不断增加的材料科学文章使得很难从已发表的文献中推断化学结构 - 培训关系。我们使用自然语言处理（NLP）方法从聚合物文献的摘要中自动提取材料属性数据。作为我们管道的组成部分，我们使用240万材料科学摘要培训了一种语言模型的材料，该材料模型在用作文本编码器时，在五分之三命名实体识别数据集中的其他基线模型都优于其他基线模型。使用此管道，我们在60小时内从约130,000个摘要中获得了约300,000个物质记录。分析了提取的数据，分析了各种应用，例如燃料电池，超级电容器和聚合物太阳能电池，以恢复非平凡的见解。通过我们的管道提取的数据可通过https://polymerscholar.org的Web平台提供，该数据可方便地定位摘要中记录的材料属性数据。这项工作证明了自动管道的可行性，该管道从已发布的文献开始，并以一组完整的提取物质属性信息结束。

Can we take a recurrent neural network (RNN) trained to translate between languages and augment it to support a new natural language without retraining the model from scratch? Can we fix the faulty behavior of the RNN by replacing portions associated with the faulty behavior? Recent works on decomposing a fully connected neural network (FCNN) and convolutional neural network (CNN) into modules have shown the value of engineering deep models in this manner, which is standard in traditional SE but foreign for deep learning models. However, prior works focus on the image-based multiclass classification problems and cannot be applied to RNN due to (a) different layer structures, (b) loop structures, (c) different types of input-output architectures, and (d) usage of both nonlinear and logistic activation functions. In this work, we propose the first approach to decompose an RNN into modules. We study different types of RNNs, i.e., Vanilla, LSTM, and GRU. Further, we show how such RNN modules can be reused and replaced in various scenarios. We evaluate our approach against 5 canonical datasets (i.e., Math QA, Brown Corpus, Wiki-toxicity, Clinc OOS, and Tatoeba) and 4 model variants for each dataset. We found that decomposing a trained model has a small cost (Accuracy: -0.6%, BLEU score: +0.10%). Also, the decomposed modules can be reused and replaced without needing to retrain.

Fairness of machine learning (ML) software has become a major concern in the recent past. Although recent research on testing and improving fairness have demonstrated impact on real-world software, providing fairness guarantee in practice is still lacking. Certification of ML models is challenging because of the complex decision-making process of the models. In this paper, we proposed Fairify, an SMT-based approach to verify individual fairness property in neural network (NN) models. Individual fairness ensures that any two similar individuals get similar treatment irrespective of their protected attributes e.g., race, sex, age. Verifying this fairness property is hard because of the global checking and non-linear computation nodes in NN. We proposed sound approach to make individual fairness verification tractable for the developers. The key idea is that many neurons in the NN always remain inactive when a smaller part of the input domain is considered. So, Fairify leverages whitebox access to the models in production and then apply formal analysis based pruning. Our approach adopts input partitioning and then prunes the NN for each partition to provide fairness certification or counterexample. We leveraged interval arithmetic and activation heuristic of the neurons to perform the pruning as necessary. We evaluated Fairify on 25 real-world neural networks collected from four different sources, and demonstrated the effectiveness, scalability and performance over baseline and closely related work. Fairify is also configurable based on the domain and size of the NN. Our novel formulation of the problem can answer targeted verification queries with relaxations and counterexamples, which have practical implications.

Machine Learning (ML) software has been widely adopted in modern society, with reported fairness implications for minority groups based on race, sex, age, etc. Many recent works have proposed methods to measure and mitigate algorithmic bias in ML models. The existing approaches focus on single classifier-based ML models. However, real-world ML models are often composed of multiple independent or dependent learners in an ensemble (e.g., Random Forest), where the fairness composes in a non-trivial way. How does fairness compose in ensembles? What are the fairness impacts of the learners on the ultimate fairness of the ensemble? Can fair learners result in an unfair ensemble? Furthermore, studies have shown that hyperparameters influence the fairness of ML models. Ensemble hyperparameters are more complex since they affect how learners are combined in different categories of ensembles. Understanding the impact of ensemble hyperparameters on fairness will help programmers design fair ensembles. Today, we do not understand these fully for different ensemble algorithms. In this paper, we comprehensively study popular real-world ensembles: bagging, boosting, stacking and voting. We have developed a benchmark of 168 ensemble models collected from Kaggle on four popular fairness datasets. We use existing fairness metrics to understand the composition of fairness. Our results show that ensembles can be designed to be fairer without using mitigation techniques. We also identify the interplay between fairness composition and data characteristics to guide fair ensemble design. Finally, our benchmark can be leveraged for further research on fair ensembles. To the best of our knowledge, this is one of the first and largest studies on fairness composition in ensembles yet presented in the literature.

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.

自2016年成立以来，Alexa奖计划使数百名大学生能够通过Socialbot Grand Challenge探索和竞争以发展对话代理商。挑战的目的是建立能够与人类在流行主题上连贯而诱人的代理人20分钟，同时达到至少4.0/5.0的平均评分。但是，由于对话代理商试图帮助用户完成日益复杂的任务，因此需要新的对话AI技术和评估平台。成立于2021年的Alexa奖Taskbot Challenge建立在Socialbot Challenge的成功基础上，通过引入交互式协助人类进行现实世界烹饪和做自己动手做的任务的要求，同时同时使用语音和视觉方式。这项挑战要求TaskBots识别和理解用户的需求，识别和集成任务和域知识，并开发新的方式，不分散用户的注意力，而不必分散他们的任务，以及其他挑战。本文概述了Taskbot挑战赛，描述了使用Cobot Toolkit提供给团队提供的基础架构支持，并总结了参与团队以克服研究挑战所采取的方法。最后，它分析了比赛第一年的竞争任务机器人的性能。

近年来，深度学习（DL）算法的使用改善了基于视觉的空间应用的性能。但是，生成大量的注释数据来培训这些DL算法已被证明具有挑战性。虽然可以使用合成生成的图像，但在实际环境中测试时，经过合成数据训练的DL模型通常容易受到性能降解。在这种情况下，卢森堡大学的安全，可靠性和信任（SNT）跨学科中心开发了“ SNT Zero-G Lab”，用于在模拟现实世界太空环境的条件下培训和验证基于视觉的空间算法。 SNT Zero-G实验室开发的一个重要方面是设备选择。从实验室开发过程中学到的经验教训，本文提出了一种系统的方法，将市场调查和设备选择的实验分析结合在一起。特别是，本文专注于太空实验室中的图像采集设备：背景材料，相机和照明灯。实验分析的结果表明，在太空实验室开发项目中选择有效的设备选择需要通过实验分析来称赞的市场调查。

口服食物挑战（OFC）对于准确诊断患者的食物过敏至关重要。但是，患者不愿接受OFC，对于那些这样做的患者，在农村/社区医疗保健环境中，对过敏症患者的使用率有限。通过机器学习方法对OFC结果的预测可以促进在家中食品过敏原的删除，在OFC中改善患者和医师的舒适度，并通过最大程度地减少执行的OFC的数量来节省医疗资源。临床数据是从共同接受1,284个OFC的1,12例患者那里收集的，包括临床因素，包括血清特异性IgE，总IgE，皮肤刺测试（SPTS），症状，性别和年龄。使用这些临床特征，构建了机器学习模型，以预测花生，鸡蛋和牛奶挑战的结果。每种过敏原的最佳性能模型是使用凹入和凸内核（LUCCK）方法创建的，该方法在曲线（AUC）（AUC）下分别用于花生，鸡蛋和牛奶OFC预测为0.76、0.68和0.70， 。通过Shapley添加说明（SHAP）的模型解释表明，特定的IgE以及SPTS的Wheal和Flare值高度预测了OFC结果。该分析的结果表明，机器学习有可能预测OFC结果，并揭示了相关的临床因素进行进一步研究。

我们开发了BenchPress，这是第一个用于编译器的ML基准生成器，它是在源代码的功能空间表示中可检测的。卧推通过在空序列或现有序列的任何部分中添加新代码，通过共同观察其左和右下文，从而综合编译函数，从而达到出色的汇编速率。卧推操纵基准的生成迈向了所需的目标特征，这对于最先进的合成器（或实际上人类）不可能达到。与（a）clgen-最先进的ML合成器，（b）Clsmith Fuzzer，（c）Srciror Mutator或（d）人写代码相比来自Github。 Benchpress是第一个通过主动学习搜索功能空间的生成器，以生成可以改善下游任务的基准。我们展示了Grewe's等人如何使用台式。与其他技术相比，CPU与GPU启发式模型在台式基准测试中进行训练时可以获得更高的加速。卧推是一个强大的代码生成器：其生成的样品以86％的速度编译，而Clgen的2.33％则以86％的速度编译。从一个空的固定输入开始，台式比CLGEN产生的10倍，可汇编的OpenCL基准测试，这些基准比Clgen更大，并且更具多样性。

一个沿着城市街道行走的人试图对世界各个方面进行建模，这很快就会被许多商店，汽车和人们遵循自己的复杂且难以理解的动态所淹没。在这种环境中的探索和导航是一项日常任务，不需要大量精神资源。是否可以将这种感官信息的消防软管转变为最小的潜在状态，这是代理在世界上成功采取行动的必要和足够的？我们具体地提出了这个问题，并提出了可控制的状态发现算法（AC-State），该算法具有理论保证，并且实际上被证明可以发现\ textit {最小可控的潜在状态}，其中包含所有用于控制控制的信息代理，同时完全丢弃所有无关的信息。该算法由一个具有信息瓶颈的多步逆模型（预测遥远观察结果的动作）组成。 AC-State可以在没有奖励或示威的情况下实现本地化，探索和导航。我们证明了在三个领域中发现可控潜在状态的发现：将机器人组分散注意力（例如，照明条件和背景变化），与其他代理商一起在迷宫中进行探索，并在Matterport House Simulator中导航。