Exposure to ideas in domains outside a scientist's own may benefit her in reformulating existing research problems in novel ways and discovering new application domains for existing solution ideas. While improved performance in scholarly search engines can help scientists efficiently identify relevant advances in domains they may already be familiar with, it may fall short of helping them explore diverse ideas \textit{outside} such domains. In this paper we explore the design of systems aimed at augmenting the end-user ability in cross-domain exploration with flexible query specification. To this end, we develop an exploratory search system in which end-users can select a portion of text core to their interest from a paper abstract and retrieve papers that have a high similarity to the user-selected core aspect but differ in terms of domains. Furthermore, end-users can `zoom in' to specific domain clusters to retrieve more papers from them and understand nuanced differences within the clusters. Our case studies with scientists uncover opportunities and design implications for systems aimed at facilitating cross-domain exploration and inspiration.

A tractogram is a virtual representation of the brain white matter. It is composed of millions of virtual fibers, encoded as 3D polylines, which approximate the white matter axonal pathways. To date, tractograms are the most accurate white matter representation and thus are used for tasks like presurgical planning and investigations of neuroplasticity, brain disorders, or brain networks. However, it is a well-known issue that a large portion of tractogram fibers is not anatomically plausible and can be considered artifacts of the tracking procedure. With Verifyber, we tackle the problem of filtering out such non-plausible fibers using a novel fully-supervised learning approach. Differently from other approaches based on signal reconstruction and/or brain topology regularization, we guide our method with the existing anatomical knowledge of the white matter. Using tractograms annotated according to anatomical principles, we train our model, Verifyber, to classify fibers as either anatomically plausible or non-plausible. The proposed Verifyber model is an original Geometric Deep Learning method that can deal with variable size fibers, while being invariant to fiber orientation. Our model considers each fiber as a graph of points, and by learning features of the edges between consecutive points via the proposed sequence Edge Convolution, it can capture the underlying anatomical properties. The output filtering results highly accurate and robust across an extensive set of experiments, and fast; with a 12GB GPU, filtering a tractogram of 1M fibers requires less than a minute. Verifyber implementation and trained models are available at https://github.com/FBK-NILab/verifyber.

Indonesia holds the second-highest-ranking country for the highest number of malaria cases in Southeast Asia. A different malaria parasite semantic segmentation technique based on a deep learning approach is an alternative to reduce the limitations of traditional methods. However, the main problem of the semantic segmentation technique is raised since large parasites are dominant, and the tiny parasites are suppressed. In addition, the amount and variance of data are important influences in establishing their models. In this study, we conduct two contributions. First, we collect 559 microscopic images containing 691 malaria parasites of thin blood smears. The dataset is named PlasmoID, and most data comes from rural Indonesia. PlasmoID also provides ground truth for parasite detection and segmentation purposes. Second, this study proposes a malaria parasite segmentation and detection scheme by combining Faster RCNN and a semantic segmentation technique. The proposed scheme has been evaluated on the PlasmoID dataset. It has been compared with recent studies of semantic segmentation techniques, namely UNet, ResFCN-18, DeepLabV3, DeepLabV3plus and ResUNet-18. The result shows that our proposed scheme can improve the segmentation and detection of malaria parasite performance compared to original semantic segmentation techniques.

符号回归是一种非线性回归方法，通常通过诸如遗传编程等进化计算方法执行。量化回归模型的不确定性对于模型和决策的解释很重要。线性近似和所谓的似然谱是非线性回归模型计算置信度和预测间隔的众所周知的可能性。到目前为止，这些简单有效的技术在遗传编程文献中已被完全忽略。在这项工作中，我们在详细信息中描述了似然概况的计算，还提供了一些说明性示例，其中使用了两个不同数据集上使用三种不同的符号回归算法创建的模型。这些示例突出了可能性概况的重要性，即了解符号回归模型的局限性，并帮助用户做出明智的预测后决策。

对于机器人应用来说，人类的方法非常重要。在介绍的研究中，我们实施了多模式的人类机器人互动（HRI）方案，其中模拟机器人通过语音和手势与其人类伴侣进行交流。机器人口头宣布其意图，并使用指向手势选择适当的动作。反过来，人类合作伙伴会评估机器人的口头公告（意图）是否与机器人选择的动作（指向手势）相匹配。对于机器人的口头公告与机器人的相应动作选择不符的情况，我们预计人类脑电图（EEG）中与错误相关的电位（ERRP）。实时记录了人类对机器人动作的固有评估，在脑电图中显而易见，在线连续分段并异步分类。对于功能选择，我们提出了一种方法，该方法允许向前和向后滑动窗口组合以训练分类器。我们在9个受试者中达到了91％的平均分类性能。正如预期的那样，我们还观察到受试者之间的变异性相对较高。将来，将扩展提出的特征选择方法，以允许自定义功能选择。为此，将自动选择向前和后滑动窗口的最佳组合，以说明分类性能中受试者间的可变性。此外，我们计划使用ERRP在互动增强学习中使用ERRP在错误情况下明显的固有人类错误评估来改善多模式的人类机器人相互作用。

自动识别基础心脏异常的结构底物可以潜在地为介入程序提供实时指导。有了心脏组织底物的了解，可以通过检测心律不齐的底物来进一步优化复杂的心律不齐和心室心动过速等复杂的心律不齐和心室心动过速。光学相干断层扫描（OCT）是一种实时成像方式，有助于满足这一需求。心脏图像分析的现有方法主要依赖于完全监督的学习技术，这些技术遇到了在像素标签的劳动密集型注释过程中工作量的缺点。为了减少对像素标签的需求，我们使用人类心脏底物的OCT图像上的图像级注释开发了一个两阶段的深度学习框架，用于心脏脂肪组织分割。特别是，我们将类激活映射与超像素分割整合在一起，以解决心脏组织分割中提出的稀疏组织种子挑战。我们的研究弥合了自动组织分析的需求与缺乏高质量像素的注释之间的差距。据我们所知，这是第一项尝试通过弱监督的学习技术来解决OCT图像上心脏组织分割的研究。在体外人类心脏OCT数据集中，我们证明了我们对图像级注释的弱监督方法可与对像素式注释进行训练的完全监督方法相当。

自我关注是强大的模拟远程依赖性，但在本地更精细的特征学习中是薄弱的。局部自我关注（LSA）的表现正恰好搭配卷积，劣于动态过滤器，这拼图是使用LSA或其同行的研究人员，哪一个更好，是什么让LSA平庸。为了澄清这些，我们全面调查了来自双方的LSA及其对应物：\ EMPH {频道设置}和\ EMPH {空间处理}。我们发现魔鬼在于生成和应用空间注意，其中相对位置嵌入和相邻过滤器应用是关键因素。根据这些调查结果，我们提出了具有Hadamard注意力和幽灵头的局部自我关注（ELSA）。 Hadamard注意介绍了Hadamard产品，在邻近壳体中有效地产生注意，同时保持高阶映射。 Ghost Head将注意力映射与静态矩阵相结合以增加信道容量。实验证明了ELSA的有效性。如果没有架构/封路数据计修改，则使用ELSA的替换LSA将Swin Transformer \ Cite {Swin}替换为高达+1.​​4，最高1精度。 ELSA还一直在D1至D5中始终如一地享受Volo \ Cite {Volo}，其中Elsa-Volo-D5在ImageNet-1K上实现87.2，而无需额外的培训图像。此外，我们在下游任务中评估ELSA。 ELSA在COCO上显着改善了最高+1.9盒AP / +1.3面膜AP，并在ADE20K上达到+1.9 miou。代码可用于\ url {https:/github.com/damo-cv/elsa}。

深度学习（DL）模型为各种医学成像基准挑战提供了最先进的性能，包括脑肿瘤细分（BRATS）挑战。然而，局灶性病理多隔室分割（例如，肿瘤和病变子区）的任务特别具有挑战性，并且潜在的错误阻碍DL模型转化为临床工作流程。量化不确定形式的DL模型预测的可靠性，可以实现最不确定的地区的临床审查，从而建立信任并铺平临床翻译。最近，已经引入了许多不确定性估计方法，用于DL医学图像分割任务。开发指标评估和比较不确定性措施的表现将有助于最终用户制定更明智的决策。在本研究中，我们探索并评估在Brats 2019-2020任务期间开发的公制，以对不确定量化量化（Qu-Brats），并旨在评估和排列脑肿瘤多隔室分割的不确定性估计。该公制（1）奖励不确定性估计，对正确断言产生高置信度，以及在不正确的断言处分配低置信水平的估计数，（2）惩罚导致更高百分比的无关正确断言百分比的不确定性措施。我们进一步基准测试由14个独立参与的Qu-Brats 2020的分割不确定性，所有这些都参与了主要的Brats细分任务。总体而言，我们的研究结果证实了不确定性估计提供了分割算法的重要性和互补价值，因此突出了医学图像分析中不确定性量化的需求。我们的评估代码在HTTPS://github.com/ragmeh11/qu-brats公开提供。

异常检测旨在识别来自正常数据分布的异常情况。该领域已经取得了许多进展，包括创新使用无监督的对比学习。然而，现有方法通常假设清洁训练数据，并且当数据包含未知异常时受限。本文介绍了一种新型半监督异常检测方法，统一了与无监督的对比学习的能源的模型的概念。 ELSA通过基于新能量函数的精心设计的微调步骤灌输对任何数据污染的鲁棒性，这些步骤迫使正常数据分为原型的类别。多种污染方案的实验表明，所提出的模型实现了SOTA性能。广泛的分析还验证了每个组件在所提出的模型中的贡献。除了实验之外，我们还提供了一种理论解释，对何对象学习独自无法检测到数据污染下的异常。

The relationship between words in a sentence often tells us more about the underlying semantic content of a document than its actual words, individually. In this work, we propose two novel algorithms, called Flexible Lexical Chain II and Fixed Lexical Chain II. These algorithms combine the semantic relations derived from lexical chains, prior knowledge from lexical databases, and the robustness of the distributional hypothesis in word embeddings as building blocks forming a single system. In short, our approach has three main contributions: (i) a set of techniques that fully integrate word embeddings and lexical chains; (ii) a more robust semantic representation that considers the latent relation between words in a document; and (iii) lightweight word embeddings models that can be extended to any natural language task. We intend to assess the knowledge of pre-trained models to evaluate their robustness in the document classification task. The proposed techniques are tested against seven word embeddings algorithms using five different machine learning classifiers over six scenarios in the document classification task. Our results show the integration between lexical chains and word embeddings representations sustain state-of-the-art results, even against more complex systems.