoder raises the question of how to train the resulting deep classifier by maximum likelihood estimation (MLE). We first show that end-to-end MLE training of an unconstrained Deep LDA model ignores discrimination: when both the LDA parameters and the encoder parameters are learned jointly, the likelihood admits a degenerate solution in which some of the class clusters may heavily overlap or even collapse, and classification performance deteriorates. Batchwise moment re-estimation of the LDA parameters does not remove this failure mode. We then propose a constrained Deep LDA formulation that fixes the class means to the vertices of a regular simplex in the latent space and restricts the shared covariance to be spherical, leaving only the priors and a single variance parameter to be learned along with the encoder. Under these geometric constraints, MLE becomes stable and yields well-separated class clusters in the latent space. On images (Fashion-MNIST, CIFAR-10, CIFAR-100), the resulting Deep LDA models achieve accuracy competitive with softmax baselines while offering a simple, interpretable latent geometry that is clearly visible in two-dimensional projections.

Solving the quantum many-body Schrödinger equation is a fundamental and challenging problem in the fields of quantum physics, quantum chemistry, and material sciences. One of the common computational approaches to this problem is Quantum Variational Monte Carlo (QVMC), in which ground-state solutions are obtained by minimizing the energy of the system within a restricted family of parameterized wave functions. Deep learning methods partially address the limitations of traditional QVMC by representing a rich family of wave functions in terms of neural networks. However, the optimization objective in QVMC remains notoriously hard to minimize and requires second-order optimization methods such as natural gradient. In this paper, we first reformulate energy functional minimization in the space of Born distributions corresponding to particle-permutation (anti-)symmetric wave functions, rather than the space of wave functions. We then interpret QVMC as the Fisher--Rao gradient flow in this distributional space, followed by a projection step onto the variational manifold. This perspective provides us with a principled framework to derive new QMC algorithms, by endowing the distributional space with better metrics, and following the projected gradient flow induced by those metrics. More specifically, we propose Wasserstein Quantum Monte Carlo (WQMC), which uses the gradient flow induced by the Wasserstein metric, rather than the Fisher--Rao metric, and corresponds to the probability mass, rather than it. We demonstrate empirically that the dynamics of WQMC results in faster convergence to the ground state of molecular systems.