步态冻结(FOG)是帕金森氏病的最常见症状之一,这是中枢神经系统的神经退行性疾病,影响了世界各地数百万的人。为了满足提高雾的治疗质量的紧迫需求,设计雾计算机辅助检测和量化工具的需求越来越重要。作为一种用于收集运动模式的非侵入性技术,从压力敏感步态垫中获得的脚步压力序列为评估诊所和家庭环境中的雾气提供了绝佳的机会。在这项研究中,提出了雾检测为一项顺序建模任务,并提出了一种新颖的深度学习结构,即对对抗性时空网络(ASTN),提出了跨多个级别的雾模式。引入了一种新型的对抗训练方案,并具有多级主题鉴别器,以获得独立的雾代表示,这有助于降低由于高主体间方差而导致的过度拟合风险。结果,对于看不见的受试者,可以实现强大的雾检测。拟议的计划还阐明了从其他场景中改善主题级临床研究,因为它可以与许多现有的深层建筑集成在一起。据我们所知,这是基于脚步压力的雾检测的最早研究之一,利用ASTN的方法是追求独立于主题的表示形式的第一个深神经网络架构。从21名受试者收集的393次试验的实验结果表明,AUC 0.85的雾检测提出的ASTN表现令人鼓舞。
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随着视频数量的越来越多,对技术的需求很大,可以帮助人们迅速导航到他们感兴趣的视频片段。但是,当前的视频理解主要理解主要是视频内容摘要,而几乎没有努力,而对探索视频的结构。受文本轮廓生成的启发,我们介绍了一项新颖的视频理解任务,即视频大纲生成(VOG)。该任务定义为包含两个子任务:(1)首先根据内容结构对视频进行分割,然后(2)为每个段生成一个标题。要学习和评估VOG,我们注释了一个10K+数据集,称为Duvog。具体来说,我们使用OCR工具来识别视频的字幕。然后,要求注释者将字幕分为章节,并将每个章节分为标题。在视频中,突出显示的文本往往是标题,因为它更有可能引起人们的注意。因此,我们提出了一个视觉字幕功能增强的视频大纲生成模型(VSENET),该模型将文本字幕及其视觉字体大小和位置作为输入。我们将VOG任务视为一个序列标记问题,该问题提取了跨标题的位置,然后将其重写以形成最终大纲。此外,基于视频概述和文本概述之间的相似性,我们使用大量文章带有章节标题来预先我们的模型。 Duvog上的实验表明,我们的模型在很大程度上胜过其他基线方法,对于视频分割水平达到了77.1的F1得分,对于标题生成级别的Rouge-L_F0.5的85.0。
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非结构化的文本中存在大量的位置信息,例如社交媒体帖子,新闻报道,科学文章,网页,旅行博客和历史档案。地理学是指识别文本中的位置参考并识别其地理空间表示的过程。虽然地理标准可以使许多领域受益,但仍缺少特定应用程序的摘要。此外,缺乏对位置参考识别方法的现有方法的全面审查和比较,这是地理验证的第一个和核心步骤。为了填补这些研究空白,这篇综述首先总结了七个典型的地理应用程序域:地理信息检索,灾难管理,疾病监视,交通管理,空间人文,旅游管理和犯罪管理。然后,我们通过将这些方法分类为四个组,以基于规则的基于规则,基于统计学学习的基于统计学学习和混合方法将这些方法分类为四个组,从而回顾了现有的方法参考识别方法。接下来,我们彻底评估了27种最广泛使用的方法的正确性和计算效率,该方法基于26个公共数据集,其中包含不同类型的文本(例如,社交媒体帖子和新闻报道),包含39,736个位置参考。这项彻底评估的结果可以帮助未来的方法论发展以获取位置参考识别,并可以根据应用需求指导选择适当方法的选择。
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手语翻译(SLT),它以手语中的视觉内容以口语中的语言生成文本,很重要,以协助听力态度的沟通。灵感来自神经机翻译(NMT),最现有的SLT研究采用了一般序列来序列学习策略。然而,SLT与常规NMT任务显着不同,因为Sign语言通过多个视觉手动方面传达了消息。因此,在本文中,标志语言的这些独特的特征被制定为分层时空图表示,包括高级和微级图形,顶点表征指定的身体部位和边缘表示它们的交互。特别地,高级图表代表了手表和面部的区域中的图案,并且细级图考虑了面部区域的手和地标的关系。为了了解这些图形模式,提出了一种新颖的深度学习架构,即分层时空图神经网络(HST-GNN)。提出了具有邻域上下文的图形卷积和图形自我关注,以表征本地和全局图形属性。基准数据集的实验结果证明了该方法的有效性。
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With the increasing ability of large language models (LLMs), in-context learning (ICL) has become a new paradigm for natural language processing (NLP), where LLMs make predictions only based on contexts augmented with a few training examples. It has been a new trend exploring ICL to evaluate and extrapolate the ability of LLMs. In this paper, we aim to survey and summarize the progress, challenges, and future work in ICL. We first present a formal definition of ICL and clarify its correlation to related studies. Then, we organize and discuss advanced techniques of ICL, including training strategies, prompting strategies, and so on. Finally, we present the challenges of ICL and provide potential directions for further research. We hope our work can encourage more research on uncovering how ICL works and improving ICL in future work.
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Supervised Deep-Learning (DL)-based reconstruction algorithms have shown state-of-the-art results for highly-undersampled dynamic Magnetic Resonance Imaging (MRI) reconstruction. However, the requirement of excessive high-quality ground-truth data hinders their applications due to the generalization problem. Recently, Implicit Neural Representation (INR) has appeared as a powerful DL-based tool for solving the inverse problem by characterizing the attributes of a signal as a continuous function of corresponding coordinates in an unsupervised manner. In this work, we proposed an INR-based method to improve dynamic MRI reconstruction from highly undersampled k-space data, which only takes spatiotemporal coordinates as inputs. Specifically, the proposed INR represents the dynamic MRI images as an implicit function and encodes them into neural networks. The weights of the network are learned from sparsely-acquired (k, t)-space data itself only, without external training datasets or prior images. Benefiting from the strong implicit continuity regularization of INR together with explicit regularization for low-rankness and sparsity, our proposed method outperforms the compared scan-specific methods at various acceleration factors. E.g., experiments on retrospective cardiac cine datasets show an improvement of 5.5 ~ 7.1 dB in PSNR for extremely high accelerations (up to 41.6-fold). The high-quality and inner continuity of the images provided by INR has great potential to further improve the spatiotemporal resolution of dynamic MRI, without the need of any training data.
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A computational graph in a deep neural network (DNN) denotes a specific data flow diagram (DFD) composed of many tensors and operators. Existing toolkits for visualizing computational graphs are not applicable when the structure is highly complicated and large-scale (e.g., BERT [1]). To address this problem, we propose leveraging a suite of visual simplification techniques, including a cycle-removing method, a module-based edge-pruning algorithm, and an isomorphic subgraph stacking strategy. We design and implement an interactive visualization system that is suitable for computational graphs with up to 10 thousand elements. Experimental results and usage scenarios demonstrate that our tool reduces 60% elements on average and hence enhances the performance for recognizing and diagnosing DNN models. Our contributions are integrated into an open-source DNN visualization toolkit, namely, MindInsight [2].
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Despite the surprising few-shot performance of in-context learning (ICL), it is still a common practice to randomly sample examples to serve as context. This paper advocates a new principle for ICL: self-adaptive in-context learning. The self-adaption mechanism is introduced to help each sample find an in-context example permutation (i.e., selection and ordering) that can derive the correct prediction, thus maximizing performance. To validate the effectiveness of self-adaptive ICL, we propose a general select-then-rank framework and instantiate it with new selection and ranking algorithms. Upon extensive evaluation on eight different NLP datasets, our self-adaptive ICL method achieves a 40% relative improvement over the common practice setting. Further analysis reveals the enormous potential of self-adaptive ICL that it might be able to close the gap between ICL and finetuning given more advanced algorithms. Our code is released to facilitate future research in this area: https://github.com/Shark-NLP/self-adaptive-ICL
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Explaining the black-box predictions of NLP models naturally and accurately is an important open problem in natural language generation. These free-text explanations are expected to contain sufficient and carefully-selected evidence to form supportive arguments for predictions. Due to the superior generative capacity of large pretrained language models, recent work built on prompt engineering enables explanation generation without specific training. However, explanation generated through single-pass prompting often lacks sufficiency and conciseness. To address this problem, we develop an information bottleneck method EIB to produce refined explanations that are sufficient and concise. Our approach regenerates the free-text explanation by polishing the single-pass output from the pretrained language model but retaining the information that supports the contents being explained. Experiments on two out-of-domain tasks verify the effectiveness of EIB through automatic evaluation and thoroughly-conducted human evaluation.
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Deep-learning-based technologies such as deepfakes ones have been attracting widespread attention in both society and academia, particularly ones used to synthesize forged face images. These automatic and professional-skill-free face manipulation technologies can be used to replace the face in an original image or video with any target object while maintaining the expression and demeanor. Since human faces are closely related to identity characteristics, maliciously disseminated identity manipulated videos could trigger a crisis of public trust in the media and could even have serious political, social, and legal implications. To effectively detect manipulated videos, we focus on the position offset in the face blending process, resulting from the forced affine transformation of the normalized forged face. We introduce a method for detecting manipulated videos that is based on the trajectory of the facial region displacement. Specifically, we develop a virtual-anchor-based method for extracting the facial trajectory, which can robustly represent displacement information. This information was used to construct a network for exposing multidimensional artifacts in the trajectory sequences of manipulated videos that is based on dual-stream spatial-temporal graph attention and a gated recurrent unit backbone. Testing of our method on various manipulation datasets demonstrated that its accuracy and generalization ability is competitive with that of the leading detection methods.
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