我们提出了Pangu-Coder，这是一种仅预读的解码器语言模型，该模型采用pangu-alpha架构进行文本到代码生成，即给定自然语言问题描述的编程语言解决方案的合成。我们使用两阶段策略训练Pangu-Coder：第一阶段采用因果语言建模（CLM）来预先培训原始编程语言数据，而第二阶段则使用因果语言建模和掩盖语言建模（MLM）的组合培训目标，专注于文本到代码生成的下游任务，并培训松散的自然语言程序定义和代码功能。最后，我们讨论了pangu-coder-ft，该pander the是通过竞争性编程问题和代码与持续集成测试的结合进行了微调的。我们评估了pangu-coder，重点是它是否生成功能上正确的程序，并证明它在参加较小的上下文窗口和较少的数据培训的同时，它比诸如Codex之类的类似大小的模型（例如Codex）实现等效性或更好的性能。

Modeling lies at the core of both the financial and the insurance industry for a wide variety of tasks. The rise and development of machine learning and deep learning models have created many opportunities to improve our modeling toolbox. Breakthroughs in these fields often come with the requirement of large amounts of data. Such large datasets are often not publicly available in finance and insurance, mainly due to privacy and ethics concerns. This lack of data is currently one of the main hurdles in developing better models. One possible option to alleviating this issue is generative modeling. Generative models are capable of simulating fake but realistic-looking data, also referred to as synthetic data, that can be shared more freely. Generative Adversarial Networks (GANs) is such a model that increases our capacity to fit very high-dimensional distributions of data. While research on GANs is an active topic in fields like computer vision, they have found limited adoption within the human sciences, like economics and insurance. Reason for this is that in these fields, most questions are inherently about identification of causal effects, while to this day neural networks, which are at the center of the GAN framework, focus mostly on high-dimensional correlations. In this paper we study the causal preservation capabilities of GANs and whether the produced synthetic data can reliably be used to answer causal questions. This is done by performing causal analyses on the synthetic data, produced by a GAN, with increasingly more lenient assumptions. We consider the cross-sectional case, the time series case and the case with a complete structural model. It is shown that in the simple cross-sectional scenario where correlation equals causation the GAN preserves causality, but that challenges arise for more advanced analyses.

In addition to being a widely recognised novelist, Milan Kundera has also authored three pieces for theatre: The Owners of the Keys (Majitel\'e kl\'i\v{c}\r{u}, 1961), The Blunder (Pt\'akovina, 1967), and Jacques and his Master (Jakub a jeho p\'an, 1971). In recent years, however, the hypothesis has been raised that Kundera is the true author of a fourth play: Juro J\'ano\v{s}\'ik, first performed in a 1974 production under the name of Karel Steigerwald, who was Kundera's student at the time. In this study, we make use of supervised machine learning to settle the question of authorship attribution in the case of Juro J\'ano\v{s}\'ik, with results strongly supporting the hypothesis of Kundera's authorship.

We consider the nonstochastic multi-agent multi-armed bandit problem with agents collaborating via a communication network with delays. We show a lower bound for individual regret of all agents. We show that with suitable regularizers and communication protocols, a collaborative multi-agent \emph{follow-the-regularized-leader} (FTRL) algorithm has an individual regret upper bound that matches the lower bound up to a constant factor when the number of arms is large enough relative to degrees of agents in the communication graph. We also show that an FTRL algorithm with a suitable regularizer is regret optimal with respect to the scaling with the edge-delay parameter. We present numerical experiments validating our theoretical results and demonstrate cases when our algorithms outperform previously proposed algorithms.

Cross entropy loss has served as the main objective function for classification-based tasks. Widely deployed for learning neural network classifiers, it shows both effectiveness and a probabilistic interpretation. Recently, after the success of self supervised contrastive representation learning methods, supervised contrastive methods have been proposed to learn representations and have shown superior and more robust performance, compared to solely training with cross entropy loss. However, cross entropy loss is still needed to train the final classification layer. In this work, we investigate the possibility of learning both the representation and the classifier using one objective function that combines the robustness of contrastive learning and the probabilistic interpretation of cross entropy loss. First, we revisit a previously proposed contrastive-based objective function that approximates cross entropy loss and present a simple extension to learn the classifier jointly. Second, we propose a new version of the supervised contrastive training that learns jointly the parameters of the classifier and the backbone of the network. We empirically show that our proposed objective functions show a significant improvement over the standard cross entropy loss with more training stability and robustness in various challenging settings.

Industrial Internet of Things (IoT) systems increasingly rely on wireless communication standards. In a common industrial scenario, indoor wireless IoT devices communicate with access points to deliver data collected from industrial sensors, robots and factory machines. Due to static or quasi-static locations of IoT devices and access points, historical observations of IoT device channel conditions provide a possibility to precisely identify the device without observing its traditional identifiers (e.g., MAC or IP address). Such device identification methods based on wireless fingerprinting gained increased attention lately as an additional cyber-security mechanism for critical IoT infrastructures. In this paper, we perform a systematic study of a large class of machine learning algorithms for device identification using wireless fingerprints for the most popular cellular and Wi-Fi IoT technologies. We design, implement, deploy, collect relevant data sets, train and test a multitude of machine learning algorithms, as a part of the complete end-to-end solution design for device identification via wireless fingerprinting. The proposed solution is currently being deployed in a real-world industrial IoT environment as part of H2020 project COLLABS.

我们描述了关于多语言核心分辨率的CRAC 2022共享任务的获胜提交。我们的系统首先求解了提及检测，然后使用先进的最大化方法在检索到的跨度上链接，并且这两个任务均与共享变压器的权重进行微调。我们报告了微调各种预审预告额的结果。此贡献的中心是微调的多语言模型。我们发现了一个具有足够大的编码器的大型多语言模型，可以全面提高所有数据集的性能，因此不仅限于代表性不足的语言或类型上相对语言的群体。源代码可在https://github.com/ufal/crac2022-corpipe上获得。

图像二进制技术通常用于增强嘈杂和/或退化的图像来迎合不同文档图像Anlaysis（DIA）应用（如单词斑点，文档检索和OCR）。大多数现有技术都集中在将像素图像馈送到卷积神经网络中以完成文档二进制化，这在使用不完全减压的情况下需要处理的压缩图像时可能不会产生有效的结果。因此，在本研究论文中，通过使用双重鉴别器生成对抗网络（DD-GAN），提出了使用JPEG压缩图像的文档图像二进制的想法。在这里，两个歧视者网络 - 全球和本地工作在不同的图像比率上，并将焦点损失用作发电机损失。提出的模型已通过不同版本的DIBCO数据集进行了彻底的测试，该数据集具有诸如孔，擦除或弄脏的墨水，灰尘和放错地方的挑战。在时间和空间复杂性方面，该模型被证明是高度鲁棒，有效的，并且还导致了JPEG压缩域中的最新性能。

在包括搜索在内的各种应用程序中，积极消费数字文档的研究范围为研究范围。传统上，文档中的搜索是作为文本匹配的问题施放的，忽略了结构化文档，表格等中常见的丰富布局和视觉提示。为此，我们提出了一个大多数未探索的问题：“我们可以搜索其他类似的snippets在目标文档页面中存在给定文档摘要的单个查询实例吗？”。我们建议单体将其作为单拍的摘要检测任务解决。单体融合了摘要和文档的视觉，文本和空间方式的上下文，以在目标文档中找到查询片段。我们进行了广泛的消融和实验，显示单体从一击对象检测（BHRL），模板匹配和文档理解（Layoutlmv3）中优于几个基线。由于目前的任务缺乏相关数据，因此我们对单体进行了编程生成的数据训练，该数据具有许多视觉上相似的查询片段和来自两个数据集的目标文档对 - Flamingo表单和PublayNet。我们还进行人类研究以验证生成的数据。

我们在GPU上实现了一种信任区域方法，用于使用称为JAX的新的深度学习Python库，用于非线性最小二乘曲线曲线拟合问题。我们的开源软件包JaxFit适用于无约束和约束曲线拟合问题，并允许仅在Python中定义拟合功能 - 而无需对GPU或CUDA编程的任何专业知识。由于JaxFit在GPU上运行，尽管非常易于使用，但它比基于CPU的库甚至其他基于GPU的库快得多。此外，由于JAX的深度学习基础，Jaxfit的信任区域算法中的Jacobian是通过自动分化计算的，而不是使用衍生近似值或要求用户定义拟合函数的部分导数。