During the deployment of deep neural networks (DNNs) on edge devices, many research efforts are devoted to the limited hardware resource. However, little attention is paid to the influence of dynamic power management. As edge devices typically only have a budget of energy with batteries (rather than almost unlimited energy support on servers or workstations), their dynamic power management often changes the execution frequency as in the widely-used dynamic voltage and frequency scaling (DVFS) technique. This leads to highly unstable inference speed performance, especially for computation-intensive DNN models, which can harm user experience and waste hardware resources. We firstly identify this problem and then propose All-in-One, a highly representative pruning framework to work with dynamic power management using DVFS. The framework can use only one set of model weights and soft masks (together with other auxiliary parameters of negligible storage) to represent multiple models of various pruning ratios. By re-configuring the model to the corresponding pruning ratio for a specific execution frequency (and voltage), we are able to achieve stable inference speed, i.e., keeping the difference in speed performance under various execution frequencies as small as possible. Our experiments demonstrate that our method not only achieves high accuracy for multiple models of different pruning ratios, but also reduces their variance of inference latency for various frequencies, with minimal memory consumption of only one model and one soft mask.

Deep neural networks (DNNs) have rapidly become a \textit{de facto} choice for medical image understanding tasks. However, DNNs are notoriously fragile to the class imbalance in image classification. We further point out that such imbalance fragility can be amplified when it comes to more sophisticated tasks such as pathology localization, as imbalances in such problems can have highly complex and often implicit forms of presence. For example, different pathology can have different sizes or colors (w.r.t.the background), different underlying demographic distributions, and in general different difficulty levels to recognize, even in a meticulously curated balanced distribution of training data. In this paper, we propose to use pruning to automatically and adaptively identify \textit{hard-to-learn} (HTL) training samples, and improve pathology localization by attending them explicitly, during training in \textit{supervised, semi-supervised, and weakly-supervised} settings. Our main inspiration is drawn from the recent finding that deep classification models have difficult-to-memorize samples and those may be effectively exposed through network pruning \cite{hooker2019compressed} - and we extend such observation beyond classification for the first time. We also present an interesting demographic analysis which illustrates HTLs ability to capture complex demographic imbalances. Our extensive experiments on the Skin Lesion Localization task in multiple training settings by paying additional attention to HTLs show significant improvement of localization performance by $\sim$2-3\%.

AI-powered Medical Imaging has recently achieved enormous attention due to its ability to provide fast-paced healthcare diagnoses. However, it usually suffers from a lack of high-quality datasets due to high annotation cost, inter-observer variability, human annotator error, and errors in computer-generated labels. Deep learning models trained on noisy labelled datasets are sensitive to the noise type and lead to less generalization on the unseen samples. To address this challenge, we propose a Robust Stochastic Knowledge Distillation (RoS-KD) framework which mimics the notion of learning a topic from multiple sources to ensure deterrence in learning noisy information. More specifically, RoS-KD learns a smooth, well-informed, and robust student manifold by distilling knowledge from multiple teachers trained on overlapping subsets of training data. Our extensive experiments on popular medical imaging classification tasks (cardiopulmonary disease and lesion classification) using real-world datasets, show the performance benefit of RoS-KD, its ability to distill knowledge from many popular large networks (ResNet-50, DenseNet-121, MobileNet-V2) in a comparatively small network, and its robustness to adversarial attacks (PGD, FSGM). More specifically, RoS-KD achieves >2% and >4% improvement on F1-score for lesion classification and cardiopulmonary disease classification tasks, respectively, when the underlying student is ResNet-18 against recent competitive knowledge distillation baseline. Additionally, on cardiopulmonary disease classification task, RoS-KD outperforms most of the SOTA baselines by ~1% gain in AUC score.

任务概括是自然语言处理（NLP）的漫长挑战。最近的研究试图通过将NLP任务映射到人类可读的提示形式中来提高预训练语言模型的任务概括能力。但是，这些方法需要费力且不灵活的提示，并且在同一下游任务上的不同提示可能会获得不稳定的性能。我们提出了统一的架构提示，这是一种灵活且可扩展的提示方法，该方法会根据任务输入架构自动自动自定义每个任务的可学习提示。它在任务之间建模共享知识，同时保持不同任务架构的特征，从而增强任务概括能力。架构提示采用每个任务的明确数据结构，以制定提示，因此涉及几乎没有人类的努力。为了测试模式提示的任务概括能力，我们对各种一般NLP任务进行基于模式提示的多任务预训练。该框架在从8种任务类型（例如QA，NLI等）的16个看不见的下游任务上实现了强劲的零射击和很少的概括性能。此外，全面的分析证明了每个组件在架构提示中的有效性，其在任务组成性方面的灵活性以及在全DATA微调设置下提高性能的能力。

本文提出了一种新颖的统一特征优化（UFO）范式，用于训练和在现实世界和大规模场景下进行深层模型，这需要集合多个AI功能。不明飞行物的目标是通过对所有任务进行大规模预修。与众所周知的基础模型相比，UFO具有两个不同的重点，即相对较小的模型大小，没有适应性成本：1）UFO以多任务学习方式将广泛的任务挤入中等尺寸的统一模型中并在转移到下游任务时进一步修剪模型大小。 2）不明飞行物不强调转移到新任务。相反，它旨在使修剪模型专门用于一个或多个已经看到的任务。有了这两个特征，UFO为灵活的部署提供了极大的便利，同时保持了大规模预处理的好处。 UFO的一个关键优点是修剪过程不仅可以减少模型的大小和推理消耗，而且还提高了某些任务的准确性。具体而言，UFO考虑了多任务培训，并对统一模型产生了两倍的影响：一些密切相关的任务具有相互利益，而某些任务相互冲突。不明飞行物设法通过新颖的网络体系结构搜索（NAS）方法来减少冲突并保留相互利益。对各种深度表示学习任务（即面部识别，人重新识别，车辆重新识别和产品检索）的实验表明，从UFO中修剪的模型比单件任务训练的对应物更高，但却具有更高的准确性较小的型号大小，验证不明飞行物的概念。此外，UFO还支持发布170亿个参数计算机视觉（CV）基础模型，该模型是该行业中最大的CV模型。

面部影响分析仍然是一项艰巨的任务，其设置从实验室控制到野外情况。在本文中，我们提出了新的框架，以应对第四次情感行为分析（ABAW）竞争的两个挑战：i）多任务学习（MTL）挑战和II）从合成数据（LSD）中学习挑战。对于MTL挑战，我们采用SMM-EmotionNet具有更好的特征向量策略。对于LSD挑战，我们建议采用各自的方法来应对单个标签，不平衡分布，微调限制和模型体系结构的选择。竞争的官方验证集的实验结果表明，我们提出的方法的表现优于基线。该代码可在https://github.com/sylyoung/abaw4-hust-ant上找到。

在深度学习方法进行自动医学图像分析的最新成功之前，从业者使用手工制作的放射线特征来定量描述当地的医学图像斑块。但是，提取区分性放射素特征取决于准确的病理定位，这在现实世界中很难获得。尽管疾病分类和胸部X射线的定位方面取得了进步，但许多方法未能纳入临床知名的领域知识。由于这些原因，我们提出了一个放射素引导的变压器（RGT），该变压器（RGT）与\ textit {global}图像信息与\ textit {local}知识引导的放射线信息信息提供准确的心肺病理学定位和分类\ textit {无需任何界限盒{ }。 RGT由图像变压器分支，放射线变压器分支以及聚集图像和放射线信息的融合层组成。 RGT使用对图像分支的自我注意事项，提取了一个边界框来计算放射线特征，该特征由放射线分支进一步处理。然后通过交叉注意层融合学习的图像和放射线特征。因此，RGT利用了一种新型的端到端反馈回路，该回路只能使用图像水平疾病标签引导精确的病理定位。 NIH CHESTXRAR数据集的实验表明，RGT的表现优于弱监督疾病定位的先前作品（在各个相交联合阈值的平均余量为3.6 \％）和分类（在接收器操作方下平均1.1 \％\％\％\％曲线）。接受代码和训练有素的模型将在接受后发布。

Question Answering (QA) is a longstanding challenge in natural language processing. Existing QA works mostly focus on specific question types, knowledge domains, or reasoning skills. The specialty in QA research hinders systems from modeling commonalities between tasks and generalization for wider applications. To address this issue, we present ProQA, a unified QA paradigm that solves various tasks through a single model. ProQA takes a unified structural prompt as the bridge and improves the QA-centric ability by structural prompt-based pre-training. Through a structurally designed prompt-based input schema, ProQA concurrently models the knowledge generalization for all QA tasks while keeping the knowledge customization for every specific QA task. Furthermore, ProQA is pre-trained with structural prompt-formatted large-scale synthesized corpus, which empowers the model with the commonly-required QA ability. Experimental results on 11 QA benchmarks demonstrate that ProQA consistently boosts performance on both full data fine-tuning, few-shot learning, and zero-shot testing scenarios. Furthermore, ProQA exhibits strong ability in both continual learning and transfer learning by taking the advantages of the structural prompt.

端到端的语音到语音翻译（S2ST）而不依赖中间文本表示是一个快速新兴的研究领域。最近的作品表明，这种直接S2ST系统的性能正在接近常规级联S2ST时，在可比较的数据集中进行了培训。但是，实际上，直接S2ST的性能受到配对S2ST培训数据的可用性。在这项工作中，我们探索了多种方法，用于利用更广泛的无监督和弱监督的语音和文本数据，以改善基于Translatotron 2的直接S2ST的性能2.使用我们最有效的方法，我们的最有效的方法是21号直接S2ST的平均翻译质量与没有其他数据的先前最新的训练相比，CVSS-C语料库上的语言对改善了+13.6 BLEU（OR +113％）。低资源语言的改进更加显着（平均+398％）。我们的比较研究表明，S2ST和语音表示学习的未来研究方向。

放射学报告生成旨在产生计算机辅助诊断，以缓解放射科医生的工作量，并最近引起了越来越长的关注。然而，之前的深度学习方法倾向于忽视医学发现之间的相互影响，这可以是限制所生成的报告质量的瓶颈。在这项工作中，我们建议在信息知识图表中提出和代表医学发现的协会，并将此事先知识纳入放射学报告，以帮助提高所生成的报告质量。实验结果证明了我们在IU X射线数据集上的提出方法的优越性，Rouge-L为0.384 $ \ PM $ 0.007和0.340 $ \ PM $ 0.011。与以前的作品相比，我们的模型平均实现了1.6％（苹果酒和Rouge-L的增加2.0％和1.5％）。实验表明，先验知识可以为准确的放射学报告生成表现收益。我们将在https://github.com/bionlplab/report_generation_amia2022中公开公开可用的代码。