整个幻灯片图像（WSI）分类是诊断和治疗疾病的基本任务；但是，精确标签的策划是耗时的，并限制了完全监督的方法的应用。为了解决这个问题，多个实例学习（MIL）是一种流行的方法，它仅使用幻灯片级标签作为一个弱监督的学习任务。尽管当前的MIL方法将注意机制的变体应用于具有更强模型的重量实例特征，但注意力不足是对数据分布的属性的不足。在这项工作中，我们建议通过使用Max-Instance（关键）功能的统计数据来重新校准WSI袋（实例）的分布。我们假设在二进制MIL中，正面袋的特征幅度大于负面，因此我们可以强制执行该模型，以最大程度地利用公制特征损失的袋子之间的差异，该袋子将正面袋模型为未分布。为了实现这一目标，与使用单批训练模式的现有MIL方法不同，我们建议平衡批次采样以有效地使用功能丢失，即同时（+/-）袋子。此外，我们采用编码模块（PEM）的位置来建模空间/形态信息，并通过变压器编码器通过多头自我注意（PSMA）进行汇总。现有基准数据集的实验结果表明我们的方法是有效的，并且对最先进的MIL方法有所改善。

大多数最新的说话者验证架构都采用了多尺度处理和频道注意机制。这些模型的卷积层通常具有固定的内核大小，例如3或5。在本研究中，我们进一步为这一研究采用了选择性核心注意（SKA）机制。SKA机制允许每个卷积层以数据驱动的方式自适应地选择内核大小。它基于利用频率和通道域的注意机制。我们首先将现有的SKA模块应用于我们的基线。然后，我们提出了两个SKA变体，其中第一个变体在ECAPA-TDNN模型的前面应用，另一个变体与RES2NET骨干块结合使用。通过广泛的实验，我们证明了我们提出的两个SKA变体始终提高性能，并在三个不同的评估方案上进行测试时是互补的。

在本文中，我们提出了自我监督的发言者表示学习策略，该策略包括在前端的引导平衡扬声器表示学习和在后端的不确定性意识的概率扬声器嵌入训练。在前端阶段，我们通过具有均匀性正则化术语的引导训练方案来学习扬声器表示。在后端阶段，通过最大化属于同一扬声器的语音样本之间的相互似然分数来估计概率扬声器嵌入，这不仅提供扬声器表示，而且提供数据不确定性。实验结果表明，拟议的举止均衡训练策略可以有效地帮助了解扬声器表示，并以基于对比学习的传统方法优越。此外，我们展示了集成的两级框架在eer和mindcf方面进一步改善了VoxceleB1测试中的扬声器验证性能。

知识蒸馏（KD），最称为模型压缩的有效方法，旨在将更大的网络（教师）的知识转移到更小的网络（学生）。传统的KD方法通常采用以监督方式培训的教师模型，其中输出标签仅作为目标处理。我们进一步扩展了这一受监督方案，我们为KD，即Oracle老师推出了一种新型的教师模型，它利用源输入和输出标签的嵌入来提取更准确的知识来转移到学生。所提出的模型遵循变压器网络的编码器解码器注意结构，这允许模型从输出标签上参加相关信息。在三种不同的序列学习任务中进行了广泛的实验：语音识别，场景文本识别和机器翻译。从实验结果来看，我们经验证明，拟议的模型在这些任务中改善了学生，同时在教师模型的培训时间内实现了相当大的速度。

对于大部分现实生活中的话语，其意图不能仅由它们的语义或句法特征决定。尽管并非所有的社会语言和务实信息都可以数字化，但至少语音特征在理解口语中是必不可少的。尤其是在诸如韩国人之类的头部最终语言中，句子 - 最终韵律在识别说话者的意图方面非常重要。本文提出了一个系统，该系统鉴于其笔录在某些情况下使用辅助声学特征，可以识别出口语的固有意图。这里的要点是对于歧视意图需要声学提示的情况。因此，提出的分类系统决定给定的话语是利用来自头部金属的语调依赖性的片段，陈述，问题，命令还是命令/命令。基于对参与数据注释的韩语的直观理解，我们构建了一个网络，该网络识别语音的意图，并通过测试句子验证其实用性。该系统如果与最新的语音识别器结合在一起，则预计将灵活地插入各种语言理解模块中。

Crowdsourcing has emerged as an effective platform to label a large volume of data in a cost- and time-efficient manner. Most previous works have focused on designing an efficient algorithm to recover only the ground-truth labels of the data. In this paper, we consider multi-choice crowdsourced labeling with the goal of recovering not only the ground truth but also the most confusing answer and the confusion probability. The most confusing answer provides useful information about the task by revealing the most plausible answer other than the ground truth and how plausible it is. To theoretically analyze such scenarios, we propose a model where there are top-two plausible answers for each task, distinguished from the rest of choices. Task difficulty is quantified by the confusion probability between the top two, and worker reliability is quantified by the probability of giving an answer among the top two. Under this model, we propose a two-stage inference algorithm to infer the top-two answers as well as the confusion probability. We show that our algorithm achieves the minimax optimal convergence rate. We conduct both synthetic and real-data experiments and demonstrate that our algorithm outperforms other recent algorithms. We also show the applicability of our algorithms in inferring the difficulty of tasks and training neural networks with the soft labels composed of the top-two most plausible classes.

In this paper, we propose a diffusion-based face swapping framework for the first time, called DiffFace, composed of training ID conditional DDPM, sampling with facial guidance, and a target-preserving blending. In specific, in the training process, the ID conditional DDPM is trained to generate face images with the desired identity. In the sampling process, we use the off-the-shelf facial expert models to make the model transfer source identity while preserving target attributes faithfully. During this process, to preserve the background of the target image and obtain the desired face swapping result, we additionally propose a target-preserving blending strategy. It helps our model to keep the attributes of the target face from noise while transferring the source facial identity. In addition, without any re-training, our model can flexibly apply additional facial guidance and adaptively control the ID-attributes trade-off to achieve the desired results. To the best of our knowledge, this is the first approach that applies the diffusion model in face swapping task. Compared with previous GAN-based approaches, by taking advantage of the diffusion model for the face swapping task, DiffFace achieves better benefits such as training stability, high fidelity, diversity of the samples, and controllability. Extensive experiments show that our DiffFace is comparable or superior to the state-of-the-art methods on several standard face swapping benchmarks.

This study proposes an approach for establishing an optimal multihop ad-hoc network using multiple unmanned aerial vehicles (UAVs) to provide emergency communication in disaster areas. The approach includes two stages, one uses particle swarm optimization (PSO) to find optimal positions to deploy UAVs, and the other uses a behavior-based controller to navigate the UAVs to their assigned positions without colliding with obstacles in an unknown environment. Several constraints related to the UAVs' sensing and communication ranges have been imposed to ensure the applicability of the proposed approach in real-world scenarios. A number of simulation experiments with data loaded from real environments have been conducted. The results show that our proposed approach is not only successful in establishing multihop ad-hoc routes but also meets the requirements for real-time deployment of UAVs.

Prostate cancer is the most common cancer in men worldwide and the second leading cause of cancer death in the United States. One of the prognostic features in prostate cancer is the Gleason grading of histopathology images. The Gleason grade is assigned based on tumor architecture on Hematoxylin and Eosin (H&E) stained whole slide images (WSI) by the pathologists. This process is time-consuming and has known interobserver variability. In the past few years, deep learning algorithms have been used to analyze histopathology images, delivering promising results for grading prostate cancer. However, most of the algorithms rely on the fully annotated datasets which are expensive to generate. In this work, we proposed a novel weakly-supervised algorithm to classify prostate cancer grades. The proposed algorithm consists of three steps: (1) extracting discriminative areas in a histopathology image by employing the Multiple Instance Learning (MIL) algorithm based on Transformers, (2) representing the image by constructing a graph using the discriminative patches, and (3) classifying the image into its Gleason grades by developing a Graph Convolutional Neural Network (GCN) based on the gated attention mechanism. We evaluated our algorithm using publicly available datasets, including TCGAPRAD, PANDA, and Gleason 2019 challenge datasets. We also cross validated the algorithm on an independent dataset. Results show that the proposed model achieved state-of-the-art performance in the Gleason grading task in terms of accuracy, F1 score, and cohen-kappa. The code is available at https://github.com/NabaviLab/Prostate-Cancer.

Nowadays, fake news easily propagates through online social networks and becomes a grand threat to individuals and society. Assessing the authenticity of news is challenging due to its elaborately fabricated contents, making it difficult to obtain large-scale annotations for fake news data. Due to such data scarcity issues, detecting fake news tends to fail and overfit in the supervised setting. Recently, graph neural networks (GNNs) have been adopted to leverage the richer relational information among both labeled and unlabeled instances. Despite their promising results, they are inherently focused on pairwise relations between news, which can limit the expressive power for capturing fake news that spreads in a group-level. For example, detecting fake news can be more effective when we better understand relations between news pieces shared among susceptible users. To address those issues, we propose to leverage a hypergraph to represent group-wise interaction among news, while focusing on important news relations with its dual-level attention mechanism. Experiments based on two benchmark datasets show that our approach yields remarkable performance and maintains the high performance even with a small subset of labeled news data.