Learning disentangled representations for user intentions from multi-feedback (i.e., positive and negative feedback) can enhance the accuracy and explainability of recommendation algorithms. However, learning such disentangled representations from multi-feedback data is challenging because i) multi-feedback is complex: there exist complex relations among different types of feedback (e.g., click, unclick, and dislike, etc) as well as various user intentions, and ii) multi-feedback is noisy: there exists noisy (useless) information both in features and labels, which may deteriorate the recommendation performance. Existing disentangled recommendation works only focus on positive feedback, failing to handle the complex relations and noise hidden in multi-feedback data. To solve this problem, in this work we propose a Curriculum Disentangled Recommendation (CDR) model that is capable of efficiently learning disentangled representations from complex and noisy multi-feedback for better recommendation.

The proofs that earned EPFL professor Maryna Viazovska the Fields Medal in 2022 have reached a new milestone: their complete formalization by computer, achieved through a collaboration between mathematicians and artificial intelligence tools. In 2016, Maryna Viazovska solved the sphere packing problem in dimension 8, proving that the E lattice constitutes the densest possible arrangement. Shortly after, together with collaborators, she established an analogous result in dimension 24 using the Leech lattice. Her method provided an elegant solution to a problem studied for centuries, with close ties to applied fields such as error-correcting codes. For this major contribution, Viazovska was awarded the Fields Medal in 2022, the highest distinction in mathematics.

Maryland is set to become the first state to ban surveillance pricing in grocery stores after Gov. Wes Moore said he will sign the new law taking effect October 2026.

Nicotine pouches are revered among tech workers, who tout them as the perfect brain-boosting, productivity-jacking stimulants. Entrepreneur Garrett Campbell has a 6-mg "cool mint" Zyn tucked under his lip at all times during his mammoth 15-hour workdays, aside from when he is eating. "I was always very against nicotine," says the software company founder. The 26-year-old saw his peers using nicotine pouches at college, when they first emerged as a potential productivity-boosting hack, and considered it a "degenerate thing to do." But then all of his fellow founders started fueling themselves with nicotine pouches, of which the Philip Morris International-owned Zyn is the market leader.

PCWorld highlights 20 free Windows applications that address common PC frustrations, from poor search functionality to audio control issues. Featured tools include Microsoft PowerToys for system utilities, Voidtools Everything for faster file searching, and EarTrumpet for individual app volume management. These hidden gems can significantly improve PC workflows by offering alternatives to built-in Windows features and solving everyday computing problems. Even if you aren't a huge techie, you probably know about all the big, mainstream apps that are fundamental to how we use our PCs.

When learning from sensitive data, care must be taken to ensure that training algorithms address privacy concerns. The canonical Private Aggregation of Teacher Ensembles, or PATE, computes output labels by aggregating the predictions of a (possibly distributed) collection of teacher models via a voting mechanism. The mechanism adds noise to attain a differential privacy guarantee with respect to the teachers' training data. In this work, we observe that this use of noise, which makes PATE predictions stochastic, enables new forms of leakage of sensitive information. For a given input, our adversary exploits this stochasticity to extract high-fidelity histograms of the votes submitted by the underlying teachers. From these histograms, the adversary can learn sensitive attributes of the input such as race, gender, or age. Although this attack does not directly violate the differential privacy guarantee, it clearly violates privacy norms and expectations, and would not be possible at all without the noise inserted to obtain differential privacy. In fact, counter-intuitively, the attack becomes easier as we add more noise to provide stronger differential privacy. We hope this encourages future work to consider privacy holistically rather than treat differential privacy as a panacea.

A recent attack on Sam Altman's home and OpenAI offices has put corporate security under renewed scrutiny. Records reveal how much some tech firms spend to arm up. Elon Musk called violent crime in San Francisco " horrific " and moved the offices of his social media business X outside the city in 2024 because of safety and business considerations. Other local tech companies have attempted to address their security concerns by partnering directly with cops. Airbnb and Salesforce are among businesses that for years have contracted San Francisco police to protect their offices on a regular basis, according to public records obtained by WIRED.

Recent work has demonstrated the effectiveness of formulating decision making as supervised learning on offline-collected trajectories. Powerful sequence models, such as GPT or BERT, are often employed to encode the trajectories. However, the benefits of performing sequence modeling on trajectory data remain unclear. In this work, we investigate whether sequence modeling has the ability to condense trajectories into useful representations that enhance policy learning. We adopt a two-stage framework that first leverages sequence models to encode trajectory-level representations, and then learns a goal-conditioned policy employing the encoded representations as its input.

What are Russia's gains from the Iran war? 'We are not losers; we are winners' Ukrainian officials say Russian drones have again attacked the southern port city of Odesa, injuring at least 11 people, including two children, and damaging homes and important infrastructure. Odesa Governor Oleh Kiper said the attack affected three districts, hitting residential buildings, vehicles and civilian facilities, including a hotel, warehouses and funicular railway. Windows shattered in many buildings and the port area sustained damage. Law enforcement agencies are documenting the latest war crimes committed by Russia against the peaceful population of [the] Odesa region," Kiper said. Russian attacks killed one person in the southeastern Zaporizhzhia region, according to Governor Ivan Fedorov. "A 59-year-old man died as a result of an enemy attack on the Zaporizhzhia region," Fedorov wrote on Telegram. A Ukrainian drone attack killed an employee at the Zaporizhzhia nuclear power plant, which was captured by Russian forces and is shut down. "A driver was killed today when a Ukrainian Armed Forces drone struck the transport department at the Zaporizhzhia Nuclear Power Plant," said a statement from plant managers who were installed by Russia. Regional governor Fedorov said Russian forces launched 629 strikes across 45 settlements in the region in a single day, with at least 50 reports of damage to homes and infrastructure. Russian officials reported Ukrainian drone attacks in the Belgorod border region, where at least one person was killed and four women injured, alongside damage to buildings and vehicles. The attacks come as diplomatic efforts to end the war remain stalled. Donald Trump said on Sunday that he has had "good conversations" with Presidents Vladimir Putin and Volodymyr Zelenskyy. "We're working on the Russia situation, Russia and Ukraine, and hopefully we're going to get it," Trump said on Fox News. "I do have conversations with him, and I do have conversations with President Zelenskyy, and good conversations," he said. "The hatred between President Putin and President Zelenskyy is ridiculous.