Prior work on ideology prediction has largely focused on single modalities, i.e., text or images. In this work, we introduce the task of multimodal ideology prediction, where a model predicts binary or five-point scale ideological leanings, given a text-image pair with political content. We first collect five new large-scale datasets with English documents and images along with their ideological leanings, covering news articles from a wide range of US mainstream media and social media posts from Reddit and Twitter. We conduct in-depth analyses of news articles and reveal differences in image content and usage across the political spectrum. Furthermore, we perform extensive experiments and ablation studies, demonstrating the effectiveness of targeted pretraining objectives on different model components. Our best-performing model, a late-fusion architecture pretrained with a triplet objective over multimodal content, outperforms the state-of-the-art text-only model by almost 4% and a strong multimodal baseline with no pretraining by over 3%.

临床问题应答（QA）旨在根据临床文本自动回答医疗专业人员的问题。研究表明，在一个语料库上培训的神经QA模型可能对来自不同研究所或不同患者组的新临床文本概括，其中大规模的QA对不容易获得模型再培训。为了解决这一挑战，我们提出了一个简单但有效的框架CliniQG4QA，它利用问题生成（QG）在新的临床环境中综合QA对，并在不需要手动注释的情况下提升QA模型。为了生成对训练QA模型至关重要的不同类型的问题，我们进一步引入了基于SEQ2SEQ的问题短语预测（QPP）模块，可以与大多数现有的QG模型一起使用以使生成多样化。我们的综合实验结果表明，我们的框架产生的QA​​语料库可以改善新上下文的QA模型（在完全匹配方面最高8％的绝对增益），QPP模块在实现增益方面发挥着至关重要的作用。

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

The selection of an optimal pacing site, which is ideally scar-free and late activated, is critical to the response of cardiac resynchronization therapy (CRT). Despite the success of current approaches formulating the detection of such late mechanical activation (LMA) regions as a problem of activation time regression, their accuracy remains unsatisfactory, particularly in cases where myocardial scar exists. To address this issue, this paper introduces a multi-task deep learning framework that simultaneously estimates LMA amount and classify the scar-free LMA regions based on cine displacement encoding with stimulated echoes (DENSE) magnetic resonance imaging (MRI). With a newly introduced auxiliary LMA region classification sub-network, our proposed model shows more robustness to the complex pattern cause by myocardial scar, significantly eliminates their negative effects in LMA detection, and in turn improves the performance of scar classification. To evaluate the effectiveness of our method, we tests our model on real cardiac MR images and compare the predicted LMA with the state-of-the-art approaches. It shows that our approach achieves substantially increased accuracy. In addition, we employ the gradient-weighted class activation mapping (Grad-CAM) to visualize the feature maps learned by all methods. Experimental results suggest that our proposed model better recognizes the LMA region pattern.

在多输入多输出（MIMO）系统中使用深度自动码器（DAE）进行端到端通信，是一种具有重要潜力的新概念。在误码率（BER）方面，已示出DAE-ADED MIMO以占地识别的奇异值分解（SVD）为基础的预编码MIMO。本文提出将信道矩阵的左右奇异矢量嵌入到DAE编码器和解码器中，以进一步提高MIMO空间复用的性能。 SVD嵌入式DAE主要优于BER的理论线性预编码。这是显着的，因为它表明所提出的DAES通过将通信系统视为单个端到端优化块来超出当前系统设计的极限。基于仿真结果，在SNR = 10dB，所提出的SVD嵌入式设计可以实现近10美元，并将BER减少至少10次，而没有SVD，相比增长了18倍的增长率最高18倍具有理论线性预编码。我们将这一点归因于所提出的DAE可以将输入和输出与具有有限字母输入的自适应调制结构匹配。我们还观察到添加到DAE的剩余连接进一步提高了性能。

We present an extension to masked autoencoders (MAE) which improves on the representations learnt by the model by explicitly encouraging the learning of higher scene-level features. We do this by: (i) the introduction of a perceptual similarity term between generated and real images (ii) incorporating several techniques from the adversarial training literature including multi-scale training and adaptive discriminator augmentation. The combination of these results in not only better pixel reconstruction but also representations which appear to capture better higher-level details within images. More consequentially, we show how our method, Perceptual MAE, leads to better performance when used for downstream tasks outperforming previous methods. We achieve 78.1% top-1 accuracy linear probing on ImageNet-1K and up to 88.1% when fine-tuning, with similar results for other downstream tasks, all without use of additional pre-trained models or data.

Learning good representation of giga-pixel level whole slide pathology images (WSI) for downstream tasks is critical. Previous studies employ multiple instance learning (MIL) to represent WSIs as bags of sampled patches because, for most occasions, only slide-level labels are available, and only a tiny region of the WSI is disease-positive area. However, WSI representation learning still remains an open problem due to: (1) patch sampling on a higher resolution may be incapable of depicting microenvironment information such as the relative position between the tumor cells and surrounding tissues, while patches at lower resolution lose the fine-grained detail; (2) extracting patches from giant WSI results in large bag size, which tremendously increases the computational cost. To solve the problems, this paper proposes a hierarchical-based multimodal transformer framework that learns a hierarchical mapping between pathology images and corresponding genes. Precisely, we randomly extract instant-level patch features from WSIs with different magnification. Then a co-attention mapping between imaging and genomics is learned to uncover the pairwise interaction and reduce the space complexity of imaging features. Such early fusion makes it computationally feasible to use MIL Transformer for the survival prediction task. Our architecture requires fewer GPU resources compared with benchmark methods while maintaining better WSI representation ability. We evaluate our approach on five cancer types from the Cancer Genome Atlas database and achieved an average c-index of $0.673$, outperforming the state-of-the-art multimodality methods.

Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic features are poorly defined. Here, we present a method for improving explainability of DNN models using synthetic histology generated by a conditional generative adversarial network (cGAN). We show that cGANs generate high-quality synthetic histology images that can be leveraged for explaining DNN models trained to classify molecularly-subtyped tumors, exposing histologic features associated with molecular state. Fine-tuning synthetic histology through class and layer blending illustrates nuanced morphologic differences between tumor subtypes. Finally, we demonstrate the use of synthetic histology for augmenting pathologist-in-training education, showing that these intuitive visualizations can reinforce and improve understanding of histologic manifestations of tumor biology.

Human behavior emerges from planning over elaborate decompositions of tasks into goals, subgoals, and low-level actions. How are these decompositions created and used? Here, we propose and evaluate a normative framework for task decomposition based on the simple idea that people decompose tasks to reduce the overall cost of planning while maintaining task performance. Analyzing 11,117 distinct graph-structured planning tasks, we find that our framework justifies several existing heuristics for task decomposition and makes predictions that can be distinguished from two alternative normative accounts. We report a behavioral study of task decomposition ($N=806$) that uses 30 randomly sampled graphs, a larger and more diverse set than that of any previous behavioral study on this topic. We find that human responses are more consistent with our framework for task decomposition than alternative normative accounts and are most consistent with a heuristic -- betweenness centrality -- that is justified by our approach. Taken together, our results provide new theoretical insight into the computational principles underlying the intelligent structuring of goal-directed behavior.

视频中的人类对象相互作用（HOI）识别对于分析人类活动很重要。在现实世界中，大多数关注视觉特征的工作通常都会受到阻塞。当HOI中有多个人和物体涉及时，这种问题将更加复杂。考虑到诸如人类姿势和物体位置之类的几何特征提供有意义的信息来了解HOI，我们认为将视觉和几何特征的好处结合在HOI识别中，并提出了一个新颖的两级几何形状特征信息信息图形卷积（2G） -GCN）。几何级图模拟了人类和对象的几何特征之间的相互依赖性，而融合级别的图将它们与人类和对象的视觉特征融合在一起。为了证明我们方法在挑战性场景中的新颖性和有效性，我们提出了一个新的多人HOI数据集（Mphoi-72）。关于Mphoi-72（多人HOI），CAD-1220（单人HOI）和双人动作（双手HOI）数据集的广泛实验证明了我们的表现与最先进的表现相比。