Humans can classify an unseen category by reasoning on its language explanations. This ability is owing to the compositional nature of language: we can combine previously seen concepts to describe the new category. For example, we might describe mavens as "a kind of large birds with black feathers", so that others can use their knowledge of concepts "large birds" and "black feathers" to recognize a maven. Inspired by this observation, in this work we tackle zero-shot classification task by logically parsing and reasoning on natural language explanations. To this end, we propose the framework CLORE (Classification by LOgical Reasoning on Explanations). While previous methods usually regard textual information as implicit features, CLORE parses the explanations into logical structure the and then reasons along this structure on the input to produce a classification score. Experimental results on explanation-based zero-shot classification benchmarks demonstrate that CLORE is superior to baselines, mainly because it performs better on tasks requiring more logical reasoning. Alongside classification decisions, CLORE can provide the logical parsing and reasoning process as a form of rationale. Through empirical analysis we demonstrate that CLORE is also less affected by linguistic biases than baselines.

通常使用自回归生成模型，尤其是对于涉及顺序数据的那些任务。然而，由于链式有条件建模的内在特征（例如，暴露偏见或缺乏远距离连贯性），由于许多固有的缺陷而困扰着它们，严重限制了它们正确模型分布的能力。在本文中，我们提出了一种独特的方法，该方法称为训练自回旋生成模型，以利用精心设计的基于能量的学习目标。通过利用SoftMax操作的额外自由度，我们被允许使自回归模型本身成为基于能量的模型，用于衡量输入的可能性，而无需引入任何额外的参数。此外，我们表明可以有效地训练电子臂，并能够减轻暴露偏置问题并增加自回归生成模型的时间连贯性。广泛的经验结果涵盖了语言建模，神经机器翻译和图像产生等基准，证明了拟议方法的有效性。

时间序列数据生成近年来越来越受到关注。已经提出了几种生成的对抗网络（GaN）的方法通常是假设目标时间序列数据良好格式化并完成的假设来解决问题。然而，现实世界时间序列（RTS）数据远离该乌托邦，例如，具有可变长度的长序列和信息缺失数据，用于设计强大的发电算法的棘手挑战。在本文中，我们向RTS数据提出了一种新的生成框架 - RTSGAN来解决上述挑战。 RTSGAN首先学习编码器 - 解码器模块，该模块提供时间序列实例和固定维度潜在载体之间的映射，然后学习生成模块以在同一潜在空间中生成vectors。通过组合发电机和解码器，RTSGAN能够生成尊重原始特征分布和时间动态的RTS。为了生成具有缺失值的时间序列，我们进一步用观察嵌入层和决定和生成解码器装备了RTSGAN，以更好地利用信息缺失模式。四个RTS数据集上的实验表明，该框架在用于下游分类和预测任务的合成数据实用程序方面优于前一代方法。

最近已经采取了密集的算法努力来实现复杂ML模型的认证鲁棒性快速改善。但是，当前的鲁棒性认证方法只能在有限的扰动半径下进行认证。鉴于现有的纯数据驱动的统计方法已经达到了瓶颈，因此我们建议将统计ML模型与知识（以逻辑规则表示为逻辑规则）作为使用Markov Logic Networks（MLN（MLN）（以进一步提高）的推理组件，以进一步改善总体认证的鲁棒性。这为证明这种范式的鲁棒性，尤其是推理组成部分（例如MLN）开辟了新的研究问题。作为理解这些问题的第一步，我们首先证明了证明MLN鲁棒性的计算复杂性是＃p-hard。在这种硬度结果的指导下，我们通过仔细分析不同的模型制度来得出第一个用于MLN的认证鲁棒性。最后，我们对五个数据集进行了广泛的实验，包括高维图像和自然语言文本，以及自然语言文本，以及我们表明，具有基于知识的逻辑推理的经认证的鲁棒性确实胜过了T。心。

Blind image quality assessment (BIQA) remains challenging due to the diversity of distortion and image content variation, which complicate the distortion patterns crossing different scales and aggravate the difficulty of the regression problem for BIQA. However, existing BIQA methods often fail to consider multi-scale distortion patterns and image content, and little research has been done on learning strategies to make the regression model produce better performance. In this paper, we propose a simple yet effective Progressive Multi-Task Image Quality Assessment (PMT-IQA) model, which contains a multi-scale feature extraction module (MS) and a progressive multi-task learning module (PMT), to help the model learn complex distortion patterns and better optimize the regression issue to align with the law of human learning process from easy to hard. To verify the effectiveness of the proposed PMT-IQA model, we conduct experiments on four widely used public datasets, and the experimental results indicate that the performance of PMT-IQA is superior to the comparison approaches, and both MS and PMT modules improve the model's performance.

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.

We present SODA: the first publicly available, million-scale high-quality social dialogue dataset. Using SODA, we train COSMO: a generalizable conversation agent outperforming previous best-performing agents on both in- and out-of-domain datasets. In contrast to most existing crowdsourced, small-scale dialogue corpora, we distill 1.5M socially-grounded dialogues from a pre-trained language model (InstructGPT; Ouyang et al., 2022). Dialogues are distilled by contextualizing social commonsense knowledge from a knowledge graph (Atomic10x; West et al., 2022). Human evaluation shows that dialogues in SODA are more consistent, specific, and (surprisingly) natural than prior human-authored datasets - e.g., DailyDialog (Li et al., 2017), BlendedSkillTalk (Smith et al., 2020). In addition, extensive evaluations show that COSMO is significantly more natural and consistent on unseen datasets than best-performing dialogue models - e.g., GODEL (Peng et al., 2022), BlenderBot (Roller et al., 2021), DialoGPT (Zhang et al., 2020). Furthermore, it is sometimes even preferred to the original human-written gold responses. We make our data, models, and code public.

We propose a novel task, G4C (Goal-driven Guidance Generation in Grounded Communication), for studying goal-driven and grounded natural language interactions. Specifically, we choose Dungeons and Dragons (D&D) -- a role-playing game consisting of multiple player characters and a Dungeon Master (DM) who collaborate to achieve a set of goals that are beneficial to the players -- as a testbed for this task. Here, each of the player characters is a student, with their own personas and abilities, and the DM is the teacher, an arbitrator of the rules of the world and responsible for assisting and guiding the students towards a global goal. We propose a theory-of-mind-inspired methodology for training such a DM with reinforcement learning (RL), where a DM: (1) learns to predict how the players will react to its utterances using a dataset of D&D dialogue transcripts; and (2) uses this prediction as a reward function providing feedback on how effective these utterances are at guiding the players towards a goal. Human and automated evaluations show that a DM trained with RL to generate guidance by incorporating a theory-of-mind of the players significantly improves the players' ability to achieve goals grounded in their shared world.

Aiming at the current problems of theory-oriented,practice-light,and lack of innovation ability in the teaching of postgraduate software engineering courses,a multi-stage feedback teaching mode for software engineering postgraduates based on competition project_driven is proposed. The model is driven by the competition project,and implementing suggestions are given in terms of stage allocation of software engineering course tasks and ability cultivation,competition case design and process evaluation improvement,etc. Through the implementation of this teaching mode,students enthusiasm and initiative are expected to be stimulated,and the overall development of students professional skills and comprehension ability would be improved to meet the demand of society for software engineering technical talents.

We present POTATO, the Portable text annotation tool, a free, fully open-sourced annotation system that 1) supports labeling many types of text and multimodal data; 2) offers easy-to-configure features to maximize the productivity of both deployers and annotators (convenient templates for common ML/NLP tasks, active learning, keypress shortcuts, keyword highlights, tooltips); and 3) supports a high degree of customization (editable UI, inserting pre-screening questions, attention and qualification tests). Experiments over two annotation tasks suggest that POTATO improves labeling speed through its specially-designed productivity features, especially for long documents and complex tasks. POTATO is available at https://github.com/davidjurgens/potato and will continue to be updated.