Our cousins'Homo juluensis' knew how to adapt in the face of an ice age. 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. One of the 146,000-year-old stone cores used to make butcher's tools, found in Lingjing, China. Breakthroughs, discoveries, and DIY tips sent six days a week. A remarkable collection of ancient stone tools proves that human creativity can thrive in challenging times.

Plug-in panels are getting popular--how do we make sure they're safe? Dozens of US states are considering legislation to allow people to install plug-in solar systems, often called balcony solar. These small arrays require little to no setup and could help cut emissions and power bills. Balcony solar is already popular in Europe, and proponents say that the systems could make solar power more accessible for more people in the US, including renters. As popularity rises, though, some experts caution that there are safety concerns with how balcony solar would work with existing electrical equipment in homes. Let's talk about what balcony solar is, why it's unique, and how new testing requirements could affect our progress toward deploying the technology in the US.

Cybersecurity experts said the research was interesting, though not alarming at this stage. Cybersecurity experts said the research was interesting, though not alarming at this stage. 'No one has done this in the wild': study observes AI replicate itself It's the stuff of science fiction cinema, or particularly breathless AI company blogposts: new research finds recent AI systems can independently copy themselves on to other computers. In the doom scenario, this means that when the superintelligent AI goes rogue, it will escape shutdown by seeding itself across the world wide web, lurking outside the reach of frantic IT professionals and continuing to plot world domination or paving over the world with solar panels . "We're rapidly approaching the point where no one would be able to shut down a rogue AI, because it would be able to self-exfiltrate its weights and copy itself to thousands of computers around the world," said Jeffrey Ladish, the director of Palisade research, a Berkeley-based organisation which did the study.

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. Psilocybin may help treat issues like PTSD, depression, and alcoholism. Breakthroughs, discoveries, and DIY tips sent six days a week. Psilocybin is the psychoactive compound that puts the "magic" in magic mushrooms . Ingest enough of a fungus like, and users are liable to experience sensory hallucinations, euphoria, and even altered perceptions of time.

Jeffrey Epstein scrawled suicide note finally released: 'No fun. Surprising fate of CNN founder Ted Turner's multibillion-dollar fortune after thrice-married father-of-five died aged 87 Wall Street Titan lays out his ultimate revenge for woke NYC mayor Mamdani's'creepy weird' video Mike Vrabel'rented a boat with pregnant Dianna Russini in 2021' months before she welcomed first son Ultimate Spirit Airlines compensation guide: 'Magic words' to tell your bank for BIGGEST refund... what to do if you DIDN'T use a credit card... how to reclaim higher cost of new flights.... and'rescue' option when all else fails Once-bustling Nevada vacation resort becomes America's newest GHOST TOWN as its final hotel closes Farrah Fawcett's twisted family secrets: Siblings of her devil-horned son accused of hideous knife spree reveal dark childhood home truths Tragic Saved By The Bell star Dustin Diamond's residual pay revealed after his shock death at age 44 Rat virus'was brought onto cruise ship by birdwatcher couple who visited garbage dump to snap birds before setting off': Possible cause revealed - as Brits face eight-week quarantine Scandal as female World Cup soccer player is accused by police of raping baby-faced boy, 14, up to'three times a week' Triple Crown thrown into disarray with major announcement from Kentucky Derby winner Golden Tempo's trainer The photos that say it all! Justin Baldoni beams as he steps out with his wife for the first time since Blake Lively's humiliating lawsuit settlement The next generation of Ozempic is here. Turbo shots deliver 250% more weight loss... at record speeds. Patients are begging for them - but there's a major warning: DR SHEILA NAZARIAN Meghan Markle shares unseen photo of Prince Archie asleep on Harry's chest as a baby to celebrate his 7th birthday I sat with FedEx child killer Tanner Horner for weeks.

We develop a physics-informed learning framework for energy-shaping control of port-Hamiltonian (pH) systems from trajectory data. The proposed approach co-learns a pH system model and an optimal energy-balancing passivity-based controller (EB-PBC) through alternating optimization with policy-aware data collection. At each iteration, the system model is refined using trajectory data collected under the current control policy, and the controller is re-optimized on the updated model. Both components are parameterized by neural networks that embed the pH dynamics and EB-PBC structure, ensuring interpretability in terms of energy interactions. The learned controller renders the closed-loop system inherently passive and provably stable, and exploits passive plant dynamics without canceling the natural potential. A dissipation regularization enforces strict energy decay during training, thereby enhancing robustness to sim-to-real gaps. The proposed framework is validated on state-regulation and swing-up tasks for planar and torsional pendulum systems.

Bayesian optimization is a sequential method for minimizing objective functions that are expensive to evaluate and about which few assumptions can be made. By using all gathered data to train a Gaussian process model for the function and adaptively employing a mixture of global exploration and local exploitation, this method has been used for optimization in many fields including machine learning, automotive engineering and reinforcement learning. However, the standard method suffers from two problems: 1) with cubic computational complexity in the training-set size it eventually becomes computationally infeasible to train the model, and 2) globally modeling the objective function is not necessarily optimal given the local nature of minimization. Using flexible and recursive binary partitioning of the search space, we adapt both the modeling and acquisitive aspects of standard Bayesian optimization to work harmoniously with the partitioning scheme, thereby ameliorating both standard shortcomings. We compare our method against a commonly used Bayesian optimization library on seven challenging test functions, ranging in dimensionality from $6$ to $124$, and show that our method achieves superior optimization performance in all tests. In addition our method has linear computational complexity.

In complex real-world settings, optimization is challenged by the presence of diverse models of differing fidelity. In many optimization problems, a single model is treated as the most accurate representation of the underlying system, while other models are evaluated primarily by their agreement with this presumed most accurate model. Yet in real-world applications, model accuracy is rarely known a priori and assuming a single most accurate model can be misleading. This paper addresses this gap by proposing a flexible set-based optimization methodology called Set-Based Optimization with Multiple Models (S-BOMM) that works with multiple models without the assumption of a most accurate high-fidelity model. Unlike traditional optimization approaches that focus on finding an optimal solution according to the high-fidelity model, our methodology utilizes consistency between models to identify good solutions across multiple models. A probabilistic analysis of the consistency method is provided that bounds the likelihood of the methodology producing correct or incorrect results. Empirical results demonstrate the effectiveness of S-BOMM on test problems. By focusing on the consistency across models rather than relying on a single best solution, this set-based approach offers a practical alternative to optimization problems where multiple models must be considered without assuming a single most accurate high-fidelity model.

Optimization over the Stiefel manifold $\mathrm{St}(p,d)$, the set of $p \times d$ column-orthonormal matrices, is fundamental in statistics, machine learning, and scientific computing, yet remains challenging in the presence of non-convex, non-smooth, or black-box objectives. Existing methods largely rely on either convex relaxations or gradient-based Riemannian optimization, limiting applicability in derivative-free and highly multimodal settings. We propose \textsc{BOOOM} (Black-box Optimization Over Orthonormal Manifolds), a general-purpose framework for loss-function-agnostic optimization on $\mathrm{St}(p,d)$. The key idea is a global Givens rotation-based parametrization that maps the manifold to an unconstrained Euclidean angle space while preserving feasibility exactly. Building on this representation, BOOOM employs a structured, parallelizable, derivative-free search based on Recursive Modified Pattern Search, enabling systematic exploration through plane-wise rotations without requiring gradient information and facilitating escape from poor local optima. We establish a unified theoretical framework showing equivalence between angle-space and manifold optimization, transfer of stationarity, and global convergence in probability under mild conditions. Empirical results across diverse problems, including heterogeneous quadratic optimization, low-rank and sparse matrix decomposition, independent component analysis, and orthogonal joint diagonalization, among other widely studied settings, demonstrate strong performance relative to state-of-the-art methods, particularly in non-smooth and highly multimodal regimes. We further illustrate its practical utility through a novel supervised PCA formulation applied to metabolomics data in colorectal cancer.

Many machine learning problems involve data supported on curved spaces such as spheres, rotation groups, hyperbolic spaces, and general Riemannian manifolds, where Euclidean geometry can distort distances, averages, and the resulting optimal transport (OT) problem. Existing manifold OT methods have pursued amortized out-of-sample maps, while entropic regularization has made discrete OT more scalable, but these advantages have remained largely disjoint. We propose Entropic Riemannian Neural Optimal Transport (Entropic RNOT), a unified framework that combines intrinsic entropic OT with amortized out-of-sample evaluation on Riemannian manifolds. Our method learns a single target-side Schrödinger potential through a neural pullback parameterization, recovers the induced Gibbs coupling, and uses the resulting conditional laws to construct intrinsic transport surrogates. These include barycentric projections on Cartan-Hadamard manifolds and heat-smoothed conditional surrogates on stochastically complete manifolds, the latter turning possibly atomic target laws into absolutely continuous ones. For fixed regularization $\varepsilon>0$, we prove that the proposed hypothesis class recovers the entropic optimal coupling in strong probabilistic metrics. As consequences, barycentric surrogates converge in $L^2$, while heat-smoothed surrogates are stable at fixed heat time and asymptotically unbiased as the heat time vanishes. The guarantees hold for compactly supported data on possibly noncompact manifolds. Empirically, our method matches or improves over Euclidean, tangent-space, and log-Euclidean baselines on benchmarks over $\mathbb{S}^2$, $\mathrm{SO}(3)$, $\mathrm{SPD}(3)$, $\mathrm{SE}(3)$, and $\mathbb{H}^2$, scales favorably relative to discrete manifold Sinkhorn, and in a protein-ligand docking application, refines poses on $\mathrm{SE}(3)$ without retraining or per-instance optimization.