在本文中，我们描述了我们参与Case-2022的子任务1，即与休闲新闻语料库的事件因果关系识别。我们通过在少数带注释的示例（即几次配置）上利用一组简单但互补的技术来解决因果关系识别（CRI）任务。我们遵循一种基于迅速的预测方法，用于微调LMS，其中CRI任务被视为掩盖语言建模问题（MLM）。这种方法允许LMS在MLM问题上进行本地预先训练，可以直接生成对CRI特异性提示的文本响应。我们将此方法的性能与在整个数据集中训练的集合技术进行比较。我们表现​​最佳的提交仅接受了每班256个实例，整个数据集的一小部分培训，但能够获得第二好的精度（0.82），第三好的精度（0.82）和F1得分。 （0.85）非常接近获胜者团队（0.86）的报道。

在本文中，我们描述了Case-2022中的子任务2（与休闲新闻语料库的事件因果关系识别）中的共享任务提交。挑战的重点是自动检测新闻媒体中句子中存在的所有因果信号跨度。我们使用T5（一种预先训练的自回归语言模型）检测句子中的因果信号跨度。我们迭代地识别所有原因效应信号跨度三重态，始终在先前预测的三胞胎上预测下一个三重态。为了预测三胞胎本身，我们考虑了不同的因果关系，例如$ \ rightarrow $效果$ \ rightarrow $信号。每个三重态组件都是通过在句子上，当前三重态的前部以及先前预测的三胞胎的语言模型生成的。尽管在一个非常小的160个样本数据集上进行了培训，但我们的方法仍取得了竞争性能，并在比赛中排名第二。此外，我们表明，假设$ \ rightarrow $效果或效果$ \ rightarrow $导致订单实现相似的结果。我们的代码和模型预测将在线发布。

我们提出了索赔动物：事实检验和事实分析的新型潜在变量模型，该模型给出了索赔和一组检索的证据，可以共同学习：（i）该主张的相关证明是什么（ii）这一说法的真实性。我们建议以可解释的方式删除提供的全部相关性概率及其对最终准确性概率的贡献 - 最终的准确性概率与单位相关性概率的线性集合成正比。这样，可以清楚地识别出哪些来源的相关性在何种程度上朝着最终概率方面的范围。我们表明，我们的系统在发烧数据集上实现了最先进的结果，可与通常在传统事实检查管道中使用的两阶段系统相当，而通常使用的参数和计算较少。我们的分析表明，提出的方法进一步允许不仅了解哪些证明是相关的，而且还可以在没有直接监督的情况下获得支持和拒绝索赔的哪些证明。这不仅增加了解释性，而且还允许自动检测出与证据相互冲突的索赔。此外，我们研究模型在使用粗粒监督时是否可以学习细粒度的相关性线索。我们表明，我们的模型只能使用段落级相关性监督，可以实现竞争性的句子回顾。最后，朝着最优质的相关性跨度，我们表明我们的框架能够在令牌级别上识别相关性。为此，我们提出了一个专注于令牌级别的解释性的新基准 - 人类在相关证明中注释令牌，他们在做出判断时认为必不可少。然后，我们衡量这些注释与代币的相似之处是我们的模型的重点。我们的代码和数据集将在线发布。

In this paper, we propose a new neural network architecture based on the H2 matrix. Even though networks with H2-inspired architecture already exist, and our approach is designed to reduce memory costs and improve performance by taking into account the sparsity template of the H2 matrix. In numerical comparison with alternative neural networks, including the known H2-based ones, our architecture showed itself as beneficial in terms of performance, memory, and scalability.

t-SNE remains one of the most popular embedding techniques for visualizing high-dimensional data. Most standard packages of t-SNE, such as scikit-learn, use the Barnes-Hut t-SNE (BH t-SNE) algorithm for large datasets. However, existing CPU implementations of this algorithm are inefficient. In this work, we accelerate the BH t-SNE on CPUs via cache optimizations, SIMD, parallelizing sequential steps, and improving parallelization of multithreaded steps. Our implementation (Acc-t-SNE) is up to 261x and 4x faster than scikit-learn and the state-of-the-art BH t-SNE implementation from daal4py, respectively, on a 32-core Intel(R) Icelake cloud instance.

We investigate a model for image/video quality assessment based on building a set of codevectors representing in a sense some basic properties of images, similar to well-known CORNIA model. We analyze the codebook building method and propose some modifications for it. Also the algorithm is investigated from the point of inference time reduction. Both natural and synthetic images are used for building codebooks and some analysis of synthetic images used for codebooks is provided. It is demonstrated the results on quality assessment may be improves with the use if synthetic images for codebook construction. We also demonstrate regimes of the algorithm in which real time execution on CPU is possible for sufficiently high correlations with mean opinion score (MOS). Various pooling strategies are considered as well as the problem of metric sensitivity to bitrate.

Online controlled experiments (A/B tests) have become the gold standard for learning the impact of new product features in technology companies. Randomization enables the inference of causality from an A/B test. The randomized assignment maps end users to experiment buckets and balances user characteristics between the groups. Therefore, experiments can attribute any outcome differences between the experiment groups to the product feature under experiment. Technology companies run A/B tests at scale -- hundreds if not thousands of A/B tests concurrently, each with millions of users. The large scale poses unique challenges to randomization. First, the randomized assignment must be fast since the experiment service receives hundreds of thousands of queries per second. Second, the variant assignments must be independent between experiments. Third, the assignment must be consistent when users revisit or an experiment enrolls more users. We present a novel assignment algorithm and statistical tests to validate the randomized assignments. Our results demonstrate that not only is this algorithm computationally fast but also satisfies the statistical requirements -- unbiased and independent.

Vision Transformers convert images to sequences by slicing them into patches. The size of these patches controls a speed/accuracy tradeoff, with smaller patches leading to higher accuracy at greater computational cost, but changing the patch size typically requires retraining the model. In this paper, we demonstrate that simply randomizing the patch size at training time leads to a single set of weights that performs well across a wide range of patch sizes, making it possible to tailor the model to different compute budgets at deployment time. We extensively evaluate the resulting model, which we call FlexiViT, on a wide range of tasks, including classification, image-text retrieval, open-world detection, panoptic segmentation, and semantic segmentation, concluding that it usually matches, and sometimes outperforms, standard ViT models trained at a single patch size in an otherwise identical setup. Hence, FlexiViT training is a simple drop-in improvement for ViT that makes it easy to add compute-adaptive capabilities to most models relying on a ViT backbone architecture. Code and pre-trained models are available at https://github.com/google-research/big_vision

Multi-agent path finding (MAPF) is a task of finding non-conflicting paths connecting agents' specified initial and goal positions in a shared environment. We focus on compilation-based solvers in which the MAPF problem is expressed in a different well established formalism such as mixed-integer linear programming (MILP), Boolean satisfiability (SAT), or constraint programming (CP). As the target solvers for these formalisms act as black-boxes it is challenging to integrate MAPF specific heuristics in the MAPF compilation-based solvers. We show in this work how the build a MAPF encoding for the target SAT solver in which domain specific heuristic knowledge is reflected. The heuristic knowledge is transferred to the SAT solver by selecting candidate paths for each agent and by constructing the encoding only for these candidate paths instead of constructing the encoding for all possible paths for an agent. The conducted experiments show that heuristically guided compilation outperforms the vanilla variants of the SAT-based MAPF solver.

The appearance of an object can be fleeting when it transforms. As eggs are broken or paper is torn, their color, shape and texture can change dramatically, preserving virtually nothing of the original except for the identity itself. Yet, this important phenomenon is largely absent from existing video object segmentation (VOS) benchmarks. In this work, we close the gap by collecting a new dataset for Video Object Segmentation under Transformations (VOST). It consists of more than 700 high-resolution videos, captured in diverse environments, which are 20 seconds long on average and densely labeled with instance masks. A careful, multi-step approach is adopted to ensure that these videos focus on complex object transformations, capturing their full temporal extent. We then extensively evaluate state-of-the-art VOS methods and make a number of important discoveries. In particular, we show that existing methods struggle when applied to this novel task and that their main limitation lies in over-reliance on static appearance cues. This motivates us to propose a few modifications for the top-performing baseline that improve its capabilities by better modeling spatio-temporal information. But more broadly, the hope is to stimulate discussion on learning more robust video object representations.