注意缺陷/多动症（ADHD）是一种神经发育障碍，高度流行，需要临床专家才能诊断。众所周知，个人的观察行为反映在眼睛运动中，直接与注意机制和高阶认知过程有关。因此，我们探讨了是否可以根据记录的眼动动作以及在免费观看任务中的视频刺激信息进行检测到多动症。为此，我们开发了一个基于端到端的深度学习序列模型％，该模型％使用眼动扫描路径，我们将其预先培训在相关任务上，该任务可获得更多数据。我们发现该方法实际上能够检测ADHD并胜过相关的基线。我们在消融研究中研究了输入特征的相关性。有趣的是，我们发现该模型的性能与视频内容密切相关，该视频为未来的实验设计提供了见解。
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Unpaired exemplar-based image-to-image (UEI2I) translation aims to translate a source image to a target image domain with the style of a target image exemplar, without ground-truth input-translation pairs. Existing UEI2I methods represent style using either a global, image-level feature vector, or one vector per object instance/class but requiring knowledge of the scene semantics. Here, by contrast, we propose to represent style as a dense feature map, allowing for a finer-grained transfer to the source image without requiring any external semantic information. We then rely on perceptual and adversarial losses to disentangle our dense style and content representations, and exploit unsupervised cross-domain semantic correspondences to warp the exemplar style to the source content. We demonstrate the effectiveness of our method on two datasets using standard metrics together with a new localized style metric measuring style similarity in a class-wise manner. Our results evidence that the translations produced by our approach are more diverse and closer to the exemplars than those of the state-of-the-art methods while nonetheless preserving the source content.
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With the success of neural volume rendering in novel view synthesis, neural implicit reconstruction with volume rendering has become popular. However, most methods optimize per-scene functions and are unable to generalize to novel scenes. We introduce VolRecon, a generalizable implicit reconstruction method with Signed Ray Distance Function (SRDF). To reconstruct with fine details and little noise, we combine projection features, aggregated from multi-view features with a view transformer, and volume features interpolated from a coarse global feature volume. A ray transformer computes SRDF values of all the samples along a ray to estimate the surface location, which are used for volume rendering of color and depth. Extensive experiments on DTU and ETH3D demonstrate the effectiveness and generalization ability of our method. On DTU, our method outperforms SparseNeuS by about 30% in sparse view reconstruction and achieves comparable quality as MVSNet in full view reconstruction. Besides, our method shows good generalization ability on the large-scale ETH3D benchmark. Project page: https://fangjinhuawang.github.io/VolRecon.
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Runtime monitoring provides a more realistic and applicable alternative to verification in the setting of real neural networks used in industry. It is particularly useful for detecting out-of-distribution (OOD) inputs, for which the network was not trained and can yield erroneous results. We extend a runtime-monitoring approach previously proposed for classification networks to perception systems capable of identification and localization of multiple objects. Furthermore, we analyze its adequacy experimentally on different kinds of OOD settings, documenting the overall efficacy of our approach.
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Information spread on networks can be efficiently modeled by considering three features: documents' content, time of publication relative to other publications, and position of the spreader in the network. Most previous works model up to two of those jointly, or rely on heavily parametric approaches. Building on recent Dirichlet-Point processes literature, we introduce the Houston (Hidden Online User-Topic Network) model, that jointly considers all those features in a non-parametric unsupervised framework. It infers dynamic topic-dependent underlying diffusion networks in a continuous-time setting along with said topics. It is unsupervised; it considers an unlabeled stream of triplets shaped as \textit{(time of publication, information's content, spreading entity)} as input data. Online inference is conducted using a sequential Monte-Carlo algorithm that scales linearly with the size of the dataset. Our approach yields consequent improvements over existing baselines on both cluster recovery and subnetworks inference tasks.
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The publication time of a document carries a relevant information about its semantic content. The Dirichlet-Hawkes process has been proposed to jointly model textual information and publication dynamics. This approach has been used with success in several recent works, and extended to tackle specific challenging problems --typically for short texts or entangled publication dynamics. However, the prior in its current form does not allow for complex publication dynamics. In particular, inferred topics are independent from each other --a publication about finance is assumed to have no influence on publications about politics, for instance. In this work, we develop the Multivariate Powered Dirichlet-Hawkes Process (MPDHP), that alleviates this assumption. Publications about various topics can now influence each other. We detail and overcome the technical challenges that arise from considering interacting topics. We conduct a systematic evaluation of MPDHP on a range of synthetic datasets to define its application domain and limitations. Finally, we develop a use case of the MPDHP on Reddit data. At the end of this article, the interested reader will know how and when to use MPDHP, and when not to.
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Image noise can often be accurately fitted to a Poisson-Gaussian distribution. However, estimating the distribution parameters from a noisy image only is a challenging task. Here, we study the case when paired noisy and noise-free samples are accessible. No method is currently available to exploit the noise-free information, which may help to achieve more accurate estimations. To fill this gap, we derive a novel, cumulant-based, approach for Poisson-Gaussian noise modeling from paired image samples. We show its improved performance over different baselines, with special emphasis on MSE, effect of outliers, image dependence, and bias. We additionally derive the log-likelihood function for further insights and discuss real-world applicability.
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大多数信息传播模型在线依赖于以下假设：信息彼此独立传播。但是，一些作品指出了研究相互作用在现实世界过程中的作用的必要性，并强调了这样做的可能困难：相互作用稀疏和简短。作为答案，最近的进步开发了模型来说明潜在出版物动态的相互作用。在本文中，我们建议扩展和应用一个这样的模型，以确定Reddit的新闻头条之间的互动是否在其基本出版机制中起重要作用。在对2019年的100,000个新闻标题进行了深入的案例研究之后，我们检索了有关互动的最新结论，并得出结论，它们在该数据集中扮演了较小的角色。
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去年，在推荐系统中使用随机块建模（SBM）的兴趣恢复了。这些模型被视为能够处理标记数据的张量分解技术的灵活替代方法。最近提议通过将较大的上下文作为输入数据并在上下文相关元素之间添加二阶交互来解决通过SBM解决离散建议问题的最新作品。在这项工作中，我们表明这些模型都是单个全局框架的特殊情况：序列化的交互混合成员随机块模型（SIMSBM）。它允许建模任意较大的上下文以及任意高级的交互作用。我们证明了SIMSBM概括了一些最近基于SBM的基线。此外，我们证明我们的配方允许在六个现实世界数据集上增加预测能力。
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本文总结了SMM4H 2022任务10的CLAC提交，该提交涉及西班牙推文中提到的疾病的识别。在对每个令牌进行分类之前，我们使用多语言Roberta大型，UMLS Gazetteer和Distemist Gazetteer等功能对每个令牌编码进行编码。我们获得0.869的严格F1得分，竞争平均值为0.675，标准偏差为0.245，中值为0.761。
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