我们介绍了一项对自然语言（NL）推理的人类通知，开放域和逻辑上复杂且多样的数据集，配备了一阶逻辑（fol）注释。对开本由1,435个示例（独特的结论）组成，每个示例与487组前提之一搭配，这些场所作为规则，可用于演绎理由，以理解每个结论的有效性。前提和结论的逻辑正确性是通过其平行注释来确保的，这些注释会自动由我们的FOL推理引擎验证。除了主要的NL推理任务外，对开本中的NL-FOL对自动构成了使用FOL作为逻辑形式的新的NL-FOL翻译数据集。我们对广泛的实验系统地评估了对中型语言模型（BERT，ROBERTA）进行微调的FOL推理能力，并且在大型语言模型（GPT-NEOX，OPT，OPT，GPT-3，Codex）上促成了很少的射击。对于NL-FOL翻译，我们尝试使用GPT-3和Codex。我们的结果表明，公开可用的最强大的大语言模型之一（LLM），GPT-3 Davinci，仅比随机结果略好，而在一部分集的一部分中，该模型尤其不好，并且在预测该模型方面尤其不好。纠正虚假和未知结论的真实价值。我们的数据集和代码可在https://github.com/yale-lily/folio上找到。

机器的图像编码（ICM）旨在压缩图像进行AI任务分析，而不是满足人类的看法。学习一种既是一般（用于AI任务）的特征，也是紧凑的（用于压缩）的功能，这对于其成功而言至关重要。在本文中，我们试图通过学习通用功能，同时考虑压缩来开发ICM框架。我们将诸如无所不能功能和相应框架的功能命名为Omni-ICM。考虑到自我监督学习（SSL）提高了特征的概括，我们将其与压缩任务集成到OMNI-ICM框架中，以学习无所不能的功能。但是，在SSL中协调语义建模并在压缩中删除冗余是不平凡的，因此我们通过合作实例区分和熵最小化以自适应掉落的信息来设计新颖的信息过滤（如果）模块，以较弱相关的信息执行AI任务（例如，某些纹理冗余）。与以前的特定解决方案不同，Omni-ICM可以直接基于学习的无能功能的AI任务分析，而无需联合培训或额外的转换。尽管简单而直观，但Omni-ICM在多个基本愿景任务上大大优于现有的传统和基于学习的编解码器。

Human evaluation is the foundation upon which the evaluation of both summarization systems and automatic metrics rests. However, existing human evaluation protocols and benchmarks for summarization either exhibit low inter-annotator agreement or lack the scale needed to draw statistically significant conclusions, and an in-depth analysis of human evaluation is lacking. In this work, we address the shortcomings of existing summarization evaluation along the following axes: 1) We propose a modified summarization salience protocol, Atomic Content Units (ACUs), which relies on fine-grained semantic units and allows for high inter-annotator agreement. 2) We curate the Robust Summarization Evaluation (RoSE) benchmark, a large human evaluation dataset consisting of over 22k summary-level annotations over state-of-the-art systems on three datasets. 3) We compare our ACU protocol with three other human evaluation protocols, underscoring potential confounding factors in evaluation setups. 4) We evaluate existing automatic metrics using the collected human annotations across evaluation protocols and demonstrate how our benchmark leads to more statistically stable and significant results. Furthermore, our findings have important implications for evaluating large language models (LLMs), as we show that LLMs adjusted by human feedback (e.g., GPT-3.5) may overfit unconstrained human evaluation, which is affected by the annotators' prior, input-agnostic preferences, calling for more robust, targeted evaluation methods.

This paper introduces the shared task of summarizing documents in several creative domains, namely literary texts, movie scripts, and television scripts. Summarizing these creative documents requires making complex literary interpretations, as well as understanding non-trivial temporal dependencies in texts containing varied styles of plot development and narrative structure. This poses unique challenges and is yet underexplored for text summarization systems. In this shared task, we introduce four sub-tasks and their corresponding datasets, focusing on summarizing books, movie scripts, primetime television scripts, and daytime soap opera scripts. We detail the process of curating these datasets for the task, as well as the metrics used for the evaluation of the submissions. As part of the CREATIVESUMM workshop at COLING 2022, the shared task attracted 18 submissions in total. We discuss the submissions and the baselines for each sub-task in this paper, along with directions for facilitating future work in the field.

由于非平稳性，现实世界多变量时间序列（MTS）的分布会随着时间而变化，称为分布漂移。大多数现有的MT预测模型都会极大地遭受分销漂移的影响，并随着时间的推移降低了预测性能。现有方法通过适应最新到达数据或根据未来数据得出的元知识进行自我纠正来解决分布漂移。尽管在MT的预测中取得了巨大的成功，但这些方法几乎无法捕获固有的分布变化，尤其是从分布的角度来看。因此，我们提出了一个新型的框架时间条件变化自动编码器（TCVAE），以对MTS中历史观察结果和未来数据之间的动态分布依赖性进行建模，并将依赖性作为时间条件分布推断为利用潜在变量。具体而言，新型的颞鹰注意机制代表了随后馈入馈送前网络的时间因素，以估计潜在变量的先前高斯分布。时间因素的表示进一步动态地调整了基于变压器的编码器和解码器的结构，以利用门控注意机制来变化。此外，我们引入条件连续归一化流量，以将先前的高斯转化为复杂且无形式的分布，以促进对时间条件分布的灵活推断。在六个现实世界MTS数据集上进行的广泛实验表明，与最先进的MTS预测基线相比，TCVAE的出色鲁棒性和有效性。我们进一步说明了TCVAE通过多方面的案例研究和现实情况下的可视化来说明TCVAE的适用性。

在过去的几年中，对MPMRI的恶性前列腺癌患者进行了自动诊断。模型解释和域漂移一直是临床利用的主要路障。作为我们以前的工作的扩展，我们在公共队列上培训了一个定制的卷积神经网络，其中有201名患者和感兴趣区域周围的裁剪2D斑块作为输入，将前列腺的2.5d片用作前列腺的2.5d片。使用Autokeras在模型空间中搜索了输入和最佳模型。外围区（PZ）和中央腺（CG）分别进行了训练和测试，有效地证明了一些不同的东西，PZ探测器和CG探测器有效地展示了序列中最可疑的切片，希望极大地减轻医生的工作量。

理解NAND闪存信道由于不断增加的密度和从写入和读取机制而产生的复杂扭曲而变得越来越具有挑战性。在这项工作中，我们提出了一种数据驱动的生成建模方法来表征闪存通道。学习模型可以基于小区的节目级别和其周围的单元阵列重建来自单个存储器单元的读取电压。实验结果表明，重建读取电压的统计分布精确地反映了商业闪存芯片上的测量分布，定性和通过总变化距离量化。此外，我们观察到学习模型可以捕获精确的小区间干扰（ICI）效应，如通过字线和位线的特定模式的误差概率比较所验证。

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

Given the increasingly intricate forms of partial differential equations (PDEs) in physics and related fields, computationally solving PDEs without analytic solutions inevitably suffers from the trade-off between accuracy and efficiency. Recent advances in neural operators, a kind of mesh-independent neural-network-based PDE solvers, have suggested the dawn of overcoming this challenge. In this emerging direction, Koopman neural operator (KNO) is a representative demonstration and outperforms other state-of-the-art alternatives in terms of accuracy and efficiency. Here we present KoopmanLab, a self-contained and user-friendly PyTorch module of the Koopman neural operator family for solving partial differential equations. Beyond the original version of KNO, we develop multiple new variants of KNO based on different neural network architectures to improve the general applicability of our module. These variants are validated by mesh-independent and long-term prediction experiments implemented on representative PDEs (e.g., the Navier-Stokes equation and the Bateman-Burgers equation) and ERA5 (i.e., one of the largest high-resolution data sets of global-scale climate fields). These demonstrations suggest the potential of KoopmanLab to be considered in diverse applications of partial differential equations.

Rankings are widely collected in various real-life scenarios, leading to the leakage of personal information such as users' preferences on videos or news. To protect rankings, existing works mainly develop privacy protection on a single ranking within a set of ranking or pairwise comparisons of a ranking under the $\epsilon$-differential privacy. This paper proposes a novel notion called $\epsilon$-ranking differential privacy for protecting ranks. We establish the connection between the Mallows model (Mallows, 1957) and the proposed $\epsilon$-ranking differential privacy. This allows us to develop a multistage ranking algorithm to generate synthetic rankings while satisfying the developed $\epsilon$-ranking differential privacy. Theoretical results regarding the utility of synthetic rankings in the downstream tasks, including the inference attack and the personalized ranking tasks, are established. For the inference attack, we quantify how $\epsilon$ affects the estimation of the true ranking based on synthetic rankings. For the personalized ranking task, we consider varying privacy preferences among users and quantify how their privacy preferences affect the consistency in estimating the optimal ranking function. Extensive numerical experiments are carried out to verify the theoretical results and demonstrate the effectiveness of the proposed synthetic ranking algorithm.