在本文中，我们提出了帕托 - 一种可生产性感知拓扑优化（至）框架，以帮助有效地探索使用金属添加剂制造（AM）制造的部件的设计空间，同时确保相对于裂化的可制造性。具体地，通过激光粉末融合制造的部件由于从构建过程中产生的陡峭热梯度产生的高残余应力值而易于诸如翘曲或裂缝的缺陷。为这些零件的设计成熟并规划其制作可能跨越几年，通常涉及设计和制造工程师之间的多种切换。帕托基于先验的无裂缝设计的发现，使得优化部分可以在一开始就自由缺陷。为确保设计在优化期间无裂缝，可以在使用裂缝指数的标准制剂中明确地编码生产性。探索多个裂缝指数并使用实验验证，最大剪切应变指数（MSSI）被显示为准确的裂缝指数。模拟构建过程是耦合的多物理计算，并将其结合在循环中可以计算上禁止。我们利用了深度卷积神经网络的当前进步，并基于基于关注的U-Net架构的高保真代理模型，以将MSSI值预测为部分域上的空间变化的字段。此外，我们采用自动差异来直接计算关于输入设计变量的最大MSSI的梯度，并使用基于性能的灵敏度字段增强，以优化设计，同时考虑重量，可制造性和功能之间的权衡。我们通过3D基准研究以及实验验证来证明所提出的方法的有效性。

Climate change, population growth, and water scarcity present unprecedented challenges for agriculture. This project aims to forecast soil moisture using domain knowledge and machine learning for crop management decisions that enable sustainable farming. Traditional methods for predicting hydrological response features require significant computational time and expertise. Recent work has implemented machine learning models as a tool for forecasting hydrological response features, but these models neglect a crucial component of traditional hydrological modeling that spatially close units can have vastly different hydrological responses. In traditional hydrological modeling, units with similar hydrological properties are grouped together and share model parameters regardless of their spatial proximity. Inspired by this domain knowledge, we have constructed a novel domain-inspired temporal graph convolution neural network. Our approach involves clustering units based on time-varying hydrological properties, constructing graph topologies for each cluster, and forecasting soil moisture using graph convolutions and a gated recurrent neural network. We have trained, validated, and tested our method on field-scale time series data consisting of approximately 99,000 hydrological response units spanning 40 years in a case study in northeastern United States. Comparison with existing models illustrates the effectiveness of using domain-inspired clustering with time series graph neural networks. The framework is being deployed as part of a pro bono social impact program. The trained models are being deployed on small-holding farms in central Texas.

As Artificial and Robotic Systems are increasingly deployed and relied upon for real-world applications, it is important that they exhibit the ability to continually learn and adapt in dynamically-changing environments, becoming Lifelong Learning Machines. Continual/lifelong learning (LL) involves minimizing catastrophic forgetting of old tasks while maximizing a model's capability to learn new tasks. This paper addresses the challenging lifelong reinforcement learning (L2RL) setting. Pushing the state-of-the-art forward in L2RL and making L2RL useful for practical applications requires more than developing individual L2RL algorithms; it requires making progress at the systems-level, especially research into the non-trivial problem of how to integrate multiple L2RL algorithms into a common framework. In this paper, we introduce the Lifelong Reinforcement Learning Components Framework (L2RLCF), which standardizes L2RL systems and assimilates different continual learning components (each addressing different aspects of the lifelong learning problem) into a unified system. As an instantiation of L2RLCF, we develop a standard API allowing easy integration of novel lifelong learning components. We describe a case study that demonstrates how multiple independently-developed LL components can be integrated into a single realized system. We also introduce an evaluation environment in order to measure the effect of combining various system components. Our evaluation environment employs different LL scenarios (sequences of tasks) consisting of Starcraft-2 minigames and allows for the fair, comprehensive, and quantitative comparison of different combinations of components within a challenging common evaluation environment.

Next-generation sequencing technologies have enhanced the scope of Internet-of-Things (IoT) to include genomics for personalized medicine through the increased availability of an abundance of genome data collected from heterogeneous sources at a reduced cost. Given the sheer magnitude of the collected data and the significant challenges offered by the presence of highly similar genomic structure across species, there is a need for robust, scalable analysis platforms to extract actionable knowledge such as the presence of potentially zoonotic pathogens. The emergence of zoonotic diseases from novel pathogens, such as the influenza virus in 1918 and SARS-CoV-2 in 2019 that can jump species barriers and lead to pandemic underscores the need for scalable metagenome analysis. In this work, we propose MG2Vec, a deep learning-based solution that uses the transformer network as its backbone, to learn robust features from raw metagenome sequences for downstream biomedical tasks such as targeted and generalized pathogen detection. Extensive experiments on four increasingly challenging, yet realistic diagnostic settings, show that the proposed approach can help detect pathogens from uncurated, real-world clinical samples with minimal human supervision in the form of labels. Further, we demonstrate that the learned representations can generalize to completely unrelated pathogens across diseases and species for large-scale metagenome analysis. We provide a comprehensive evaluation of a novel representation learning framework for metagenome-based disease diagnostics with deep learning and provide a way forward for extracting and using robust vector representations from low-cost next generation sequencing to develop generalizable diagnostic tools.

Scene graphs provide a rich, structured representation of a scene by encoding the entities (objects) and their spatial relationships in a graphical format. This representation has proven useful in several tasks, such as question answering, captioning, and even object detection, to name a few. Current approaches take a generation-by-classification approach where the scene graph is generated through labeling of all possible edges between objects in a scene, which adds computational overhead to the approach. This work introduces a generative transformer-based approach to generating scene graphs beyond link prediction. Using two transformer-based components, we first sample a possible scene graph structure from detected objects and their visual features. We then perform predicate classification on the sampled edges to generate the final scene graph. This approach allows us to efficiently generate scene graphs from images with minimal inference overhead. Extensive experiments on the Visual Genome dataset demonstrate the efficiency of the proposed approach. Without bells and whistles, we obtain, on average, 20.7% mean recall (mR@100) across different settings for scene graph generation (SGG), outperforming state-of-the-art SGG approaches while offering competitive performance to unbiased SGG approaches.

In this work, we present an evaluation of smaller BLOOM model variants (350m/560m and 1b3/1b7) on various natural language processing tasks. This includes GLUE - language understanding, prompt-based zero-shot and few-shot text classification and extraction, question answering, prompt-based text generation, and multi-lingual text classification to understand model strengths/weaknesses and behavior. Empirical results show that BLOOM variants under-perform on all GLUE tasks (except WNLI), question-answering, and text generation. The variants bloom for WNLI, with an accuracy of 56.3%, and for prompt-based few-shot text extraction on MIT Movies and ATIS datasets. The BLOOM variants on average have 7% greater accuracy over GPT-2 and GPT-Neo models on Director and Airline Name extraction from MIT Movies and ATIS datasets, respectively.

Identification of named entities from legal texts is an essential building block for developing other legal Artificial Intelligence applications. Named Entities in legal texts are slightly different and more fine-grained than commonly used named entities like Person, Organization, Location etc. In this paper, we introduce a new corpus of 46545 annotated legal named entities mapped to 14 legal entity types. The Baseline model for extracting legal named entities from judgment text is also developed.

场景变化检测（SCD）是一项关键的感知任务，通过比较在不同时间捕获的场景来确定变化。 SCD由于嘈杂的照明，季节性变化和两次观点的透视差异而具有挑战性。基于深度神经网络的解决方案需要大量的注释数据，这些数据乏味而昂贵。另一方面，从大型数据集中传输学习会导致域移动。为了应对这些挑战，我们提出了一种新颖的\ textit {差异自我监督预审（DSP）}方法，该方法使用特征差异来学习与变化区域相对应的歧视性表示，同时通过跨视图来实现时间不变性来解决嘈杂的变化。我们对SCD数据集的实验结果证明了我们方法的有效性，特别是在摄像机观点和照明条件下的差异。与使用超过一百万个标记的图像的自我监督的Barlow双胞胎和标准图像预处理相比，DSP可以超过它而无需使用任何其他数据。我们的结果还证明了DSP对自然腐败，分配转移和学习有限的数据的鲁棒性。

应对深层终身强化学习（LRL）挑战的一种方法是仔细管理代理商的学习经验，以学习（不忘记）并建立内部元模型（任务，环境，代理商和世界）。生成重播（GR）是一种以生物学启发的重播机制，可以通过从内部生成模型中绘制的自标记示例来增强学习经验，该模型随着时间的推移而更新。在本文中，我们提出了一个满足两个Desiderata的GR版本：（a）使用深RL学习的策略的潜在策略的内省密度建模，以及（b）无模型的端到端学习。在这项工作中，我们研究了三个无模型GR的深度学习体系结构。我们在三种不同的情况下评估了我们提出的算法，其中包括来自Starcraft2和Minigrid域的任务。我们报告了几个关键发现，显示了设计选择对定量指标的影响，包括转移学习，对看不见的任务的概括，任务更改后的快速适应，与任务专家相当的绩效以及最小化灾难性遗忘。我们观察到我们的GR可以防止从深层批评剂的潜在矢量空间中的特征映射中漂移。我们还显示了既定的终身学习指标的改进。我们发现，当与重播缓冲液和生成的重播缓冲液结合使用时，需要引入一个小的随机重放缓冲液，以显着提高训练的稳定性。总体而言，我们发现“隐藏的重播”（一种众所周知的班级入学分类体系结构）是最有前途的方法，它推动了LRL的GR中最新的方法。

在本文中，我们介绍了战术边缘（水合物）的高维可重构分析，使用低S型嵌入式硬件可以在利用非MAC的边缘进行实时重新配置（不含浮点多裂动作）（无浮点多裂动作）（深神经网络）（ DNN）结合了高度（HD）计算加速器。我们描述了算法，经过训练的量化模型生成以及功能提取器的模拟性能，不含多重蓄能的供您喂养基于高维逻辑的分类器。然后，我们展示了性能如何随着超数的数量而增加。我们将与传统DNN相比，描述已实现的低压FPGA硬件和嵌入式软件系统，并详细介绍实现的硬件加速器。我们讨论了测量的系统延迟和功率，由于使用可学习的量化和高清计算而引起的噪声稳健性，用于视频活动分类任务的实际和模拟系统性能以及在同一数据集上进行重新配置的演示。我们表明，仅使用梯度下降反向传播（无梯度）的馈电HD分类器（无梯度），可以通过使用几乎没有射击的新课程来实现现场的可重构性。最初的工作使用了LRCN DNN，目前已扩展到使用具有改进性能的两流DNN。