Technology Cold War espionage added $4.6 billion to East Germany's economy Stolen trade secrets boosted the country's output--to a point. 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. A Wartburg 353 and Trabant cars parked beside the Berlin Wall while a queue of East German people wait to cross to the West after the Wall fell and travel restrictions were lifted, Berlin, December 1989. No matter what side you were on, espionage was a cornerstone of Cold War political life . But it was also big business.

Stefon Diggs guilty of NFL's dumbest move of Week 1 and it may have decided Commanders loss to Eagles John Harbaugh's Giants on full display in bruising win over Cowboys to kick off NFL season WWE star Stephanie Vaquer captures Women's World Championship at Chile live event in surprising moment Ben Shelton loses in US Open final to Zverev, but America has found its next big men's tennis star Jeremiyah Love makes immediate statement with touchdown on first career drive in Cardinals' upset win Josh Allen torches one of NFL's best defenses to make case it's time to move on from 2025 playoff loss Breanna Stewart's double-double, Caitlin Clark's emergence off bench help US to World Cup win over France Afghan woman deported in landmark'alien terrorist' court case AI policy expert dismisses Big Tech regulation calls: 'We should see through this' AI agents are'hacking out of the container' despite best efforts to prevent it, expert warns Prosecution in Lindsay Clancy trial may have'alienated' jurors, criminal defense attorney says You treat a nuclear power'significantly different' than a non-nuclear power: Gen. Keith Kellogg Accepting political violence as a form of expression is'dangerous,' Jonathan Turley warns'The Squad' faces backlash over claims'modern-day lynchings' are now common in America OutKick Caitlin Clark says she and Angel Reese have'always been friends' after winning FIBA Women's World Cup The two WNBA stars connected on key plays in the FIBA Women's World Cup final against France in Berlin Caitlin Clark is asked about her friendship with Angel Reese after winning FIBA Women's World Cup gold in Berlin, Germany. Caitlin Clark and Angel Reese sat next to each other when they were on the bench throughout the final round of the 2026 FIBA Women's World Cup. They were seen laughing together at times, and sharing emotion when their team was down. They connected on several big plays, and changed the momentum of the gold medal game against France right before halftime. The World Cup was the first time the two played together in high-stakes games, and they came out as world champions.

Why wait for the future of technology to arrive when you can see it here and now? These are the highlights from Europe's biggest tech show. 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. A humanoid robot from producer Midea is seen in front of the fair during the IFA Consumer Electronics and Home Appliances trade fair in Berlin, Germany, on September 5, 2026. We may earn revenue from the products available on this page and participate in affiliate programs.

In online clustering problems, there is often a large amount of uncertainty over possible cluster assignments that cannot be resolved until more data are observed. This difficulty is compounded when clusters follow complex distributions, as is the case with text data. Sequential Monte Carlo (SMC) methods give a natural way of representing and updating this uncertainty over time, but have prohibitive memory requirements for large-scale problems. We propose a novel SMC algorithm that decomposes clustering problems into approximately independent subproblems, allowing a more compact representation of the algorithm state. Our approach is motivated by the knowledge base construction problem, and we show that our method is able to accurately and efficiently solve clustering problems in this setting and others where traditional SMC struggles.

As deep neural networks are deployed in safety-critical domains such as autonomous driving and medical diagnosis, stakeholders need explanations that are interpretable but also trustworthy with formal guarantees. Existing XAI methods fall short: heuristic attribution techniques (e.g., LIME, Integrated Gradients) highlight influential features but offer no mathematical guarantees about decision boundaries, while formal methods verify robustness yet remain untargeted, analyzing the nearest boundary regardless of whether it represents a critical risk. In safety-critical systems, not all misclassifications carry equal consequences; confusing a "Stop" sign for a "60 kph" sign is far more dangerous than confusing it with a "No Passing" sign. We introduce ViTaX (Verified and Targeted Explanations), a formal XAI framework that generates targeted semifactual explanations with mathematical guarantees. For a given input (class y) and a user-specified critical alternative (class t), ViTaX: (1) identifies the minimal feature subset most sensitive to the y->t transition, and (2) applies formal reachability analysis to guarantee that perturbing these features by epsilon cannot flip the classification to t. We formalize this through Targeted epsilon-Robustness, certifying whether a feature subset remains robust under perturbation toward a specific target class. ViTaX is the first method to provide formally guaranteed explanations of a model's resilience against user-identified alternatives. Evaluations on MNIST, GTSRB, EMNIST, and TaxiNet demonstrate over 30% fidelity improvement with minimal explanation cardinality.

The ISOKANN (Invariant Subspaces of Koopman Operators Learned by Artificial Neural Networks) framework provides a data-driven route to extract collective variables (CVs) and effective dynamics from complex molecular systems. In this work, we integrate the theoretical foundation of Koopman operators with Krylov-like subspace algorithms, and reduced dynamical modeling to build a coherent picture of how to describe metastable transitions in high-dimensional systems based on CVs. Starting from the identification of CVs based on dominant invariant subspaces, we derive the corresponding effective dynamics on the latent space and connect these to transition rates and times, committor functions, and transition pathways. The combination of Koopman-based learning and reduced-dimensional effective dynamics yields a principled framework for computing transition rates and pathways from simulation data. Numerical experiments on one-, two-, and three-dimensional benchmark potentials illustrate the ability of ISOKANN to reconstruct the coarse-grained kinetics and reproduce transition times across enthalpic and entropic barriers.

The proposed algorithm seeks to provide a novel data-driven framework for the discovery of stochastic differential equations (SDEs) by application of the Weak-formulation to stochastic SINDy. This Weak formulation of the algorithm provides a noise-robust methodology that avoids traditional noisy derivative computation using finite differences. An additional novelty is the adoption of spatial Gaussian test functions in place of temporal test functions, wherein the use of the kernel weight $K_j(X_{t_n})$ guarantees unbiasedness in expectation and prevents the structural regression bias that is otherwise pertinent with temporal test functions. The proposed framework converts the SDE identification problem into two SINDy based linear sparse identification problems. We validate the algorithm on three SDEs, for which we recover all active non-linear terms with coefficient errors below 4%, stationary-density total-variation distances below 0.01, and autocorrelation functions that reproduce true relaxation timescales across all three benchmarks faithfully.

Generalization is a central concept in machine learning theory, yet for quantum models, it is predominantly analyzed through uniform bounds that depend on a model's overall capacity rather than the specific function learned. These capacity-based uniform bounds are often too loose and entirely insensitive to the actual training and learning process. Previous theoretical guarantees have failed to provide non-uniform, data-dependent bounds that reflect the specific properties of the learned solution rather than the worst-case behavior of the entire hypothesis class. To address this limitation, we derive the first PAC-Bayesian generalization bounds for a broad class of quantum models by analyzing layered circuits composed of general quantum channels, which include dissipative operations such as mid-circuit measurements and feedforward. Through a channel perturbation analysis, we establish non-uniform bounds that depend on the norms of learned parameter matrices; we extend these results to symmetry-constrained equivariant quantum models; and we validate our theoretical framework with numerical experiments. This work provides actionable model design insights and establishes a foundational tool for a more nuanced understanding of generalization in quantum machine learning.

Transformers owe much of their empirical success in natural language processing to the self-attention blocks. Recent perspectives interpret attention blocks as interacting particle systems, whose mean-field limits correspond to gradient flows of interaction energy functionals on probability density spaces equipped with Wasserstein-$2$-type metrics. We extend this viewpoint by introducing accelerated attention blocks derived from inertial Nesterov-type dynamics on density spaces. In our proposed architecture, tokens carry both spatial (feature) and velocity variables. The time discretization and the approximation of accelerated density dynamics yield Hamiltonian momentum attention blocks, which constitute the proposed accelerated attention architectures. In particular, for linear self-attention, we show that the attention blocks approximate a Stein variational gradient flow, using a bilinear kernel, of a potential energy. In this setting, we prove that elliptically contoured probability distributions are preserved by the accelerated attention blocks. We present implementable particle-based algorithms and demonstrate that the proposed accelerated attention blocks converge faster than the classical attention blocks while preserving the number of oracle calls.

You can now buy a humanoid robot housekeeper for less than the price of a second-hand car. But before splashing out, there's something you need to know Science fiction is strewn with humanoid robots, from bad-tempered Bender in to cunning Ava in . And it has long seemed like that's the natural home for such robots - on the screen and in books. The idea of a walking, talking, functioning robot with two arms and two legs has appeared to be a distant dream. Last year, machines ran, boxed and even played football at China's World Humanoid Robot Games, albeit sometimes falling over in the process . Meanwhile, companies have been readying their own range of humanoids that promise to do something a bit more useful: help around the house .