Pro-Iranian forces in Syria have said they have a "long arm" to respond to further United States air strikes on their positions, after tit-for-tat missile and drone attacks in Syria over the last 24 hours. The online statement, released late on Friday and signed by the Iranian Advisory Committee in Syria, said US air strikes had left several of their fighters dead and wounded, without specifying the fighters' nationality. "We have the capability to respond if our centres and forces in Syria are targeted," the statement said. On Friday night, two Syrian opposition activist groups reported a new wave of US air attacks on eastern Syria, which hit positions of Iran-backed militias, after rockets were fired at bases in Syria housing US troops. Several US officials, however, denied that attacks were launched late on Friday.

WASHINGTON – The conflict in northeast Syria escalated Friday as Iran-backed militias launched a volley of rocket and drone attacks against coalition bases after American reprisals for a drone attack that killed a U.S. contractor and injured six other Americans. President Joe Biden, speaking at a news conference in Canada, sought to tamp down fears that tit-for-tat strikes between the United States and militant groups could spiral out of control, while at the same time warning Iran to rein in its proxies. "Make no mistake, the United States does not, does not, I emphasize, seek conflict with Iran," Biden said in Ottawa, Ontario, where he was making a state visit. "But be prepared for us to act forcefully to protect our people. That's exactly what happened last night."

The rise of cyber threats on critical infrastructure and its potential for devastating consequences, has significantly increased. The dependency of new power grid technology on information, data analytic and communication systems make the entire electricity network vulnerable to cyber threats. Power transformers play a critical role within the power grid and are now commonly enhanced through factory add-ons or intelligent monitoring systems added later to improve the condition monitoring of critical and long lead time assets such as transformers. However, the increased connectivity of those power transformers opens the door to more cyber attacks. Therefore, the need to detect and prevent cyber threats is becoming critical. The first step towards that would be a deeper understanding of the potential cyber-attacks landscape against power transformers. Much of the existing literature pays attention to smart equipment within electricity distribution networks, and most methods proposed are based on model-based detection algorithms. Moreover, only a few of these works address the security vulnerabilities of power elements, especially transformers within the transmission network. To the best of our knowledge, there is no study in the literature that systematically investigate the cybersecurity challenges against the newly emerged smart transformers. This paper addresses this shortcoming by exploring the vulnerabilities and the attack vectors of power transformers within electricity networks, the possible attack scenarios and the risks associated with these attacks.

Recurrent neural networks are used to forecast time series in finance, climate, language, and from many other domains. Reservoir computers are a particularly easily trainable form of recurrent neural network. Recently, a "next-generation" reservoir computer was introduced in which the memory trace involves only a finite number of previous symbols. We explore the inherent limitations of finite-past memory traces in this intriguing proposal. A lower bound from Fano's inequality shows that, on highly non-Markovian processes generated by large probabilistic state machines, next-generation reservoir computers with reasonably long memory traces have an error probability that is at least ~ 60% higher than the minimal attainable error probability in predicting the next observation. More generally, it appears that popular recurrent neural networks fall far short of optimally predicting such complex processes. These results highlight the need for a new generation of optimized recurrent neural network architectures. Alongside this finding, we present concentration-of-measure results for randomly-generated but complex processes. One conclusion is that large probabilistic state machines -- specifically, large $\epsilon$-machines -- are key to generating challenging and structurally-unbiased stimuli for ground-truthing recurrent neural network architectures.

Conditional neural processes (CNPs; Garnelo et al., 2018a) are attractive meta-learning models which produce well-calibrated predictions and are trainable via a simple maximum likelihood procedure. Although CNPs have many advantages, they are unable to model dependencies in their predictions. Various works propose solutions to this, but these come at the cost of either requiring approximate inference or being limited to Gaussian predictions. In this work, we instead propose to change how CNPs are deployed at test time, without any modifications to the model or training procedure. Instead of making predictions independently for every target point, we autoregressively define a joint predictive distribution using the chain rule of probability, taking inspiration from the neural autoregressive density estimator (NADE) literature. We show that this simple procedure allows factorised Gaussian CNPs to model highly dependent, non-Gaussian predictive distributions. Perhaps surprisingly, in an extensive range of tasks with synthetic and real data, we show that CNPs in autoregressive (AR) mode not only significantly outperform non-AR CNPs, but are also competitive with more sophisticated models that are significantly more computationally expensive and challenging to train. This performance is remarkable given that AR CNPs are not trained to model joint dependencies. Our work provides an example of how ideas from neural distribution estimation can benefit neural processes, and motivates research into the AR deployment of other neural process models.

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Fox News chief national security correspondent Jennifer Griffin has the latest on the attack and retaliatory measures on'The Story.' The main air defense system at a coalition military base in Northeast Syria was not working Thursday when one American contractor was killed after a suspected Iranian drone hit the base and injured six other servicemen, a senior U.S. defense official told Fox News Friday. U.S. intelligence has assessed that the drone that struck the base was Iranian. The injured U.S. service members are in "stable" condition and have been transported to a hospital in Landstuhl, Germany the senior official added. In testimony on the Hill Thursday, Gen. Erik Kurilla said in that Iranian-backed forces have been behind 78 attacks on U.S. bases in Iraq and Syria since January 2021.

Nikki Haley, presidential candidate and former U.S. ambassador to the U.N., weighs in after President Biden authorized an air strike in response to an Iranian drone that killed an American. Republican presidential candidate Nikki Haley called on the Biden administration to get tough on a slew of foreign adversaries or risk war after an American citizen was killed in an Iranian drone strike in Syria. "It shows what happens when there's American weakness," Haley said Friday of the attack on "America's Newsroom." "Whether it's in Afghanistan, whether you see it in Ukraine, whether you see it on the southern border, you're going to continue to see more of these things happen." "There is no deterrence," she continued.

On Monday, as Americans considered the possibility of a Donald Trump indictment and a presidential perp walk, Eliot Higgins brought the hypothetical to life. Higgins, the founder of Bellingcat, an open-source investigations group, asked the latest version of the generative-AI art tool Midjourney to illustrate the spectacle of a Trump arrest. It pumped out vivid photos of a sea of police officers dragging the 45th president to the ground. He generated a series of images that became more and more absurd: Donald Trump Jr. and Melania Trump screaming at a throng of arresting officers; Trump weeping in the courtroom, pumping iron with his fellow prisoners, mopping a jailhouse latrine, and eventually breaking out of prison through a sewer on a rainy evening. The story, which Higgins tweeted over the course of two days, ends with Trump crying at a McDonald's in his orange jumpsuit. All of the tweets are compelling, but only the scene of Trump's arrest went mega viral, garnering 5.7 million views as of this morning.

Nikki Haley, presidential candidate and former U.S. ambassador to the U.N., weighs in after President Biden authorized an air strike in response to an Iranian drone that killed an American. The U.S. can no longer take a reactive stance toward Iran after a new Pentagon report revised the total number of troops killed by Iran-backed groups continues to rise, experts told Fox News Digital. "Iran's regional strategy of working through proxies and carve outs is continuing unabated," Behnam Ben Taleblu, a senior fellow and Iran expert at the Foundation for Defense of Democracies, said. "The open question is, when will the Biden administration ditch tit-for-tat strikes and work to rollback Iran's Shiite militia network in the heartland of the Middle East?" President Biden ordered a series of retaliatory precision airstrikes in Syria on Thursday, reportedly killing eight Iranians, after Iran's Islamic Revolutionary Guards Corps crashed a UAV into a building, killing a U.S. contractor and wounding six other Americans. U.S. intelligence assessed the UAV that crashed into a coalition base, which killed the contractor, was of Iranian origin -- so President Biden authorized the military to retaliate, the Pentagon said.