Yemen's Houthi group has claimed responsibility for the drone that struck overnight in Tel Aviv, Israel, killing one person and injuring eight. Israeli media identified the dead man as 50-year-old Yevgeny Ferder, who had moved to Israel from Belarus at the beginning of the Russia-Ukraine war. Last night's strike is unique -- it's the first time the group is known to have hit Tel Aviv, though the Houthi have waged a continued campaign against targets they claim are linked to Israel since the ongoing devastating war on Gaza broke out in October. The drone struck in central Tel Aviv in the early hours of Friday morning. The site itself is thought to be close to a number of hotels, many hosting those displaced from Israel's northern border with Lebanon. A US embassy office is also close to the site of the attack.

The Iran-backed Houthi militia claimed responsibility for a rare drone attack in central Tel Aviv that crashed into a building near the United States Embassy branch office early Friday, killing at least one person and wounding eight others. Rear Adm. Daniel Hagari, the Israeli military spokesman, told reporters that Israel's defense systems had apparently picked up the drone but failed to register it as a threat. No air-raid sirens were activated to warn civilians of the attack, despite Israel's extensive aerial defense system. "We are investigating why we did not identify it, attack it and intercept it," Admiral Hagari said. The Israeli military said the drone had likely flown from Yemen, where the Houthis are based, before approaching Tel Aviv from the coast.

Microsoft's global outage has hit vital NHS services, with the medical computer system EMIS not working. The EMIS system is used by GPs to book appointments, view patient notes, order prescriptions and make referrals. However doctors in parts of the UK are currently reporting having a '100 per cent outage' with patients also telling MailOnline they can't get life-saving drugs. Speaking to this website a GP practice manager in Berkshire said: 'We are completely dead in the water. 'We can't see any patients our systems are down.

First responders are on scene in Tel Aviv after a large explosion rocked the city in the middle of the night. The blast happened approximately one block from a U.S. embassy branch office. An explosion that rocked Tel Aviv overnight Thursday has left one person dead and several others wounded. Military officials say they believe the source of the explosion was a deadly drone attack, and Yemen's Houthi rebels have already claimed responsibility for a drone strike in the area near the U.S. embassy, the Associated Press reported. The drone was not intercepted despite it being identified prior to the explosion due to human error.

Russian attacks on Ukraine's front-line Donetsk region have killed five civilians and injured three, damaging private houses and a residential building, the Reuters news agency reported, citing prosecutors. A couple was killed by artillery shelling in the village of Pleshchiivka, while three women were killed in a strike in the village of Hrodivka. The Russian military also dropped two guided bombs on the village of Velyka Novosilka in Donetsk, injuring a man and his wife inside their house, authorities in eastern Ukraine said. Debris from a downed Ukrainian drone wounded two people in the Russian city of Kursk, the acting regional governor, Alexei Smirnov, said on the Telegram messaging app. Russia's Ministry of Defence said 19 drones had been destroyed overnight, including 11 over the Kursk region, the Interfax news agency reported.

Yemen's Houthi fighters have claimed responsibility following a suspected drone attack on Israel's Tel Aviv, which killed one person and injured at least 10, according to reports. A spokesperson for the Houthi armed forces said in a post on social media on Friday that the Yemen-based group had "targeted'Tel Aviv' in occupied Palestine". The Israeli military said it had opened an investigation into the large explosion near the United States Embassy office in the city and would determine why the country's air defence systems were not activated to intercept the "aerial target". Israel's air force has increased patrols to "protect the country's skies", the military added in a post on social media. Israeli police said the body of a man was found in an apartment close to the explosion and that the circumstances were being investigated.

The Israeli military says it is investigating an apparent drone attack that hit central Tel Aviv in the early hours of Friday. In a statement it said an initial inquiry indicated the explosion had been caused by the falling of an "aerial target" and announced it was increasing air patrols. Israeli emergency services say the explosion left one person dead and several lightly injured. Yemen's Houthi militants, which are backed by Iran, announced on social media that they would reveal details about a military operation that had targeted Tel Aviv. The incident also came after the Israeli military confirmed it had killed a senior commander of the Hezbollah militia in southern Lebanon.

Given the magnitude of data generation currently, both in quantity and speed, the use of machine learning is increasingly important. When data include protected features that might give rise to discrimination, special care must be taken. Data quality is critical in these cases, as biases in training data can be reflected in classification models. This has devastating consequences and fails to comply with current regulations. Data-Centric Artificial Intelligence proposes dataset modifications to improve its quality. Instance selection via undersampling can foster balanced learning of classes and protected feature values in the classifier. When such undersampling is done close to the decision boundary, the effect on the classifier would be bolstered. This work proposes Fair Overlap Number of Balls (Fair-ONB), an undersampling method that harnesses the data morphology of the different data groups (obtained from the combination of classes and protected feature values) to perform guided undersampling in the areas where they overlap. It employs attributes of the ball coverage of the groups, such as the radius, number of covered instances and density, to select the most suitable areas for undersampling and reduce bias. Results show that the Fair-ONB method reduces bias with low impact on the classifier's predictive performance.

Spectral Graph Neural Networks have demonstrated superior performance in graph representation learning. However, many current methods focus on employing shared polynomial coefficients for all nodes, i.e., learning node-unified filters, which limits the filters' flexibility for node-level tasks. The recent DSF attempts to overcome this limitation by learning node-wise coefficients based on positional encoding. However, the initialization and updating process of the positional encoding are burdensome, hindering scalability on large-scale graphs. In this work, we propose a scalable node-wise filter, PolyAttn. Leveraging the attention mechanism, PolyAttn can directly learn node-wise filters in an efficient manner, offering powerful representation capabilities. Building on PolyAttn, we introduce the whole model, named PolyFormer. In the lens of Graph Transformer models, PolyFormer, which calculates attention scores within nodes, shows great scalability. Moreover, the model captures spectral information, enhancing expressiveness while maintaining efficiency. With these advantages, PolyFormer offers a desirable balance between scalability and expressiveness for node-level tasks. Extensive experiments demonstrate that our proposed methods excel at learning arbitrary node-wise filters, showing superior performance on both homophilic and heterophilic graphs, and handling graphs containing up to 100 million nodes. The code is available at https://github.com/air029/PolyFormer.

As the complexities of Dynamic Data Driven Applications Systems increase, preserving their resilience becomes more challenging. For instance, maintaining power grid resilience is becoming increasingly complicated due to the growing number of stochastic variables (such as renewable outputs) and extreme weather events that add uncertainty to the grid. Current optimization methods have struggled to accommodate this rise in complexity. This has fueled the growing interest in data-driven methods used to operate the grid, leading to more vulnerability to cyberattacks. One such disruption that is commonly discussed is the adversarial disruption, where the intruder attempts to add a small perturbation to input data in order to "manipulate" the system operation. During the last few years, work on adversarial training and disruptions on the power system has gained popularity. In this paper, we will first review these applications, specifically on the most common types of adversarial disruptions: evasion and poisoning disruptions. Through this review, we highlight the gap between poisoning and evasion research when applied to the power grid. This is due to the underlying assumption that model training is secure, leading to evasion disruptions being the primary type of studied disruption. Finally, we will examine the impacts of data poisoning interventions and showcase how they can endanger power grid resilience.