There has been great progress in unifying various table-to-text tasks using a single encoder-decoder model trained via multi-task learning (Xie et al., 2022). However, existing methods typically encode task information with a simple dataset name as a prefix to the encoder. This not only limits the effectiveness of multi-task learning, but also hinders the model's ability to generalize to new domains or tasks that were not seen during training, which is crucial for real-world applications. In this paper, we propose compositional task configurations, a set of prompts prepended to the encoder to improve cross-task generalization of unified models. We design the task configurations to explicitly specify the task type, as well as its input and output types. We show that this not only allows the model to better learn shared knowledge across different tasks at training, but also allows us to control the model by composing new configurations that apply novel input-output combinations in a zero-shot manner. We demonstrate via experiments over ten table-to-text tasks that our method outperforms the UnifiedSKG baseline by noticeable margins in both in-domain and zero-shot settings, with average improvements of +0.5 and +12.6 from using a T5-large backbone, respectively.

大型语言模型在各种问题答案（QA）基准测试方面取得了高度的性能，但其产出的解释性仍然难以捉摸。最近建议将结构化的解释称为“综合树”，以解释和检查质量检查系统的答案。为了更好地生成此类树木，我们提出了一种称为迭代检索生成推理​​器（IRGR）的架构。我们的模型能够通过系统地生成文本前提的分步解释来解释给定的假设。 IRGR模型迭代地搜索合适的场所，一次构建单个零件步骤。与以前的方法相反，我们的方法结合了生成步骤和房屋的检索，允许模型利用中间结论，并减轻基线编码器模型的输入大小限制。我们使用IntailmentBank数据集进行实验，在该数据集中，我们在前提检索和索引树上的现有基准优于现有的基准，总体正确性增长了约300％。

配备高速数字化器的前端电子设备正在使用并建议将来的核检测器。最近的文献表明，在处理来自核检测器的数字信号时，深度学习模型，尤其是一维卷积神经网络。模拟和实验证明了该领域神经网络的令人满意的准确性和其他好处。但是，仍需要研究特定的硬件加速在线操作。在这项工作中，我们介绍了Pulsedl-II，这是一种专门设计的，专门为事件功能（时间，能量等）从具有深度学习的脉冲中提取的应用。根据先前的版本，PULSEDL-II将RISC CPU纳入系统结构，以更好地功能灵活性和完整性。 SOC中的神经网络加速器采用三级（算术单元，处理元件，神经网络）层次结构，并促进数字设计的参数优化。此外，我们设计了一种量化方案和相关的实现方法（恢复和位移位），以在所选层类型的选定子集中与深度学习框架（例如Tensorflow）完全兼容。通过当前方案，支持神经网络的量化训练，并通过专用脚本自动将网络模型转换为RISC CPU软件，几乎没有准确性损失。我们在现场可编程门阵列（FPGA）上验证pulsedl-ii。最后，通过由直接数字合成（DDS）信号发生器和带有模数转换器（ADC）的FPGA开发板组成的实验设置进行系统验证。拟议的系统实现了60 PS的时间分辨率和0.40％的能量分辨率，在线神经网络推断在信号与噪声比（SNR）为47.4 dB时。

当可用的硬件无法满足内存和计算要求以有效地训练高性能的机器学习模型时，需要妥协训练质量或模型复杂性。在联合学习（FL）中，节点是比传统服务器级硬件更具限制的数量级，并且通常是电池供电的，严重限制了可以在此范式下训练的模型的复杂性。尽管大多数研究都集中在设计更好的聚合策略上以提高收敛速度并减轻FL的沟通成本，但更少的努力致力于加快设备培训。这样的阶段重复数百次（即每回合）并可能涉及数千个设备，这是培训联合模型所需的大部分时间，以及客户端的全部能源消耗。在这项工作中，我们介绍了第一个研究在FL工作负载中培训时间引入稀疏性时出现的独特方面的研究。然后，我们提出了Zerofl，该框架依赖于高度稀疏的操作来加快设备训练。与通过将最先进的稀疏训练框架适应FL设置相比，接受Zerofl和95％稀疏性训练的模型高达2.3％的精度。

联邦学习（FL）已成为一种前瞻性解决方案，可促进对高性能的集中模型的培训，而不会损害用户的隐私。尽管成功，但目前的研究受到了在实验初期建立现实的大规模FL系统的可能性的限制。仿真可以帮助加速这一过程。为了促进异构客户的有效可扩展的FL模拟，我们设计和实施ProteA，这是使用FL框架花朵在联合系统中灵活且轻巧的客户型分析组件。它允许自动收集系统级统计信息并估算每个客户所需的资源，从而以资源感知方式运行模拟。结果表明，我们的设计成功地增加了1.66 $ \ times $ $更快的壁挂时间和2.6 $ \ times $更好的GPU利用率的平行性，这可以对异构客户进行大规模实验。

尽管结果令人印象深刻，但深度学习的技术还引起了经常在数据中心进行的培训程序引起的严重隐私和环境问题。作为回应，已经出现了集中培训的替代方案，例如联邦学习（FL）。也许出乎意料的是，FL开始在全球范围内部署，这些公司必须遵守源自倡导隐私保护的政府和社会团体的新法律要求和政策。 \ textit {但是，与FL有关的潜在环境影响仍然不清楚和未开发。本文提供了有关佛罗里达碳足迹的首次系统研究。然后，我们将FL的碳足迹与传统的集中学习进行了比较。我们的发现表明，根据配置，FL可以比集中的机器学习高达两个数量级。但是，在某些情况下，由于嵌入式设备的能源消耗减少，它可以与集中学习相提并论。我们使用FL进行了不同类型的数据集，设置和各种深度学习模型的广泛实验。最后，我们强调并将报告的结果与FL的未来挑战和趋势联系起来，以减少其环境影响，包括算法效率，硬件能力和更强的行业透明度。

联合学习（FL）作为边缘设备的有希望的技术，以协作学习共享预测模型，同时保持其训练数据，从而解耦了从需要存储云中的数据的机器学习的能力。然而，在规模和系统异质性方面，FL难以现实地实现。虽然有许多用于模拟FL算法的研究框架，但它们不支持在异构边缘设备上进行可扩展的流程。在本文中，我们呈现花 - 一种全面的FL框架，通过提供新的设施来执行大规模的FL实验并考虑丰富的异构流程来区分现有平台。我们的实验表明花卉可以仅使用一对高端GPU在客户尺寸下进行FL实验。然后，研究人员可以将实验无缝地迁移到真实设备中以检查设计空间的其他部分。我们认为花卉为社区提供了一个批判性的新工具，用于研究和发展。

Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may originate from multiple different domains, and may not contain labels for training. Certain settings, for instance medical applications, often involve further restrictions that prohibit retention of previously seen data due to privacy regulations. In this work, to address such challenges, we study unsupervised segmentation in continual learning scenarios that involve domain shift. To that end, we introduce GarDA (Generative Appearance Replay for continual Domain Adaptation), a generative-replay based approach that can adapt a segmentation model sequentially to new domains with unlabeled data. In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains. Unlike previous approaches in incremental UDA, our method does not require access to previously seen data, making it applicable in many practical scenarios. We evaluate GarDA on two datasets with different organs and modalities, where it substantially outperforms existing techniques.

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.