Deep learning methods have contributed substantially to the rapid advancement of medical image segmentation, the quality of which relies on the suitable design of loss functions. Popular loss functions, including the cross-entropy and dice losses, often fall short of boundary detection, thereby limiting high-resolution downstream applications such as automated diagnoses and procedures. We developed a novel loss function that is tailored to reflect the boundary information to enhance the boundary detection. As the contrast between segmentation and background regions along the classification boundary naturally induces heterogeneity over the pixels, we propose the piece-wise two-sample t-test augmented (PTA) loss that is infused with the statistical test for such heterogeneity. We demonstrate the improved boundary detection power of the PTA loss compared to benchmark losses without a t-test component.

完全监督的对数异常检测方法需要大量标记的数据才能实现有希望的性能。因此，如何减轻注释大量未标记的日志数据的沉重负担受到了很多关注。最近，已经提出了许多半监督对数异常检测方法，以借助于标记的正常数据解析的模板来降低注释成本。但是，这些方法通常独立考虑每个关键字，这无视日志事件中关键字之间的相关性以及日志序列之间的上下文关系。在本文中，我们提出了一个新型的弱监督的对数异常检测框架，名为Loglg，以探索序列中关键字之间的语义连接。具体而言，我们设计了一个迭代过程，首先提取未标记的日志的关键字以在每次迭代中构造日志事件图。然后，我们构建一个子记录注释，以更改为未标记的日志序列生成伪标签的目的，以注释相应的log-subgraphs。为了改善注释质量，我们采取了自我监督的任务来预先培训子图注释。之后，使用子图注释者生成的伪标签训练对数异常检测模型。在分类结果的条件下，我们从分类的日志序列重新提取关键字，并为下一个迭代更新日志事件图。五个基准的实验验证了LogLG在未标记的日志数据上检测异常的有效性，并证明与现有的半监督方法相比，Loglg作为最新的弱监督方法，可以取得重大改进。

$ t_ {1 \ rho} $映射是一种有希望的定量MRI技术，用于对组织性质的非侵入性评估。基于学习的方法可以从减少数量的$ t_ {1 \ rho} $加权图像中映射$ t_ {1 \ rho} $，但需要大量的高质量培训数据。此外，现有方法不提供$ t_ {1 \ rho} $估计的置信度。为了解决这些问题，我们提出了一个自我监督的学习神经网络，该网络使用学习过程中的放松约束来学习$ t_ {1 \ rho} $映射。为$ t_ {1 \ rho} $量化网络建立了认知不确定性和态度不确定性，以提供$ t_ {1 \ rho} $映射的贝叶斯置信度估计。不确定性估计还可以使模型规范化，以防止其学习不完美的数据。我们对52例非酒精性脂肪肝病患者收集的$ T_ {1 \ rho} $数据进行了实验。结果表明，我们的方法优于$ t_ {1 \ rho} $量化肝脏的现有方法，使用少于两个$ t_ {1 \ rho} $加权图像。我们的不确定性估计提供了一种可行的方法，可以建模基于自我监督学习的$ t_ {1 \ rho} $估计的信心，这与肝脏中的现实$ t_ {1 \ rho} $成像是一致的。

图形神经网络（GNN）已被广泛用于表示图数据的表示。但是，对图形数据实际上获得多少性能GNN的理解有限。本文介绍了上下文弹出的GNN框架，并提出了两个平滑度指标，以测量从图形数据获得的信息的数量和质量。然后，一种称为CS-GNN的新型GNN模型旨在根据图的平滑度值改善图形信息的使用。证明CS-GNN比不同类型的真实图中现有方法获得更好的性能。

本文研究了一个新的在线学习问题，其中包含双流式数据，其中数据流是通过不断发展的特征空间来描述的，新的功能出现了，旧功能逐渐消失。这个问题的挑战是两个折叠：1）随着时间的推移，数据样本不断流动，可能会随着时间的推移而随着时间的流逝而携带移动的模式，因此学习者可以随时更新。 2）很少的样本描述了新出现的特征，从而导致较弱的学习者倾向于做出错误预测。克服挑战的一个合理的想法是在前进的特征空间之间建立关系，以便在线学习者可以利用从旧功能中学到的知识来改善新功能的学习性能。不幸的是，这个想法并没有扩展到具有复杂功能相互作用的高维媒体流，这在善于跨性（偏见的浅学习者）和表现力（需要深度学习者）之间的权衡受到了折衷。在此激励的情况下，我们提出了一种新颖的旧^3S范式，其中发现了一个共享的潜在子空间来总结旧功能空间中的信息，从而构建了中间功能映射关系。旧^3S的关键特征是将模型容量视为可学习的语义，根据在线方式以输入数据流的复杂性和非线性，共同产生最佳模型深度和参数。理论分析和实证研究都证实了我们提议的生存能力和有效性。

用于分类任务的深度神经网络基本上由两个组件组成：特征提取器和功能近似器。它们通常作为整合整体工作，但是，任何组件的改进都可以促进整个算法的性能。本文侧重于设计新功能近似器。传统上，为了构建函数近似器，通常使用基于非线性激活函数的方法或非线性内核功能，并产生诸如前馈神经网络（MLP）和径向基函数网络（RBF）的经典网络。在本文中，提出了一种有效和有效的新功能近似器。新的建议网络使用分数形式而不是设计新的激活功能或内核功能。为了方便起见，我们将网络命名为网络。我们在手写数字和Internet电影数据库（IMDB）上的分类任务中，将RBF和RBF和RBF和MLP的效率和MLP的效率与RBF和MLP的效率进行比较，这是一个二进制情绪分析数据集。它表明，在大多数情况下，比率净会聚更快，优于MLP和RBF。

在这项工作中，我们试图通过设计简单和紧凑的条件领域的逆势培训方法来解决无监督的域适应。我们首先重新审视简单的级联调节策略，其中特征与输出预测连接为鉴别器的输入。我们发现倾斜策略遭受了弱势调节力量。我们进一步证明扩大连接预测的规范可以有效地激励条件域对齐。因此，我们通过将输出预测标准化具有相同的特征的输出预测来改善连接调节，并且派生方法作为归一化输出调节器〜（名词）。然而，对域对齐的原始输出预测的调理，名词遭受目标域的不准确预测。为此，我们建议将原型空间中的跨域特征对齐方式而不是输出空间。将新的原型基于原型的调节与名词相结合，我们将增强方法作为基于原型的归一化输出调节器〜（代词）。对象识别和语义分割的实验表明，名词可以有效地对准域跨域的多模态结构，甚至优于最先进的域侵犯训练方法。与基于原型的调节一起，代词进一步提高了UDA的多个对象识别基准上的名词的适应性能。

This paper focuses on designing efficient models with low parameters and FLOPs for dense predictions. Even though CNN-based lightweight methods have achieved stunning results after years of research, trading-off model accuracy and constrained resources still need further improvements. This work rethinks the essential unity of efficient Inverted Residual Block in MobileNetv2 and effective Transformer in ViT, inductively abstracting a general concept of Meta-Mobile Block, and we argue that the specific instantiation is very important to model performance though sharing the same framework. Motivated by this phenomenon, we deduce a simple yet efficient modern \textbf{I}nverted \textbf{R}esidual \textbf{M}obile \textbf{B}lock (iRMB) for mobile applications, which absorbs CNN-like efficiency to model short-distance dependency and Transformer-like dynamic modeling capability to learn long-distance interactions. Furthermore, we design a ResNet-like 4-phase \textbf{E}fficient \textbf{MO}del (EMO) based only on a series of iRMBs for dense applications. Massive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, \eg, our EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass \textbf{SoTA} CNN-/Transformer-based models, while trading-off the model accuracy and efficiency well.

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

In the field of cross-modal retrieval, single encoder models tend to perform better than dual encoder models, but they suffer from high latency and low throughput. In this paper, we present a dual encoder model called BagFormer that utilizes a cross modal interaction mechanism to improve recall performance without sacrificing latency and throughput. BagFormer achieves this through the use of bag-wise interactions, which allow for the transformation of text to a more appropriate granularity and the incorporation of entity knowledge into the model. Our experiments demonstrate that BagFormer is able to achieve results comparable to state-of-the-art single encoder models in cross-modal retrieval tasks, while also offering efficient training and inference with 20.72 times lower latency and 25.74 times higher throughput.