The tall container was almost certainly used for distillation experiments. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The ceramic container is over 1.5 feet tall. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

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Israel has carried out strikes across southern Lebanon, despite a warning from Iran not to continue attacks in the country. The Lebanese health ministry said eight people were killed in Tyre, where the Israeli military issued a new order for residents to leave the southern city, including its Christian quarter for the first time. Israel and Iran paused hostilities on Monday, after an Israeli strike on Beirut targeting the Iranian-backed armed group Hezbollah triggered their first exchange of fire since a truce in April. Iran warned that it could hit Israel again if it did not stop attacks in Lebanon. But Israel vowed to continue its campaign against Hezbollah.

Nasa has named the Artemis III crew - what is their mission? Nasa has named the four astronauts for its Artemis III mission, due to launch in 2027. They will not land on the Moon, but will fly to low Earth orbit where they will dock with prototype lunar landers in a rehearsal for the landing to come. The 2028 Artemis IV mission is scheduled to put American astronauts on the Moon for the first time since 1972. What will the Artemis III mission do?

Apple has announced the latest version of macOS. It's all about the reintroduction of Siri, which is now accessible from anywhere on the Mac desktop. The official name of the Mac's operating system is macOS 27 Golden Gate, keeping the California naming scheme around. This year's update is focused on the relaunched Siri (now known as Siri AI), which really strives to transform into a proper AI chatbot along the lines of ChatGPT or Google Gemini--with a unique Apple twist. Is Your Mac Compatible With macOS Golden Gate?

Recent progress in text-video retrieval has been largely driven by contrastive learning. However, existing methods often overlook the effect of the modality gap, which causes anchor representations to undergo in-place optimization (i.e., optimization tension) that limits their alignment capacity. Moreover, noisy hard negatives further distort the semantics of anchors. To address these issues, we propose GARE, a Gap-Aware Retrieval framework that introduces a learnable, pair-specific increment $\Delta_{ij}$ between text $t_i$ and video $v_j$, redistributing gradients to relieve optimization tension and absorb noise. We derive $\Delta_{ij}$ via a multivariate first-order Taylor expansion of the InfoNCE loss under a trust-region constraint, showing that it guides updates along locally consistent descent directions. A lightweight neural module conditioned on the semantic gap couples increments across batches for structure-aware correction. Furthermore, we regularize $\Delta$ through a variational information bottleneck with relaxed compression, enhancing stability and semantic consistency. Experiments on four benchmarks demonstrate that GARE consistently improves alignment accuracy and robustness, validating the effectiveness of gap-aware tension mitigation.

Selective prediction [Dru13, QV19] models the scenario where a forecaster freely decides on the prediction window that their forecast spans. Many data statistics can be predicted to a non-trivial error rate distributional assumptions or expert advice, yet these results rely on that the forecaster may predict at any time. We introduce a model of Prediction with Limited Selectivity (PLS) where the forecaster can start the prediction only on a subset of the time horizon. We study the optimal prediction error both on an instance-by-instance basis and via an average-case analysis. We introduce a complexity measure that gives instance-dependent bounds on the optimal error. For a randomly-generated PLS instance, these bounds match with high probability.

Recent advances in reinforcement learning (RL) have strengthened the reasoning capabilities of vision-language models (VLMs). However, enhancing policy exploration to better scale test-time compute remains largely underexplored. In addition, VLMs continue to struggle with imperfect visual perception, which in turn affects the subsequent reasoning process. To this end, we propose **NoisyRollout**, a simple yet effective data augmentation method that mixes trajectories from both clean and moderately distorted images during RL training. By injecting targeted diversity in visual perception and the resulting reasoning patterns, NoisyRollout promotes better policy exploration through vision-oriented inductive biases, ultimately leading to more robust reasoning behaviors. We further adopt a noise annealing schedule that gradually reduces distortion strength over training, leveraging noisy signals early on while ensuring training stability in later stages. Crucially, our method is easy-to-adopt--**requiring no additional training cost and no modifications to the RL objective**. Extensive experiments on $2$ distinct training datasets demonstrate that NoisyRollout achieves state-of-the-art performance among open-source RL-tuned models across $5$ out-of-domain reasoning and perception benchmarks.

Visual Place Recognition (VPR) enables robust localization through image retrieval based on learned descriptors. However, drastic appearance variations of images at the same place caused by viewpoint changes can lead to inconsistent supervision signals, thereby degrading descriptor learning. Existing methods either rely on manually defined cropping rules or labeled data for view differentiation, but they suffer from two major limitations: (1) reliance on labels or handcrafted rules restricts generalization capability; (2) even within the same view direction, occlusions can introduce feature ambiguity. To address these issues, we propose MutualVPR, a mutual learning framework that integrates unsupervised view self-classification and descriptor learning. We first group images by geographic coordinates, then iteratively refine the clusters using K-means to dynamically assign place categories without manual labeling. Specifically, we adopt a DINOv2-based encoder to initialize the clustering.