Why is Israel still in southern Lebanon? A war to shape Lebanon's future At least one person has been killed in an Israeli strike in southern Lebanon, according to the state-run National News Agency (NNA), as near-daily attacks by Israel continue despite a November ceasefire. The attack on Monday hit an excavator in the Shamsiyah area of Sohmor in the Bekaa Valley, killing its driver. Footage on social media, verified by Al Jazeera, showed emergency responders carrying the victim away on a stretcher. One drone targeted the town of Aitaroun on Monday afternoon while another bombed a house in Houmin al-Fauqa. No casualties were reported in those attacks.

A suite of technologies are helping taxonomists speed up species identification. Listen to more stories on the Noa app. Across a Swiss meadow and into its forested edges, the drone dragged a jumbo-size cotton swab from a 13-foot tether. Along its path, the moistened swab collected scraps of life: some combination of sloughed skin and hair; mucus, saliva, and blood splatters; pollen flecks and fungal spores. Later, biologists used a sequencer about the size of a phone to stream the landscape's DNA into code, revealing dozens upon dozens of species, some endangered, some invasive.

Electronic Arts (EA), one of the biggest gaming companies in the world, has agreed a deal to sell the company for $55bn (£41bn). The consortium of buyers include Saudi Arabia's Public Investment Fund (PIF), Silver Lake and Jared Kushner's Affinity Partners. EA is known for making and publishing best-selling games such as EA FC, formerly known as Fifa, The Sims and Mass Effect. It is understood to be the largest leveraged buyout in history - where a significant amount of the purchase is financed by borrowing money. The deal will take EA private - meaning all of its public shares will be purchased and it will no longer be traded on a stock exchange.

Spain's Valencia region has been struck by more bad weather, a year after deadly floods killed more than 230 people there. A red alert has been put in place as Storm Gabrielle hits the region. Footage shows floodwater in parts of Valencia and Zaragoza, in the neighbouring Aragon region. The meteorological agency AEMET said between 160 and 200mm of rain had fallen in six to eight hours around the Ebro delta. No injuries have been reported, but schools, libraries and parks are closed in Valencia on Monday.

Denmark has banned all civilian drone flights this week ahead of a European Union summit in Copenhagen, the country's transport minister said on Sunday. The ministry said the decision was made in order to simplify security work for the police, and they could not accept foreign drones creating uncertainty and disruption. Denmark is one of several European countries that have reported drone incidents in recent weeks, with unidentified drones sighted above Danish military sites as recently as Saturday. Defence ministers from 10 EU countries have agreed to create a drone wall in response to the sightings, and Nato says it has enhanced vigilance across the Baltic. In their statement announcing the ban, the transport ministry said police were on significantly increased alert ahead of this week's summit and that they needed to take care of Danes and our guests.

Deep neural networks achieve state-of-the-art performance in estimating heterogeneous treatment effects, but their opacity limits trust and adoption in sensitive domains such as medicine, economics, and public policy. Building on well-established and high-performing causal neural architectures, we propose causalKANs, a framework that transforms neural estimators of conditional average treatment effects (CATEs) into Kolmogorov--Arnold Networks (KANs). By incorporating pruning and symbolic simplification, causalKANs yields interpretable closed-form formulas while preserving predictive accuracy. Experiments on benchmark datasets demonstrate that causalKANs perform on par with neural baselines in CATE error metrics, and that even simple KAN variants achieve competitive performance, offering a favorable accuracy--interpretability trade-off. By combining reliability with analytic accessibility, causalKANs provide auditable estimators supported by closed-form expressions and interpretable plots, enabling trustworthy individualized decision-making in high-stakes settings. We release the code for reproducibility at https://github.com/aalmodovares/causalkans .

Most uncertainty quantification (UQ) approaches provide a single scalar value as a measure of model reliability. However, different uncertainty measures could provide complementary information on the prediction confidence. Even measures targeting the same type of uncertainty (e.g., ensemble-based and density-based measures of epistemic uncertainty) may capture different failure modes. We take a multidimensional view on UQ by stacking complementary UQ measures into a vector. Such vectors are assigned with Monge-Kantorovich ranks produced by an optimal-transport-based ordering method. The prediction is then deemed more uncertain than the other if it has a higher rank. The resulting VecUQ-OT algorithm uses entropy-regularized optimal transport. The transport map is learned on vectors of scores from in-distribution data and, by design, applies to unseen inputs, including out-of-distribution cases, without retraining. Our framework supports flexible non-additive uncertainty fusion (including aleatoric and epistemic components). It yields a robust ordering for downstream tasks such as selective prediction, misclassification detection, out-of-distribution detection, and selective generation. Across synthetic, image, and text data, VecUQ-OT shows high efficiency even when individual measures fail. The code for the method is available at: https://github.com/stat-ml/multidimensional_uncertainty.

Sparse Bayesian learning (SBL) associates to each weight in the underlying linear model a hyperparameter by assuming that each weight is Gaussian distributed with zero mean and precision (inverse variance) equal to its associated hyperparameter. The method estimates the hyperparameters by marginalizing out the weights and performing (marginalized) maximum likelihood (ML) estimation. SBL returns many hyperparameter estimates to diverge to infinity, effectively setting the estimates of the corresponding weights to zero (i.e., pruning the corresponding weights from the model) and thereby yielding a sparse estimate of the weight vector. In this letter, we analyze the marginal likelihood as function of a single hyperparameter while keeping the others fixed, when the Gaussian assumptions on the noise samples and the weight distribution that underlies the derivation of SBL are weakened. We derive sufficient conditions that lead, on the one hand, to finite hyperparameter estimates and, on the other, to infinite ones. Finally, we show that in the Gaussian case, the two conditions are complementary and coincide with the pruning condition of fast SBL (F-SBL), thereby providing additional insights into this algorithm.

Learning, whether natural or artificial, is a process of selection. It starts with a set of candidate options and selects the more successful ones. In the case of machine learning the selection is done based on empirical estimates of prediction accuracy of candidate prediction rules on some data. Due to randomness of data sampling the empirical estimates are inherently noisy, leading to selection under uncertainty. The book provides statistical tools to obtain theoretical guarantees on the outcome of selection under uncertainty. We start with concentration of measure inequalities, which are the main statistical instrument for controlling how much an empirical estimate of expectation of a function deviates from the true expectation. The book covers a broad range of inequalities, including Markov's, Chebyshev's, Hoeffding's, Bernstein's, Empirical Bernstein's, Unexpected Bernstein's, kl, and split-kl. We then study the classical (offline) supervised learning and provide a range of tools for deriving generalization bounds, including Occam's razor, Vapnik-Chervonenkis analysis, and PAC-Bayesian analysis. The latter is further applied to derive generalization guarantees for weighted majority votes. After covering the offline setting, we turn our attention to online learning. We present the space of online learning problems characterized by environmental feedback, environmental resistance, and structural complexity. A common performance measure in online learning is regret, which compares performance of an algorithm to performance of the best prediction rule in hindsight, out of a restricted set of prediction rules. We present tools for deriving regret bounds in stochastic and adversarial environments, and under full information and bandit feedback.

Abstract--Future networks are envisioned to connect massive artificial intelligence (AI) agents, enabling their extensive collaboration on diverse tasks. Compared to traditional entities, these agents naturally suit the semantic communication (SC), which can significantly enhance the bandwidth efficiency. Nevertheless, SC requires the knowledge among agents to be aligned, while agents have distinct expert knowledge for their individual tasks in practice. In this paper, we propose a distillation-enabled knowledge alignment protocol (DeKAP), which distills the expert knowledge of each agent into parameter-efficient low-rank matrices, allocates them across the network, and allows agents to simultaneously maintain aligned knowledge for multiple tasks. We formulate the joint minimization of alignment loss, communication overhead, and storage cost as a large-scale integer linear programming problem and develop a highly efficient greedy algorithm. From computer simulation, the DeKAP establishes knowledge alignment with the lowest communication and computation resources compared to conventional approaches. Future communication networks will usher in a new era of the "Internet of Intelligence," where human beings, devices, and a wide range of artificial intelligence (AI) agents are seamlessly interconnected [1].