在许多科学学科中，我们有兴趣推断一组观察到的时间序列的非线性动力学系统，这是面对混乱的行为和噪音，这是一项艰巨的任务。以前的深度学习方法实现了这一目标，通常缺乏解释性和障碍。尤其是，即使基本动力学生存在较低维的多种多样的情况下，忠实嵌入通常需要的高维潜在空间也会阻碍理论分析。在树突计算的新兴原则的推动下，我们通过线性样条基础扩展增强了动态解释和数学可牵引的分段线性（PL）复发性神经网络（RNN）。我们表明，这种方法保留了简单PLRNN的所有理论上吸引人的特性，但在相对较低的尺寸中提高了其近似任意非线性动态系统的能力。我们采用两个框架来训练该系统，一个将反向传播的时间（BPTT）与教师强迫结合在一起，另一个将基于快速可扩展的变异推理的基础。我们表明，树枝状扩展的PLRNN可以在各种动力学系统基准上获得更少的参数和尺寸，并与其他方法进行比较，同时保留了可拖动和可解释的结构。

最近，机器学习在学习数据方程和动态系统方面被证明有效。但是，通常认为数据源自单个永不改变的系统。相反，当对现实世界动态过程进行建模时，由于基础系统动力学的变化，数据分布通常会发生变化。对这些过程的持续学习旨在迅速适应突然的系统变化，而不会忘记以前的动态制度。这项工作提出了一种基于储层计算的持续学习方法，储层计算是一种针对复杂时空动力学系统训练复发性神经网络的最先进方法。储层计算修复了经常性的网络权重 - 因此，这些重量无法忘记 - 仅将线性投影头更新到输出。我们建议同时培训多个竞争预测主管。受神经科学的预测编码的启发，只有最预测的头部激活，横向抑制，从而保护不活动的头部免受干扰参数更新引起的忘记。我们表明，这种多头储层可最大程度地减少在几种动态系统上的干扰和灾难性遗忘，包括Van-der-Pol振荡器，混沌Lorenz吸引者和高维Lorenz-96天气模型。我们的结果表明，储层计算是一个有前途的候选框架，用于持续学习动态系统。我们在\ url {https://github.com/leonardbereska/multiheadheadreservoir}上为数据生成，方法和比较提供代码。

Reading comprehension of legal text can be a particularly challenging task due to the length and complexity of legal clauses and a shortage of expert-annotated datasets. To address this challenge, we introduce the Merger Agreement Understanding Dataset (MAUD), an expert-annotated reading comprehension dataset based on the American Bar Association's 2021 Public Target Deal Points Study, with over 39,000 examples and over 47,000 total annotations. Our fine-tuned Transformer baselines show promising results, with models performing well above random on most questions. However, on a large subset of questions, there is still room for significant improvement. As the only expert-annotated merger agreement dataset, MAUD is valuable as a benchmark for both the legal profession and the NLP community.

Recently, learning-based controllers have been shown to push mobile robotic systems to their limits and provide the robustness needed for many real-world applications. However, only classical optimization-based control frameworks offer the inherent flexibility to be dynamically adjusted during execution by, for example, setting target speeds or actuator limits. We present a framework to overcome this shortcoming of neural controllers by conditioning them on an auxiliary input. This advance is enabled by including a feature-wise linear modulation layer (FiLM). We use model-free reinforcement-learning to train quadrotor control policies for the task of navigating through a sequence of waypoints in minimum time. By conditioning the policy on the maximum available thrust or the viewing direction relative to the next waypoint, a user can regulate the aggressiveness of the quadrotor's flight during deployment. We demonstrate in simulation and in real-world experiments that a single control policy can achieve close to time-optimal flight performance across the entire performance envelope of the robot, reaching up to 60 km/h and 4.5g in acceleration. The ability to guide a learned controller during task execution has implications beyond agile quadrotor flight, as conditioning the control policy on human intent helps safely bringing learning based systems out of the well-defined laboratory environment into the wild.

Double-blind peer review is considered a pillar of academic research because it is perceived to ensure a fair, unbiased, and fact-centered scientific discussion. Yet, experienced researchers can often correctly guess from which research group an anonymous submission originates, biasing the peer-review process. In this work, we present a transformer-based, neural-network architecture that only uses the text content and the author names in the bibliography to atttribute an anonymous manuscript to an author. To train and evaluate our method, we created the largest authorship-identification dataset to date. It leverages all research papers publicly available on arXiv amounting to over 2 million manuscripts. In arXiv-subsets with up to 2,000 different authors, our method achieves an unprecedented authorship attribution accuracy, where up to 95% of papers are attributed correctly. Thanks to our method, we are not only able to predict the author of an anonymous work but we also identify weaknesses of the double-blind review process by finding the key aspects that make a paper attributable. We believe that this work gives precious insights into how a submission can remain anonymous in order to support an unbiased double-blind review process.

Standard language model training employs gold human documents or human-human interaction data, and treats all training data as positive examples. Growing evidence shows that even with very large amounts of positive training data, issues remain that can be alleviated with relatively small amounts of negative data -- examples of what the model should not do. In this work, we propose a novel procedure to train with such data called the CRINGE loss (ContRastive Iterative Negative GEneration). We show the effectiveness of this approach across three different experiments on the tasks of safe generation, contradiction avoidance, and open-domain dialogue. Our models outperform multiple strong baselines and are conceptually simple, easy to train and implement.

The use of multilingual language models for tasks in low and high-resource languages has been a success story in deep learning. In recent times, Arabic has been receiving widespread attention on account of its dialectal variance. While prior research studies have tried to adapt these multilingual models for dialectal variants of Arabic, it still remains a challenging problem owing to the lack of sufficient monolingual dialectal data and parallel translation data of such dialectal variants. It remains an open problem on whether the limited dialectical data can be used to improve the models trained in Arabic on its dialectal variants. First, we show that multilingual-BERT (mBERT) incrementally pretrained on Arabic monolingual data takes less training time and yields comparable accuracy when compared to our custom monolingual Arabic model and beat existing models (by an avg metric of +$6.41$). We then explore two continual pre-training methods-- (1) using small amounts of dialectical data for continual finetuning and (2) parallel Arabic to English data and a Translation Language Modeling loss function. We show that both approaches help improve performance on dialectal classification tasks ($+4.64$ avg. gain) when used on monolingual models.

We present a new NLP task and dataset from the domain of the U.S. civil procedure. Each instance of the dataset consists of a general introduction to the case, a particular question, and a possible solution argument, accompanied by a detailed analysis of why the argument applies in that case. Since the dataset is based on a book aimed at law students, we believe that it represents a truly complex task for benchmarking modern legal language models. Our baseline evaluation shows that fine-tuning a legal transformer provides some advantage over random baseline models, but our analysis reveals that the actual ability to infer legal arguments remains a challenging open research question.

在许多现实世界中，当不二维测量值时，可能会提供自由旋转3D刚体（例如卫星）的图像观察。但是，图像数据的高维度排除了学习动力学和缺乏解释性的使用，从而降低了标准深度学习方法的有用性。在这项工作中，我们提出了一个物理知识的神经网络模型，以估计和预测图像序列中的3D旋转动力学。我们使用多阶段预测管道实现了这一目标，该管道将单个图像映射到潜在表示同构为$ \ Mathbf {so}（3）$，从潜在对计算角速度，并使用Hamiltonian Motion使用Hamiltonian运动方程来预测未来的潜在状态博学的哈密顿人的代表。我们证明了方法对新的旋转刚体数据集的功效，该数据集具有旋转立方体和矩形棱镜序列，并具有均匀且不均匀的密度。

ICECUBE是一种用于检测1 GEV和1 PEV之间大气和天体中微子的光学传感器的立方公斤阵列，该阵列已部署1.45 km至2.45 km的南极的冰盖表面以下1.45 km至2.45 km。来自ICE探测器的事件的分类和重建在ICeCube数据分析中起着核心作用。重建和分类事件是一个挑战，这是由于探测器的几何形状，不均匀的散射和冰中光的吸收，并且低于100 GEV的光，每个事件产生的信号光子数量相对较少。为了应对这一挑战，可以将ICECUBE事件表示为点云图形，并将图形神经网络（GNN）作为分类和重建方法。 GNN能够将中微子事件与宇宙射线背景区分开，对不同的中微子事件类型进行分类，并重建沉积的能量，方向和相互作用顶点。基于仿真，我们提供了1-100 GEV能量范围的比较与当前ICECUBE分析中使用的当前最新最大似然技术，包括已知系统不确定性的影响。对于中微子事件分类，与当前的IceCube方法相比，GNN以固定的假阳性速率（FPR）提高了信号效率的18％。另外，GNN在固定信号效率下将FPR的降低超过8（低于半百分比）。对于能源，方向和相互作用顶点的重建，与当前最大似然技术相比，分辨率平均提高了13％-20％。当在GPU上运行时，GNN能够以几乎是2.7 kHz的中位数ICECUBE触发速率的速率处理ICECUBE事件，这打开了在在线搜索瞬态事件中使用低能量中微子的可能性。