In this paper, we propose a new method for knowledge base completion (KBC): instance-based learning (IBL). For example, to answer (Jill Biden, lived city,?

Fox News' Jacqui Heinrich reports the latest on Ukraine peace talks ahead of President Donald Trump's White House meeting. National security analyst Rebeccah Heinrichs also weighs in on the latest peace negotiations during'America's Newsroom.' Just as Ukrainian President Volodymyr Zelenskyy arrived in Washington, D.C. to meet President Donald Trump at the White House, Russia routed his nation with airstrikes on Monday, killing 10. Seven people, including a toddler and a 16-year-old, were killed by a Russian drone strike on the Ukrainian city of Kharkiv, according to local authorities. Ukrainian officials took the strikes as a message from Russian President Vladimir Putin that he has no intention to end the war.

In this work, we combine innovations from these areas to tackle the problem of N-k security-constrained optimal power flow (SCOPF).

In today's episode, our host Zöe Schiffer is joined by WIRED's senior politics writer Jake Lahut to run through five of the best stories we published this week--from how the Trump administration is creating and sharing memes to make fun of deportations, to NASA's ambitious goal to put nuclear reactors on the moon. Then, Zöe and Jake dive into why users kind of hated OpenAI's GPT-5 release. Mentioned in this episode: OpenAI Scrambles to Update GPT-5 After Users Revolt by Will Knight The Trump Administration Is Using Memes to Turn Mass Deportation Into One Big Joke by Tess Owen Trump Family–Backed World Liberty Financial Sets Up 1.5 Billion Crypto Treasury by Joel Khalili Inside the'Whites Only' Community in Arkansas by David Gilbert Why the US Is Racing to Build a Nuclear Reactor on the Moon by Becky Ferreira Join us live in San Francisco on September 9th. Write to us at uncannyvalley@wired.com. You can always listen to this week's podcast through the audio player on this page, but if you want to subscribe for free to get every episode, here's how: If you're on an iPhone or iPad, open the app called Podcasts, or just tap this link.

As governments and corporations adopt deep learning systems to collect and analyze user-generated audio data, concerns about security and privacy naturally emerge in areas such as automatic speaker recognition. While audio adversarial examples offer one route to mislead or evade these invasive systems, they are typically crafted through time-intensive offline optimization, limiting their usefulness in streaming contexts. Inspired by architectures for audio-to-audio tasks such as denoising and speech enhancement, we propose a neural network model capable of adversarially modifying a user's audio stream in real-time. Our model learns to apply a time-varying finite impulse response (FIR) filter to outgoing audio, allowing for effective and inconspicuous perturbations on a small fixed delay suitable for streaming tasks. We demonstrate our model is highly effective at de-identifying user speech from speaker recognition and able to transfer to an unseen recognition system. We conduct a perceptual study and find that our method produces perturbations significantly less perceptible than baseline anonymization methods, when controlling for effectiveness. Finally, we provide an implementation of our model capable of running in real-time on a single CPU thread.

Russian attacks on major Ukrainian cities have killed at least 12 people as President Volodymyr Zelenskyy visits Washington, DC, supported by European leaders, for high-stakes peace talks with United States President Donald Trump that could determine Ukraine's future and its fate in the war, now in its fourth year. An entire family, including a toddler and a 16-year-old, were among seven people killed in an overnight drone strike on a residential neighbourhood in the northeastern city of Kharkiv, authorities said on Monday. The attack also injured 20 people, including six children. Russian forces killed five people and injured four in attacks in eastern Ukraine's Donetsk region, where some of the fiercest fighting on the ground rages on and where Russian President Vladimir Putin, feeling Moscow has the upper hand, seeks Ukraine's withdrawal from the third of the region Kyiv still controls. In Zaporizhzhia, a city in the southeast, 17 people were injured in an attack, according to Governor Ivan Fedorov.

Bayesian optimisation (BO) algorithms have shown remarkable success in applications involving expensive black-box functions. Traditionally BO has been set as a sequential decision-making process which estimates the utility of query points via an acquisition function and a prior over functions, such as a Gaussian process. Recently, however, a reformulation of BO via density-ratio estimation (BORE) allowed reinterpreting the acquisition function as a probabilistic binary classifier, removing the need for an explicit prior over functions and increasing scalability. In this paper, we present a theoretical analysis of BORE's regret and