Shape can specify key object constraints, yet existing text-to-image diffusion models ignore this cue and synthesize objects that are incorrectly scaled, cut off, or replaced with background content. We propose a training-free method, Shape-Guided Diffusion, which uses a novel Inside-Outside Attention mechanism to constrain the cross-attention (and self-attention) maps such that prompt tokens (and pixels) referring to the inside of the shape cannot attend outside the shape, and vice versa. To demonstrate the efficacy of our method, we propose a new image editing task where the model must replace an object specified by its mask and a text prompt. We curate a new ShapePrompts benchmark based on MS-COCO and achieve SOTA results in shape faithfulness, text alignment, and realism according to both quantitative metrics and human preferences. Our data and code will be made available at https://shape-guided-diffusion.github.io.

可控图像合成模型允许根据文本指令或来自示例图像的指导创建不同的图像。最近，已经显示出去噪扩散概率模型比现有方法产生更现实的图像，并且已在无条件和类条件设置中成功展示。我们探索细粒度，连续控制该模型类，并引入了一种新颖的统一框架，用于语义扩散指导，允许语言或图像指导，或两者。使用图像文本或图像匹配分数的梯度将指导注入预训练的无条件扩散模型中。我们探讨基于剪辑的文本指导，以及以统一形式的基于内容和类型的图像指导。我们的文本引导综合方法可以应用于没有相关文本注释的数据集。我们对FFHQ和LSUN数据集进行实验，并显示出细粒度的文本引导图像合成的结果，与样式或内容示例图像相关的图像的合成，以及具有文本和图像引导的示例。

The classification of sleep stages plays a crucial role in understanding and diagnosing sleep pathophysiology. Sleep stage scoring relies heavily on visual inspection by an expert that is time consuming and subjective procedure. Recently, deep learning neural network approaches have been leveraged to develop a generalized automated sleep staging and account for shifts in distributions that may be caused by inherent inter/intra-subject variability, heterogeneity across datasets, and different recording environments. However, these networks ignore the connections among brain regions, and disregard the sequential connections between temporally adjacent sleep epochs. To address these issues, this work proposes an adaptive product graph learning-based graph convolutional network, named ProductGraphSleepNet, for learning joint spatio-temporal graphs along with a bidirectional gated recurrent unit and a modified graph attention network to capture the attentive dynamics of sleep stage transitions. Evaluation on two public databases: the Montreal Archive of Sleep Studies (MASS) SS3; and the SleepEDF, which contain full night polysomnography recordings of 62 and 20 healthy subjects, respectively, demonstrates performance comparable to the state-of-the-art (Accuracy: 0.867;0.838, F1-score: 0.818;0.774 and Kappa: 0.802;0.775, on each database respectively). More importantly, the proposed network makes it possible for clinicians to comprehend and interpret the learned connectivity graphs for sleep stages.

Sensor-based remote health monitoring is used in industrial, urban and healthcare settings to monitor ongoing operation of equipment and human health. An important aim is to intervene early if anomalous events or adverse health is detected. In the wild, these anomaly detection approaches are challenged by noise, label scarcity, high dimensionality, explainability and wide variability in operating environments. The Contextual Matrix Profile (CMP) is a configurable 2-dimensional version of the Matrix Profile (MP) that uses the distance matrix of all subsequences of a time series to discover patterns and anomalies. The CMP is shown to enhance the effectiveness of the MP and other SOTA methods at detecting, visualising and interpreting true anomalies in noisy real world data from different domains. It excels at zooming out and identifying temporal patterns at configurable time scales. However, the CMP does not address cross-sensor information, and cannot scale to high dimensional data. We propose a novel, self-supervised graph-based approach for temporal anomaly detection that works on context graphs generated from the CMP distance matrix. The learned graph embeddings encode the anomalous nature of a time context. In addition, we evaluate other graph outlier algorithms for the same task. Given our pipeline is modular, graph construction, generation of graph embeddings, and pattern recognition logic can all be chosen based on the specific pattern detection application. We verified the effectiveness of graph-based anomaly detection and compared it with the CMP and 3 state-of-the art methods on two real-world healthcare datasets with different anomalies. Our proposed method demonstrated better recall, alert rate and generalisability.

Pronoun resolution is a challenging subset of an essential field in natural language processing called coreference resolution. Coreference resolution is about finding all entities in the text that refers to the same real-world entity. This paper presents a hybrid model combining multiple rulebased sieves with a machine-learning sieve for pronouns. For this purpose, seven high-precision rule-based sieves are designed for the Persian language. Then, a random forest classifier links pronouns to the previous partial clusters. The presented method demonstrates exemplary performance using pipeline design and combining the advantages of machine learning and rulebased methods. This method has solved some challenges in end-to-end models. In this paper, the authors develop a Persian coreference corpus called Mehr in the form of 400 documents. This corpus fixes some weaknesses of the previous corpora in the Persian language. Finally, the efficiency of the presented system compared to the earlier model in Persian is reported by evaluating the proposed method on the Mehr and Uppsala test sets.

Coreference resolution (CR) is one of the most challenging areas of natural language processing. This task seeks to identify all textual references to the same real-world entity. Research in this field is divided into coreference resolution and anaphora resolution. Due to its application in textual comprehension and its utility in other tasks such as information extraction systems, document summarization, and machine translation, this field has attracted considerable interest. Consequently, it has a significant effect on the quality of these systems. This article reviews the existing corpora and evaluation metrics in this field. Then, an overview of the coreference algorithms, from rule-based methods to the latest deep learning techniques, is provided. Finally, coreference resolution and pronoun resolution systems in Persian are investigated.

病理学家对患病组织的视觉微观研究一直是一个多世纪以来癌症诊断和预后的基石。最近，深度学习方法在组织图像的分析和分类方面取得了重大进步。但是，关于此类模型在生成组织病理学图像的实用性方面的工作有限。这些合成图像在病理学中有多种应用，包括教育，熟练程度测试，隐私和数据共享的公用事业。最近，引入了扩散概率模型以生成高质量的图像。在这里，我们首次研究了此类模型的潜在用途以及优先的形态加权和颜色归一化，以合成脑癌的高质量组织病理学图像。我们的详细结果表明，与生成对抗网络相比，扩散概率模型能够合成各种组织病理学图像，并且具有较高的性能。

在生物医学语料库中预先培训的语言模型，例如Biobert，最近在下游生物医学任务上显示出令人鼓舞的结果。另一方面，由于嵌入尺寸，隐藏尺寸和层数等因素，许多现有的预训练模型在资源密集型和计算上都是沉重的。自然语言处理（NLP）社区已经制定了许多策略来压缩这些模型，利用修剪，定量和知识蒸馏等技术，从而导致模型更快，更小，随后更易于使用。同样，在本文中，我们介绍了六种轻型模型，即Biodistilbert，Biotinybert，BioMobilebert，Distilbiobert，Tinybiobert和Cmpactactbiobert，并通过掩护的语言在PubMed DataSet上通过掩护数据进行了知识蒸馏而获得的知识蒸馏来获得。建模（MLM）目标。我们在三个生物医学任务上评估了所有模型，并将它们与Biobert-V1.1进行比较，以创建有效的轻量级模型，以与较大的对应物相同。所有模型将在我们的HuggingFace配置文件上公开可用，网址为https://huggingface.co/nlpie，用于运行实验的代码将在https://github.com/nlpie-research/compact-compact-biomedical-transformers上获得。

乳腺癌是全球女性中最常见的癌症。乳腺癌的早期诊断可以显着提高治疗效率。由于其可靠性，准确性和负担能力，计算机辅助诊断（CAD）系统被广泛采用。乳腺癌诊断有不同的成像技术。本文使用的最准确的是组织病理学。深度传输学习被用作提议的CAD系统功能提取器的主要思想。尽管在这项研究中已经测试了16个不同的预训练网络，但我们的主要重点是分类阶段。在所有测试的CNN中，具有剩余网络既有剩余网络既有剩余和启动网络的启发能力，均显示出最佳的特征提取能力。在分类阶段，Catboost，XGBOOST和LIGHTGBM的合奏提供了最佳的平均精度。 Breakhis数据集用于评估所提出的方法。 Breakhis在四个放大因素中包含7909个组织病理学图像（2,480个良性和5,429个恶性）。提出的方法的准确性（IRV2-CXL）使用70％的Breakhis数据集作为40倍，100X，200X和400X放大倍率的训练数据分别为96.82％，95.84％，97.01％和96.15％。大多数关于自动乳腺癌检测的研究都集中在特征提取上，这使我们参加了分类阶段。 IRV2-CXL由于使用软投票集合方法而显示出更好或可比较的结果，该合奏方法可以将Catboost，XGBoost和LightGBM的优势结合在一起。

在医学中，图像注册对于图像引导的干预措施和其他临床应用至关重要。但是，很难解决，通过机器学习的出现，最近在该领域的医疗图像注册方面已经取得了很大的进步。深度神经网络的实施为某些医学应用提供了机会，例如在更少的时间内进行图像注册，以高精度，在操作过程中对抗肿瘤中发挥关键作用。当前的研究对基于无监督的深神经网络的医学图像注册研究的最新文献进行了全面的范围审查，其中包括到本领域在此日期中发表的所有相关研究。在这里，我们试图总结医学领域中无监督的基于深度学习的注册方法的最新发展和应用。在当前的全面范围审查中，精心讨论和传达了基本和主要概念，技术，从不同观点，新颖性和未来方向的统计分析。此外，这篇评论希望帮助那些被这一领域铆接的活跃读者深入了解这一激动人心的领域。