Donald Trump's son-in-law represents the kind of Washington figure for whom public office serves as a means to extraordinary wealth. "Kushner is plagued with conflicts of interest," Virginia Canter, a former White House counsel, said. "He is supposed to be working for the American people, but in fact he has tremendous financial incentives to further the interests of his investors." In the waning hours before the U.S. went to war with Iran, this February, Jared Kushner held a meeting with Iranian negotiators in Geneva--one last chance to avoid a conflict that would plunge the region into chaos and shock the world economy. Afterward, Kushner dismissed his counterparts as unworthy of his time. "Lots of tricks," he told reporters. Kushner, the boyish-looking son-in-law of Donald Trump, has been given an extraordinary brief in recent years. Along with Steve Witkoff, the President's special envoy, he has been charged with ending wars in Gaza, Ukraine, and just about anywhere else they might break out. Kushner, who is forty-five, has little regard for the craft of diplomacy. In his view, it is less important to know a region's history or the details of the partisans' negotiating positions than to simply hear the sides out and split the difference. Peace deals are like real-estate deals, he has said. Both involve hard bargaining and, at times, bluffing. The fact that so many diplomats before him have struggled is a testament not to the intransigence of international problems but to the mediocrity of his predecessors. "I've been getting criticized for the last couple of years, by all the people who have tried and failed, for not doing this the same way that they have," he said in 2020, after Trump unveiled his first Middle East peace plan. "If somebody was successful, I wouldn't be dealing with this." When Kushner and Witkoff began meeting with the Iranian negotiators, in February, the two countries were on the brink of war. In 2025, American and Israeli forces had bombed Iran's nuclear-enrichment facilities, which Western leaders said were engaged in a decades-long drive to develop a nuclear weapon. Afterward, Trump declared that Iran's capability to enrich uranium had been "obliterated." Yet Trump and other Western officials wanted further concessions. The Iranians maintained a large stockpile of lower-grade uranium. It was buried under rubble from the bombing in 2025, but Trump and Benjamin Netanyahu, the Israeli Prime Minister, insisted that the Iranians give it up and forswear any intention of enriching uranium in the future. Trump sent Kushner and Witkoff to present the Iranians with what amounted to a binary choice: turn over their entire stockpile or expect an attack. To emphasize his threats, he had dispatched an armada to the Middle East. "We have to have a meaningful deal, otherwise bad things happen," he said.

I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen One method is easier, but the other gets you faster, more reliable performance. Affinity is an image editor that's, unofficially, come to Linux. You can install it either via AppImage or a script. Both installations have their issues, but it's worth doing. One of the loudest cries centered around the Linux OS for years has been the need for Photoshop.

A common view of sensory processing is as probabilistic inference of latent causes from receptor activations. Standard approaches often assume these causes are a priori independent, yet real-world generative factors are typically correlated. Representing such structured priors in neural systems poses architectural challenges, particularly when direct interactions between units representing latent causes are biologically implausible or computationally expensive. Inspired by the architecture of the olfactory bulb, we propose a novel circuit motif that enables inference with correlated priors without requiring direct interactions among latent cause units. The key insight lies in using sister cells: neurons receiving shared receptor input but connected differently to local interneurons.

Current parameter-efficient fine-tuning (PEFT) methods have shown superior performance in continual learning. However, most existing PEFT-based methods focus on mitigating catastrophic forgetting by limiting modifications to the old task model caused by new tasks. This hinders backward knowledge transfer, as when new tasks have a strong positive correlation with old tasks, appropriately training on new tasks can transfer beneficial knowledge to old tasks. Critically, achieving backward knowledge transfer faces two fundamental challenges: (1) some parameters may be ineffective on task performance, which constrains the task solution space and model capacity; (2) since old task data are inaccessible, modeling task correlation via shared data is infeasible. To address these challenges, we propose CaLoRA, a novel causal-aware low-rank adaptation framework that is the first PEFT-based continual learning work with backward knowledge transfer. Specifically, we first propose parameter-level counterfactual attribution (PaCA) that estimates the causal effect of LoRA parameters via counterfactual reasoning, identifying effective parameters from a causal view. Second, we propose cross-task gradient adaptation (CaGA) to quantify task correlation by gradient projection and evaluate task affinity based on gradient similarity. By incorporating causal effect, task correlation, and affinity, CaGA adaptively adjusts task gradients, facilitating backward knowledge transfer without relying on data replay. Extensive experiments across multiple benchmarks and continual learning settings show that CaLoRA outperforms stateof-the-art methods.

Advancements in AI for science unlocks capabilities for critical drug discovery tasks such as protein-ligand binding affinity prediction. However, current models overfit to existing oversimplified datasets that does not represent naturally occurring and biologically relevant proteins with modifications. In this work, we curate a complete and modification-aware version of the widely used DAVIS dataset by incorporating 4,032 kinase-ligand pairs involving substitutions, insertions, deletions, and phosphorylation events. This enriched dataset enables benchmarking of predictive models under biologically realistic conditions. Based on this new dataset, we propose three benchmark settings--Augmented Dataset Prediction, Wild-Type to Modification Generalization, and Few-Shot Modification Generalization--designed to assess model robustness in the presence of protein modifications. Through extensive evaluation of both docking-free and docking-based methods, we find that docking-based model generalize better in zero-shot settings. In contrast, docking-free models tend to overfit to wild-type proteins and struggle with unseen modifications but show notable improvement when fine-tuned on a small set of modified examples. We anticipate that the curated dataset and benchmarks offer a valuable foundation for developing models that better generalize to protein modifications, ultimately advancing precision medicine in drug discovery.

Neural networks transform data through learned representations whose geometry affects separation, contraction, and generalization. Recent work studies this geometry using discrete curvature on neighborhood graphs, suggesting Ricci-flow-like behavior across layers. We develop a smooth operator-theoretic alternative for feedforward representation snapshots. Each feature cloud induces a Gaussian-kernel diffusion Markov operator, and transport, spectral, label-boundary, and local-scale observables are derived from this single object via Bakry-Emery $Γ$-calculus. In a balanced Gaussian class-conditional snapshot model with shared covariance, the population operator has closed-form class affinities, leakage, and coarse spectra, all controlled by pairwise regularized Mahalanobis separations $c_\varepsilon^{(a,b)}$. We also prove that the resulting operator observables vary smoothly under feature perturbations, while hard neighborhood-graph diagnostics can change discontinuously. Synthetic experiments validate the closed-form Gaussian bridge, while learned MNIST experiments show that the same operator observables track training, width, and perturbation stability. Together, these results give a stable operator-geometric framework for analyzing feedforward representation geometry.

This article proposes a biconvex modification to convex biclustering in order to improve its performance in high-dimensional settings. In contrast to heuristics that discard a subset of noisy features a priori, our method jointly learns and accordingly weighs informative features while discovering biclusters. Moreover, the method is adaptive to the data, and is accompanied by an efficient algorithm based on proximal alternating minimization, complete with detailed guidance on hyperparameter tuning and efficient solutions to optimization subproblems. These contributions are theoretically grounded; we establish finite-sample bounds on the objective function under sub-Gaussian errors, and generalize these guarantees to cases where input affinities need not be uniform. Extensive simulation results reveal our method consistently recovers underlying biclusters while weighing and selecting features appropriately, outperforming peer methods. An application to a gene microarray dataset of lymphoma samples recovers biclusters matching an underlying classification, while giving additional interpretation to the mRNA samples via the column groupings and fitted weights.

Given samples lying on any of a number of subspaces, subspace clustering is the task of grouping the samples based on the their corresponding subspaces. Many subspace clustering methods operate by assigning a measure of affinity to each pair of points and feeding these affinities into a graph clustering algorithm. This paper proposes a new paradigm for subspace clustering that computes affinities based on the corresponding conic geometry. The proposed conic subspace clustering (CSC) approach considers the convex hull of a collection of normalized data points and the corresponding tangent cones. The union of subspaces underlying the data imposes a strong association between the tangent cone at a sample $x$ and the original subspace containing $x$. In addition to describing this novel geometric perspective, this paper provides a practical algorithm for subspace clustering that leverages this perspective, where a tangent cone membership test is used to estimate the affinities. This algorithm is accompanied with deterministic and stochastic guarantees on the properties of the learned affinity matrix, on the true and false positive rates and spread, which directly translate into the overall clustering accuracy.

Graph clustering is a fundamental task in many data-mining and machine-learning pipelines. In particular, identifying a good hierarchical structure is at the same time a fundamental and challenging problem for several applications. The amount of data to analyze is increasing at an astonishing rate each day. Hence there is a need for new solutions to efficiently compute effective hierarchical clusterings on such huge data. The main focus of this paper is on minimum spanning tree (MST) based clusterings. In particular, we propose affinity, a novel hierarchical clustering based on Boruvka's MST algorithm. We prove certain theoretical guarantees for affinity (as well as some other classic algorithms) and show that in practice it is superior to several other state-of-the-art clustering algorithms.