What are Russia's gains from the Iran war? 'We are not losers; we are winners' A Ukrainian married couple, both aged 75, were killed in a Russian attack on Odesa, Ukrainian officials said. Russia launched a series of drone attacks on and near Ukraine's southern port city. The assault destroyed residential buildings and hit a foreign merchant ship, according to Ukrainian authorities. A separate attack killed the married couple and wounded another, reported Ukraine's State Emergency Service. Serhiy Lysak, head of the local military administration, shared images of a building engulfed in flames and another torn open along one side, as emergency crews worked inside.

In this supplementary material, we first explain the details of spectral clustering algorithm in Sec. B. We also analyze the sensitivity of FreeSel to the values of hyperparameters in3 Sec. C. Besides, FreeSel is compared with other intuitive baselines using the general-purpose model4 in Sec. D. Finally, implementation details of our experiments are explained in Sec. E. Our code will5 be made publicly available.6 In this section, we explain the spectral clustering algorithm [14, 18] in the semantic pattern extraction8 process for each image I (Sec.

A desirable data selection algorithm can efficiently choose the most informative samples to maximize the utility of limited annotation budgets. However, current approaches, represented by active learning methods, typically follow a cumbersome pipeline that iterates the time-consuming model training and batch data selection repeatedly. In this paper, we challenge this status quo by designing a distinct data selection pipeline that utilizes existing general-purpose models to select data from various datasets with a single-pass inference without the need for additional training or supervision. A novel free data selection (FreeSel) method is proposed following this new pipeline. Specifically, we define semantic patterns extracted from intermediate features of the general-purpose model to capture subtle local information in each image. We then enable the selection of all data samples in a single pass through distance-based sampling at the fine-grained semantic pattern level.

The ε-sensitivity of distributions is defined below. Next, we provide the following lemma. Suppose that the distribution map D(θ) forms a location family (7). To show the L-Lipschitz continuity of PR(θ), it suffices to show that there exists a positive constant L such that, for any θ,θ0 Θ, kPR(θ) PR(θ0)k2 Lkθ θ0k2. Thus, there exists a constant L Lθ + LZσmax(A) such that PR(θ) PR(θ0) 2 L θ θ0 2, θ,θ0 Θ, which proves the L-Lipschitz continuity of PR(θ).

What can be learned about causality and experimentation from passive data? This question is salient given recent successes of passively-trained language models in interactive domains such as tool use. Passive learning is inherently limited. However, we show that purely passive learning can in fact allow an agent to learn generalizable strategies for determining and using causal structures, as long as the agent can intervene at test time. We formally illustrate that, under certain assumptions, learning a strategy of first experimenting, then seeking goals, can allow generalization from passive learning in principle.