For some companies attending London Tech Week this Monday, just being there is an achievement. The sudden failure in March of Silicon Valley Bank (SVB), a financial cornerstone for the UK and US tech industries, had left many British companies wondering how they were going to see out that month. Ashley Ramrachia, chief executive of Academy, a tech company with headquarters in Manchester, said the first he knew of SVB's troubles was on Wednesday 8 March. By Thursday, Ramrachia and others were trying, unsuccessfully, to withdraw funds. By Friday, the Bank of England said it planned to put SVB's UK operation into insolvency and Ramrachia was one of 3,500 customers in Britain scrambling to deal with the consequences.

China should play a key role in shaping the artificial intelligence guardrails needed to ensure the safety of transformative new systems, OpenAI Inc.'s Chief Executive Officer Sam Altman said. "With the emergence of the increasingly powerful AI systems, the stakes for global cooperation have never been higher," Altman, whose company kick-started an AI frenzy in China with last year's launch of ChatGPT, told a Beijing conference via video link on Saturday. In both China and Silicon Valley, talent and investments are flowing into AI, a strategic area that will help define the deepening tech rivalry between the world's two largest economies. Advances in the emerging technology have also highlighted tensions in how governments are seeking to regulate the sector, one that Chinese leader Xi Jinping has said requires greater state oversight to mitigate national security risks. This could be due to a conflict with your ad-blocking or security software.

In federated learning (FL), multiple clients collaborate to train machine learning models together while keeping their data decentralized. Through utilizing more training data, FL suffers from the potential negative transfer problem: the global FL model may even perform worse than the models trained with local data only. In this paper, we propose FedCollab, a novel FL framework that alleviates negative transfer by clustering clients into non-overlapping coalitions based on their distribution distances and data quantities. As a result, each client only collaborates with the clients having similar data distributions, and tends to collaborate with more clients when it has less data. We evaluate our framework with a variety of datasets, models, and types of non-IIDness. Our results demonstrate that FedCollab effectively mitigates negative transfer across a wide range of FL algorithms and consistently outperforms other clustered FL algorithms.

We propose the deep demixing (DDmix) model, a graph autoencoder that can reconstruct epidemics evolving over networks from partial or aggregated temporal information. Assuming knowledge of the network topology but not of the epidemic model, our goal is to estimate the complete propagation path of a disease spread. A data-driven approach is leveraged to overcome the lack of model awareness. To solve this inverse problem, DDmix is proposed as a graph conditional variational autoencoder that is trained from past epidemic spreads. DDmix seeks to capture key aspects of the underlying (unknown) spreading dynamics in its latent space. Using epidemic spreads simulated in synthetic and real-world networks, we demonstrate the accuracy of DDmix by comparing it with multiple (non-graph-aware) learning algorithms. The generalizability of DDmix is highlighted across different types of networks. Finally, we showcase that a simple post-processing extension of our proposed method can help identify super-spreaders in the reconstructed propagation path.

Falls among the elderly are a major health concern, frequently resulting in serious injuries and a reduced quality of life. In this paper, we propose "BlockTheFall," a wearable device-based fall detection framework which detects falls in real time by using sensor data from wearable devices. To accurately identify patterns and detect falls, the collected sensor data is analyzed using machine learning algorithms. To ensure data integrity and security, the framework stores and verifies fall event data using blockchain technology. The proposed framework aims to provide an efficient and dependable solution for fall detection with improved emergency response, and elderly individuals' overall well-being. Further experiments and evaluations are being carried out to validate the effectiveness and feasibility of the proposed framework, which has shown promising results in distinguishing genuine falls from simulated falls. By providing timely and accurate fall detection and response, this framework has the potential to substantially boost the quality of elderly care.

There has been significant work recently in developing machine learning (ML) models in high energy physics (HEP) for tasks such as classification, simulation, and anomaly detection. Often these models are adapted from those designed for datasets in computer vision or natural language processing, which lack inductive biases suited to HEP data, such as equivariance to its inherent symmetries. Such biases have been shown to make models more performant and interpretable, and reduce the amount of training data needed. To that end, we develop the Lorentz group autoencoder (LGAE), an autoencoder model equivariant with respect to the proper, orthochronous Lorentz group $\mathrm{SO}^+(3,1)$, with a latent space living in the representations of the group. We present our architecture and several experimental results on jets at the LHC and find it outperforms graph and convolutional neural network baseline models on several compression, reconstruction, and anomaly detection metrics. We also demonstrate the advantage of such an equivariant model in analyzing the latent space of the autoencoder, which can improve the explainability of potential anomalies discovered by such ML models.

FOX Weather meteorologist Marissa Torres has the latest on the impact from Canadian wildfires on the Northeast's air quality and visibility on'Your World.' As wildfire smoke from northeastern Canada continues to drift to numerous cities in the United States, precautious Americans are actively searching for air purifiers that they can use for current and future air quality alerts. On Google, the term "best air purifier" has been highly searched in Washington, D.C., New York City, Baltimore, Philadelphia, Hartford and New Haven, and other major metros in the northeast since Wednesday, June 7, according to data collected by Google Trends, a search engine analytics platform. The U.S. Environmental Protection Agency issued a "poor air quality alert" for parts of New England (Connecticut, Massachusetts, and Rhode Island) on Wednesday, which was followed by similar announcements made by various city-level environmental and health agencies. "Wildfire smoke can rise more than 10 miles in the air and be carried hundreds of miles by wind currents," Dr.

The United States has accused the Iranian government of helping Russia to build a drone manufacturing plant near Moscow, in an escalation of their defence cooperation. In a statement on Friday, White House National Security Council spokesman John Kirby cited US intelligence findings that indicated Iran had provided material support for the plant, which could be operational by early next year. US officials also double-downed on claims that Iran has sent hundreds of drones -- or unmanned aerial vehicles (UAVs) -- to Russia for use in Ukraine, where a full-scale invasion was launched in 2022. "Russia has been using Iranian UAVs in recent weeks to strike Kyiv and terrorize the Ukrainian population, and the Russia-Iran military partnership appears to be deepening," Kirby said in Friday's statement. "We are also concerned that Russia is working with Iran to produce Iranian UAVs from inside Russia."

Fox News chief national security correspondent Jennifer Griffin has the latest on Iran's claims of developing an advanced hypersonic missile on'Special Report.' United States officials believe Iran is sending Russia materials to build a drone manufacturing plant east of Moscow to produce more Iranian drones to use in Ukraine. The intelligence was made public by the National Security Council's Coordinator for Strategic Communications John Kirby on Friday. "As of May, Russia received hundreds of one-way attack [unmanned aerial vehicles], as well as UAV production-related equipment, from Iran," Kirby said. Russian President Vladimir Putin takes part in the ceremony of signing an agreement on the construction of the Rasht-Astara railway via a video link together with Iranian President Ebrahim Raisi, at the Kremlin in Moscow.

The military partnership between Moscow and Tehran is deepening, White House officials said on Friday as they released newly declassified information about a drone factory that Iran is helping Russia build. Russia has repeatedly used Iranian-made drones to attack Ukraine in recent months, including strikes on civilian targets, buildings and electrical infrastructure as part of a push to break Ukrainian morale. And as Moscow's own weapons stocks have diminished, Iran has become a key supplier of military aid to Russia. The new factory, which is planned for a warehouse in the Yelabuga region several hundred miles east of Moscow, would allow Russia's military to have its own domestically produced source of attack drones. Iran is providing materials for the plant, said John Kirby, the National Security Council spokesman, who added that the facility could be operational next year.