Numerous algorithms have been proposed and investigated, among which the k means [1], Spectral clustering [2, 3], DB-SCAN [4], and the well-known Mean-shift (MS) clustering algorithm. MS is an effective non-parametric iterative algorithm [5], which is versatile for clustering, tracking, and smoothing tasks. A well-known and used variant of MS is the blurring mean-shift (BMS) [6]. Both MS and BMS algorithms can be coined "deterministic" iterative procedures aiming to find local maximiz-ers of an objective function, since they do not involve any random selection of points to perform their update rule. Both MS and BMS algorithms have been applied to a variety of domains, and several variations around their original formulation have been proposed: see [7] for BMS with a Gaussian kernel (known as Gaussian blurring mean-shift); for BMS applied to high-dimensional data clustering see [8].

Campaigners say a vessel, part of a flotilla carrying aid to Gaza, has been struck in a suspected drone attack. It's the second such suspected attack in two days. Swedish campaigner Greta Thunberg is amongst the activists travelling to Gaza with the flotilla to try and break Israel's naval blockade. BBC Verify has been analysing footage of the incident and has spoken to two weapons experts who say a device found on board after the attack appears to be a grenade. 'I witnessed war crimes' in Gaza, former worker at GHF aid site tells BBC A retired US soldier reveals why he quit working at Israel and US-backed Gaza Humanitarian Foundation aid hubs.

Accurate estimation of total leaf area (TLA) is essential for assessing plant growth, photosynthetic activity, and transpiration but remains a challenge for bushy plants like dwarf tomatoes. Traditional destructive methods and imaging-based techniques often fall short due to labor intensity, plant damage, or the inability to capture complex canopies. This study evaluated a non-destructive method combining sequential 3D reconstructions from RGB images and machine learning to estimate TLA for three dwarf tomato cultivars-- Mohamed, Hahms Gelbe Topftomate, and Red Robin--grown under controlled greenhouse conditions. Two experiments, conducted in spring-summer and autumn-winter, included 73 plants, yielding 418 TLA measurements using an "onion" approach, where layers of leaves were sequentially removed and scanned. High-resolution videos were recorded from multiple angles for each plant, and 500 frames were extracted per plant for 3D reconstruction. Point clouds were created and processed, four reconstruction algorithms (Alpha Shape, Marching Cubes, Poisson's, and Ball Pivoting) were tested, and meshes were evaluated using seven regression models: Multivariable Linear Regression (MLR), Lasso Regression (Lasso), Ridge Regression (Ridge-Reg), Elastic Net Regression (ENR), Random Forest (RF), extreme gradient boosting (XGBoost), and Multilayer Perceptron (MLP). The Alpha Shape reconstruction (α = 3) combined with XGBoost yielded the best performance, achieving an R of 0.80 and MAE of 489 cm These findings demonstrate the robustness of our approach across variable environmental conditions and canopy structures. This scalable, automated TLA estimation method is particularly suited for urban farming and precision agriculture, offering practical implications for automated pruning, improved resource efficiency, and sustainable food production. Keywords: Total leaf area, dwarf tomato, point cloud, mesh reconstruction, machine learning, precision agriculture 1. Introduction Total leaf area (TLA) is a comprehensive metric describing the plant's growth and functioning. It is a primary metric that describes the plant's photosynthetic activity and transpiration capacity. Normalized by the plant's surface area, TLA may provide information on the canopy structure, which is crucial for understanding the plant's energy and resource efficiency. For example, reduced TLA is a sign of stress (Dong et al., 2019), while excessive biomass, indicated by a higher TLA, signifies lower water use efficiency (Glenn et al., 2006). Farmers often use pruning to reduce TLA in commercial crops to increase crop productivity (Budiarto et al., 2023). However, measuring and finding the optimum TLA of the crop are challenging tasks.

In the early morning hours of May 15, the cargo vessel Borkum stopped off the Spanish coast, lingering in the waters a short distance from Cartagena. At the port, protesters waved Palestinian flags and called on authorities to inspect the ship based on suspicions that it carried weapons bound for Israel. Leftist members of the European Parliament sent a letter to Spanish President Pedro Sánchez requesting that the ship be prevented from docking. "Allowing a ship loaded with weapons destined for Israel is to allow the transit of arms to a country currently under investigation for genocide against the Palestinian people," the group of nine MEPs warned. Before the Spanish government could take a stand, the Borkum cancelled its planned stopover and continued to the Slovenian port of Koper.

Israel's military campaign in Gaza has brought "relentless death and destruction" to Palestinians in the strip, United Nations Secretary-General Antonio Guterres has said. In a speech marking six months since Israel's war on Gaza began, the UN chief said that "nothing can justify the collective punishment of the Palestinian people." Respect for international humanitarian law is in tatters," he added. "During my visit to the Rafah crossing 10 days ago, I met veteran humanitarians who told me categorically that the crisis and suffering in Gaza is unlike any they have ever seen," Guterres said, adding that long lines of trucks with aid continued to face "obstacle after obstacle". "When the gates to aid are closed, the doors to starvation are opened," he said. "More than half the population – over a million people – are facing catastrophic hunger.

The goal of this work is to develop a robot equipped with goal-driven explainability, i.e. a robot will explain its behavior to achieve a particular goal in a collaborative setting. The major factor in goal-driven explainability is the human'theory of mind'. In this work, we will employ Leslies' theory of mind model which includes a mechanical agency, an actionable agency and a belief agency. This thesis will focus on explaining the desire of the robot and the belief of the human if its different to the robot's intention or desire. We aim to develop a common theoretical framework for the development of understandable robots which will include learning to generate explanations, non-verbal and verbal ways of communication and explanations in context.

A deep neural network (DNN) is trained to estimate the speed of a car driving in an urban area using as input a stream of measurements from a low-cost six-axis inertial measurement unit (IMU). Three hours of data was collected by driving through the city of Ashdod, Israel in a car equipped with a global navigation satellite system (GNSS) real time kinematic (RTK) positioning device and a synchronized IMU. Ground truth labels for the car speed were calculated using the position measurements obtained at the high rate of 50 Hz. A DNN architecture with long short-term memory layers is proposed to enable high-frequency speed estimation that accounts for previous inputs history and the nonlinear relation between speed, acceleration and angular velocity. A simplified aided dead reckoning localization scheme is formulated to assess the trained model which provides the speed pseudo-measurement. The trained model is shown to substantially improve the position accuracy during a 4 minutes drive without the use of GNSS position updates.

The programme, developed by a company called SightBit, uses information collected from surveillance cameras to determine who is in the water -- an adult or child, for example -- if they are moving or limp, and the current's movement at that location. If a threat is determined, the programme sends an alert to a tablet held by the user -- a lifeguard, in this case -- with urgent instructions to act. SightBit's chief executive Netanel Eliav told AFP that he developed the technology after identifying a shortfall in how closed-circuit footage was being applied to boost safety in the water. The programme has been in use for more than a year in Ashdod, a city on Israel's Mediterranean coast that chose to deploy SightBit technology in an area at a distance from the nearest lifeguard. "We chose to locate the technology in areas away from the lifeguard towers, so the additional'eyes' there help the lifeguards very much," said Arie Turjeman, director of Ashdod's coast division.

This technical note considers the problems of blind sparse learning and inference of electrogram (EGM) signals under atrial fibrillation (AF) conditions. First of all we introduce a mathematical model for the observed signals that takes into account the multiple foci typically appearing inside the heart during AF. Then we propose a reconstruction model based on a fixed dictionary and discuss several alternatives for choosing the dictionary. In order to obtain a sparse solution that takes into account the biological restrictions of the problem, a first alternative is using LASSO regularization followed by a post-processing stage that removes low amplitude coefficients violating the refractory period characteristic of cardiac cells. As an alternative we propose a novel regularization term, called cross products LASSO (CP-LASSO), that is able to incorporate the biological constraints directly into the optimization problem. Unfortunately, the resulting problem is non-convex, but we show how it can be solved efficiently in an approximated way making use of successive convex approximations (SCA). Finally, spectral analysis is performed on the clean activation sequence obtained from the sparse learning stage in order to estimate the number of latent foci and their frequencies. Simulations on synthetic and real data are provided to validate the proposed approach.