Detecting personal health mentions on social media is essential to complement existing health surveillance systems. However, annotating data for detecting health mentions at a large scale is a challenging task. This research employs a multitask learning framework to leverage available annotated data from a related task to improve the performance on the main task to detect personal health experiences mentioned in social media texts. Specifically, we focus on incorporating emotional information into our target task by using emotion detection as an auxiliary task. Our approach significantly improves a wide range of personal health mention detection tasks compared to a strong state-of-the-art baseline.

The health mention classification (HMC) task is the process of identifying and classifying mentions of health-related concepts in text. This can be useful for identifying and tracking the spread of diseases through social media posts. However, this is a non-trivial task. Here we build on recent studies suggesting that using emotional information may improve upon this task. Our study results in a framework for health mention classification that incorporates affective features. We present two methods, an intermediate task fine-tuning approach (implicit) and a multi-feature fusion approach (explicit) to incorporate emotions into our target task of HMC. We evaluated our approach on 5 HMC-related datasets from different social media platforms including three from Twitter, one from Reddit and another from a combination of social media sources. Extensive experiments demonstrate that our approach results in statistically significant performance gains on HMC tasks. By using the multi-feature fusion approach, we achieve at least a 3% improvement in F1 score over BERT baselines across all datasets. We also show that considering only negative emotions does not significantly affect performance on the HMC task. Additionally, our results indicate that HMC models infused with emotional knowledge are an effective alternative, especially when other HMC datasets are unavailable for domain-specific fine-tuning. The source code for our models is freely available at https://github.com/tahirlanre/Emotion_PHM.

与自然语言处理的XAI旨在产生可读的解释，作为AI决策的证据，以解决解释性和透明度。但是，从HCI的角度来看，当前的方法仅着眼于提供单一的解释，该解释无法解决人类思想和语言经验的多样性。因此，本文通过提出一个生成XAI框架，交互来解决此差距（解释并预测与上下文条件变分自动编码器查询）。我们的新框架分为两个步骤提供了解释：（一步）解释和标签预测； （第二步）各种证据生成。我们在基准数据集E-SNLI上对变压器体系结构进行密集实验。我们的方法在第一步中，针对解释生成（BLEU的增长率高达4.7％）的最先进基线模型的竞争性或更好的表现；它还可以在第二步中产生多种不同的解释。

Spatial perception is a key task in several robotics applications. In general, it involves the nonlinear estimation of hidden variables that represent the state of the robot/environment. However, in the presence of outliers the standard nonlinear least squared formulation results in poor estimates. Several methods have been considered in the literature to improve the reliability of the estimation process. Most methods are based on heuristics since guaranteed global robust estimation is not generally practical due to high computational costs. Recently general purpose robust estimation heuristics have been proposed that leverage existing non-minimal solvers available for the outlier-free formulations without the need for an initial guess. In this work, we propose two similar heuristics backed by Bayesian theory. We evaluate these heuristics in practical scenarios to demonstrate their merits in different applications including 3D point cloud registration, mesh registration and pose graph optimization.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

Machine learning algorithms typically assume that the training and test samples come from the same distributions, i.e., in-distribution. However, in open-world scenarios, streaming big data can be Out-Of-Distribution (OOD), rendering these algorithms ineffective. Prior solutions to the OOD challenge seek to identify invariant features across different training domains. The underlying assumption is that these invariant features should also work reasonably well in the unlabeled target domain. By contrast, this work is interested in the domain-specific features that include both invariant features and features unique to the target domain. We propose a simple yet effective approach that relies on correlations in general regardless of whether the features are invariant or not. Our approach uses the most confidently predicted samples identified by an OOD base model (teacher model) to train a new model (student model) that effectively adapts to the target domain. Empirical evaluations on benchmark datasets show that the performance is improved over the SOTA by ~10-20%

Low-rank and sparse decomposition based methods find their use in many applications involving background modeling such as clutter suppression and object tracking. While Robust Principal Component Analysis (RPCA) has achieved great success in performing this task, it can take hundreds of iterations to converge and its performance decreases in the presence of different phenomena such as occlusion, jitter and fast motion. The recently proposed deep unfolded networks, on the other hand, have demonstrated better accuracy and improved convergence over both their iterative equivalents as well as over other neural network architectures. In this work, we propose a novel deep unfolded spatiotemporal RPCA (DUST-RPCA) network, which explicitly takes advantage of the spatial and temporal continuity in the low-rank component. Our experimental results on the moving MNIST dataset indicate that DUST-RPCA gives better accuracy when compared with the existing state of the art deep unfolded RPCA networks.

Early evaluation of patients who require special care and who have high death-expectancy in COVID-19, and the effective determination of relevant biomarkers on large sample-groups are important to reduce mortality. This study aimed to reveal the routine blood-value predictors of COVID-19 mortality and to determine the lethal-risk levels of these predictors during the disease process. The dataset of the study consists of 38 routine blood-values of 2597 patients who died (n = 233) and those who recovered (n = 2364) from COVID-19 in August-December, 2021. In this study, the histogram-based gradient-boosting (HGB) model was the most successful machine-learning classifier in detecting living and deceased COVID-19 patients (with squared F1 metrics F1^2 = 1). The most efficient binary combinations with procalcitonin were obtained with D-dimer, ESR, D-Bil and ferritin. The HGB model operated with these feature pairs correctly detected almost all of the patients who survived and those who died (precision > 0.98, recall > 0.98, F1^2 > 0.98). Furthermore, in the HGB model operated with a single feature, the most efficient features were procalcitonin (F1^2 = 0.96) and ferritin (F1^2 = 0.91). In addition, according to the two-threshold approach, ferritin values between 376.2 mkg/L and 396.0 mkg/L (F1^2 = 0.91) and pro-calcitonin values between 0.2 mkg/L and 5.2 mkg/L (F1^2 = 0.95) were found to be fatal risk levels for COVID-19. Considering all the results, we suggest that many features combined with these features, especially procalcitonin and ferritin, operated with the HGB model, can be used to achieve very successful results in the classification of those who live, and those who die from COVID-19. Moreover, we strongly recommend that clinicians consider the critical levels we have found for procalcitonin and ferritin properties, to reduce the lethality of the COVID-19 disease.

恢复质量差的图像与一组混合伪影对于可靠的诊断起着至关重要的作用。现有的研究集中在特定的恢复问题上，例如图像过度，去核和暴露校正，通常对伪影类型和严重性有很强的假设。作为盲X射线恢复的先驱研究，我们提出了一个通用图像恢复和分类的联合模型：恢复分类为分类的生成对抗网络（R2C-GAN）。这种共同优化的模型使恢复后保持任何疾病完整。因此，由于X射线图像质量的提高，这自然会导致更高的诊断性能。为了实现这一关键目标，我们将恢复任务定义为图像到图像的翻译问题，从差异，模糊或暴露不足/暴露不足的图像到高质量的图像域。提出的R2C-GAN模型能够使用未配对的训练样本在两个域之间学习前进和逆变换。同时，联合分类在恢复过程中保留了疾病标签。此外，R2C-GAN配备了操作层/神经元，可降低网络深度，并进一步增强恢复和分类性能。拟议的联合模型对2019年冠状病毒病（COVID-19）分类的卡塔-COV19数据集进行了广泛的评估。拟议的恢复方法达到了90％以上的F1得分，这显着高于任何深层模型的性能。此外，在定性分析中，R2C-GAN的恢复性能得到了一群医生的批准。我们在https://github.com/meteahishali/r2c-gan上共享软件实施。

大型人口系统的分析和控制对研究和工程的各个领域引起了极大的兴趣，从机器人群的流行病学到经济学和金融。一种越来越流行和有效的方法来实现多代理系统中的顺序决策，这是通过多机构增强学习，因为它允许对高度复杂的系统进行自动和无模型的分析。但是，可伸缩性的关键问题使控制和增强学习算法的设计变得复杂，尤其是在具有大量代理的系统中。尽管强化学习在许多情况下都发现了经验成功，但许多代理商的问题很快就变得棘手了，需要特别考虑。在这项调查中，我们将阐明当前的方法，以通过多代理强化学习以及通过诸如平均场游戏，集体智能或复杂的网络理论等研究领域进行仔细理解和分析大型人口系统。这些经典独立的主题领域提供了多种理解或建模大型人口系统的方法，这可能非常适合将来的可拖动MARL算法制定。最后，我们调查了大规模控制的潜在应用领域，并确定了实用系统中学习算法的富有成果的未来应用。我们希望我们的调查可以为理论和应用科学的初级和高级研究人员提供洞察力和未来的方向。