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.

Multimodal large language models have fueled progress in image captioning. These models, fine-tuned on vast image datasets, exhibit a deep understanding of semantic concepts.In this work, we show that this ability can be re-purposed for audio captioning, where the joint image-language decoder can be leveraged to describe auditory content associated with image sequences within videos featuring audiovisual content. This can be achieved via multimodal alignment.Yet, this multimodal alignment task is non-trivial due to the inherent disparity between audible and visible elements in real-world videos. Moreover, multimodal representation learning often relies on contrastive learning, facing the challenge of the so-called modality gap which hinders smooth integration between modalities. In this work, we introduce a novel methodology for bridging the audiovisual modality gap by matching the distributions of tokens produced by an audio backbone and those of an image captioner. Our approach aligns the audio token distribution with that of the image tokens, enabling the model to perform zero-shot audio captioning in an unsupervised fashion. This alignment allows for the use of either audio or audiovisual input by combining or substituting the image encoder with the aligned audio encoder. Our method achieves significantly improved performances in zero-shot audio captioning, compared to existing approaches.

A.1 Difference between the performance of two joint policies In Section 3.1, the difference between the performance of two joint policies is expressed as follows: The proof is a multi-agent version of the proof in (Kakade and Langford, 2002). Now we provide the mathematical detail formally. A.2 Approximation that matches the true value to first order In Section 3.1, we claim that Jπ( π) matches J( π) to first order. Intuitively, this means that a sufficiently small update of the joint policy which improves Jπ( π) will also improve J( π). Now we prove it formally.

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.