Because of the necessity to obtain high-quality images with minimal radiation doses, such as in low-field magnetic resonance imaging, super-resolution reconstruction in medical imaging has become more popular (MRI). However, due to the complexity and high aesthetic requirements of medical imaging, image super-resolution reconstruction remains a difficult challenge. In this paper, we offer a deep learning-based strategy for reconstructing medical images from low resolutions utilizing Transformer and Generative Adversarial Networks (T-GAN). The integrated system can extract more precise texture information and focus more on important locations through global image matching after successfully inserting Transformer into the generative adversarial network for picture reconstruction. Furthermore, we weighted the combination of content loss, adversarial loss, and adversarial feature loss as the final multi-task loss function during the training of our proposed model T-GAN. In comparison to established measures like PSNR and SSIM, our suggested T-GAN achieves optimal performance and recovers more texture features in super-resolution reconstruction of MRI scanned images of the knees and belly.
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端到端语音翻译(E2E-ST)由于其误差传播的潜力,较低的延迟和较少的参数而受到了越来越多的关注。但是,基于神经的方法对该任务的有效性受到可用培训语料库的严重限制,尤其是对于较少或不存在的域中三重障碍培训数据的领域适应性。在本文中,我们提出了一种新型的非参数方法,该方法利用特定于域的文本翻译语料库来实现E2E-ST系统的域适应性。为此,我们首先将一个附加的编码器纳入预先训练的E2E-ST模型中,以实现文本翻译建模,然后通过减少可用三重态训练数据中的通讯表示不匹配来统一解码器的输出表示形式,以实现文本和语音翻译任务。在域适应过程中,引入了K-Nearest-neighbor(KNN)分类器,以使用由域特异性文本翻译语料库构建的外部数据存储器生成最终的翻译分布,而采用通用输出表示来执行相似性搜索。 Europarl-St基准的实验表明,仅涉及内域文本翻译数据时,我们提出的方法在所有翻译方向上平均将基线显着提高了基线,即使表现出强大的强度内域微调方法。
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我们提出了一种基于无监督的深度学习方法,以估算焦点堆栈摄像头图像的深度。在NYU-V2数据集上,与基于单图像的方法相比,我们的方法获得了更好的深度估计精度。
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由于其误差传播,延迟较少和更少的参数较少的潜力,端到端语音到文本翻译〜(e2e-st)变得越来越受欢迎。鉴于三联培训语料库$ \ langle演讲,转录,翻译\ rangle $,传统的高质量E2E-ST系统利用$ \ langle演讲,转录\ rangle $配对预先培训模型,然后利用$ \ Langle演讲,翻译\ rangle $配对进一步优化它。然而,该过程仅涉及每个阶段的两个元组数据,并且该松散耦合不能完全利用三重态数据之间的关联。在本文中,我们试图基于语音输入模拟转录和翻译的联合概率,以直接利用这种三重态数据。基于此,我们提出了一种新的正规化方法,用于改进三重态数据中双路分解协议的模型培训,理论上应该是相等的。为实现这一目标,我们将两个Kullback-Leibler发散正规化术语介绍到模型培训目的中,以减少双路径输出概率之间的不匹配。然后,训练有素的模型可以通过预定义的早期停止标签自然地被视为E2E-ST模型。 Must-C基准测试的实验表明,我们所提出的方法在所有8个语言对上显着优于最先进的E2E-ST基线,同时在自动语音识别任务中实现更好的性能。我们的代码在https://github.com/duyichao/e2e -st-tda开放。
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最近,随着深度学习的持续发展,指定实体识别任务的表现得到了极大的改进。但是,在某些特定领域(例如生物医学和军事)中数据的隐私和机密性导致数据不足以支持深度神经网络的培训。在本文中,我们提出了一个加密学习框架,以解决数据泄漏的问题以及对某些域中敏感数据的不便披露。我们首次将多个加密算法介绍以在指定实体识别任务中加密培训数据。换句话说,我们使用加密数据训练深神网络。我们在六个中国数据集上进行实验,其中三个是由我们自己构建的。实验结果表明,加密方法可实现令人满意的结果。一些经过加密数据训练的模型的性能甚至超过了未加密方法的性能,该方法验证了引入的加密方法的有效性,并在一定程度上解决了数据泄漏问题。
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在复杂的场景中,尤其是在城市交通交叉点,对实体关系和运动行为的深刻理解对于实现高质量的计划非常重要。我们提出了有关交通信号灯D2-Tpred的轨迹预测方法,该方法使用空间动态交互图(SDG)和行为依赖图(BDG)来处理空间空间中不连续依赖的问题。具体而言,SDG用于通过在每帧中具有动态和可变特征的不同试剂的子图来捕获空间相互作用。 BDG用于通过建模当前状态对先验行为的隐式依赖性来推断运动趋势,尤其是与加速度,减速或转向方向相对应的不连续运动。此外,我们提出了一个新的数据集,用于在称为VTP-TL的交通信号灯下进行车辆轨迹预测。我们的实验结果表明,与其他轨迹预测算法相比,我们的模型在ADE和FDE方面分别获得了{20.45%和20.78%}的改善。数据集和代码可在以下网址获得:https://github.com/vtp-tl/d2-tpred。
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两种样本测试评估两个样品是否是相同分布(零假设)或两种不同分布(替代假设)的实现。在传统的本问题的制定中,统计学家可以访问测量(特征变量)和组变量(标签变量)。但是,在几个重要的应用程序中,可以轻松测量特征变量,但二进制标签变量是未知的并且获得昂贵的。在本文中,我们考虑了经典的两个样本测试问题的这一重要变化,并将其构成,作为在执行两个样本测试的服务中仅获得少量样品的标签的问题。我们设计了一个标签高效的三阶段框架:首先,分类器培训,采用均匀标记为模拟标签的后验概率;其次,将一个创新的查询计划被称为\ emph {bimodal查询}用于查询来自两个类别的样本标签,最大的后验概率,最后,对查询样本进行了经典的弗里德曼-RAFSKY(FR)两样测试。我们的理论分析表明,在合理的条件下,双峰查询对于FR测试是最佳的,并且三阶段框架控制I误差。对合成,基准和应用程序特定数据集进行的广泛实验表明,三阶段框架在控制I错误的统一查询和确定的基于标签上的统一查询和确定性的查询中的II型误差减少。
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在本文中,我们建议将面向任务导向的对话系统作为纯粹的自然语言生成任务,以便充分利用像GPT-2这样的大规模预训练模型,并简化了复杂的光学化预备。然而,直接应用这种方法严重遭受了通过删除了替代令牌而导致的对话实体不一致,以及在微调期间灾害模型的灾难性遗忘问题,导致表现不令人满意。为了缓解这些问题,我们设计了一种新颖的GPT-Adapter-CopyNet网络,它将轻量级适配器和CopyNet模块包含到GPT-2中,以实现转移学习和对话实体生成的更好性能。在DSTC8轨道1基准和多种数据集上进行的实验结果表明,我们的建议方法显着优于基线模型,在自动和人类评估中具有显着性能。
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Learning the underlying distribution of molecular graphs and generating high-fidelity samples is a fundamental research problem in drug discovery and material science. However, accurately modeling distribution and rapidly generating novel molecular graphs remain crucial and challenging goals. To accomplish these goals, we propose a novel Conditional Diffusion model based on discrete Graph Structures (CDGS) for molecular graph generation. Specifically, we construct a forward graph diffusion process on both graph structures and inherent features through stochastic differential equations (SDE) and derive discrete graph structures as the condition for reverse generative processes. We present a specialized hybrid graph noise prediction model that extracts the global context and the local node-edge dependency from intermediate graph states. We further utilize ordinary differential equation (ODE) solvers for efficient graph sampling, based on the semi-linear structure of the probability flow ODE. Experiments on diverse datasets validate the effectiveness of our framework. Particularly, the proposed method still generates high-quality molecular graphs in a limited number of steps.
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High-utility sequential pattern mining (HUSPM) has emerged as an important topic due to its wide application and considerable popularity. However, due to the combinatorial explosion of the search space when the HUSPM problem encounters a low utility threshold or large-scale data, it may be time-consuming and memory-costly to address the HUSPM problem. Several algorithms have been proposed for addressing this problem, but they still cost a lot in terms of running time and memory usage. In this paper, to further solve this problem efficiently, we design a compact structure called sequence projection (seqPro) and propose an efficient algorithm, namely discovering high-utility sequential patterns with the seqPro structure (HUSP-SP). HUSP-SP utilizes the compact seq-array to store the necessary information in a sequence database. The seqPro structure is designed to efficiently calculate candidate patterns' utilities and upper bound values. Furthermore, a new upper bound on utility, namely tighter reduced sequence utility (TRSU) and two pruning strategies in search space, are utilized to improve the mining performance of HUSP-SP. Experimental results on both synthetic and real-life datasets show that HUSP-SP can significantly outperform the state-of-the-art algorithms in terms of running time, memory usage, search space pruning efficiency, and scalability.
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